I've spent the last year selling AI services to non-technical business owners and teaching 300+ people inside AI Operator Academy to do the same. The pattern that keeps working is simple: you don't sell AI. You sell one specific outcome, and something free does the work underneath.

In this article:

• The four-part stack every one of these offers runs on
• Five repos, and for each one: what it does, what you sell, who buys it, an honest price, and where the leads come from
• The exact tool I use to pull verified buyer contacts straight from Claude
• The math behind the first $2K/month
• The proof-first cold email that earns the pitch
• Three ways this model breaks
• The first 10 actions I'd take starting tonight

Almost nobody is doing this yet, because the people who can run these repos don't sell, and the people who sell don't know the repos exist.

Before we start, I bundled the repo links, the prompts that build the offer for you, and the cold email templates into a free resource that lives inside AI Operator Hub.

You can access the hub here (free).

I also recorded an entire video breaking down these 5 repos in detail that you can watch here.

Let's get into it.

The positioning shift: nobody buys a repo

Every offer in this article runs on the same four layers. I call it the Repo-to-Retainer Stack:

1. Repo. A free open-source project that does the heavy lifting.
2. Runner. Claude Code installs it, configures it, and runs it. You give plain-English instructions.
3. Result. The one outcome the business owner actually pays for. Not "AI." Not "a chatbot." A rival's price drop in their inbox within minutes.
4. Reach. A people-search tool called treg that plugs into Claude and finds the exact buyer for that result.

Get all four right and you have a productized service you can sell over and over.

The layer most people skip is Result. They lead with the tool, the owner hears "open-source scraping framework," and tunes out.

Lead with the outcome. Every time.

The five repos

Every price below is what I'd charge today. Treat them as targets, not proven pricing on every offer.

1. Scrapling: the weekly competitor price and stock report

What it does. Scrapling is a free scraping framework that gets past bot walls and keeps working when a site changes its layout. Most scrapers break the day a competitor redesigns a product page. This one doesn't.

What you sell. A weekly competitor price and stock report. You scrape five of the client's rivals into a Google Sheet and alert them every time a price drops or a product sells out.

Who buys. Shopify and DTC brand owners with 50 to 500 SKUs, doing $1M to $10M a year, with 3 to 5 named competitors.

Price. $500 setup, $300 a month. Your cost is roughly $40 a month for proxies and a small server (an estimate, not an audited number).

Where the leads come from. treg finds companies running Shopify with 5 to 50 US employees, filtered to founder, owner, or e-commerce manager. Send them a screenshot of one rival's actual price drop, captured with Scrapling, before you ask for anything.

Ten clients: about $5,000 in setup fees and $3,000 a month recurring, for roughly 10 hours a month of upkeep.

2. Dify: the private handbook bot

What it does. Dify is an open-source platform for building production-grade AI agents and chatbots. 156,000 GitHub stars, so it isn't going anywhere.

What you sell. A private chatbot trained on a company's SOPs, employee handbook, and price sheets. New hires ask it questions instead of interrupting the owner. You refresh it monthly as documents change, and that refresh is the retainer.

Who buys. Service businesses with 20 to 100 employees and real turnover: home services, property management, insurance agencies, staffing firms.

Why Dify instead of ChatGPT. These owners do not want internal documents sitting inside OpenAI. Dify Cloud is $59 a month, one instance per client, billed straight through. Your out-of-pocket cost is close to zero.

Price. $1,500 setup, $250 a month.

Where the leads come from. treg scrapes job postings for companies in those industries with 3 or more open roles. If they're hiring three people right now, they have the onboarding pain right now. Target owner, COO, and ops titles. The qualifying question: "How often do people ask you things that are already written in your SOPs?"

Most owners answer that one before you finish asking it.

Ten clients: $15,000 in setup fees and $2,500 a month recurring.

3. OpenSEO: the $499 audit and the $199 monthly scorecard

This is the one I'd start with.

What it does. OpenSEO is a newer repo, near 19,000 stars, that does most of what a $250 a month Semrush subscription does: keyword rankings, backlinks, site audits, Google Business Profile audits, and visibility inside AI search engines. Hosting runs about $10 a month.

What you sell. A $499 SEO audit as the front-end offer, then a $199 a month scorecard: a two-page report with 30 keyword rankings, actual Google clicks, top three competitors, a Google Business Profile audit, and three recommended fixes.

Inside AI Operator Academy we teach the audit as the front door to everything. Our members sell them at $1,000. The audit is how you get paid to figure out what to sell next.

Who buys. Local service businesses already paying an agency for SEO or ads but getting weak reporting: dentists, law firms, HVAC companies, med spas. 5 to 30 staff, metro areas.

The outcome they pay for. They know whether they're winning or losing local search, and exactly what to fix, for less than a Semrush seat.

Where the leads come from. treg pulls the dentists, firms, and HVAC companies. You run each domain through OpenSEO before you send a single email and put their three worst findings in the first paragraph. Proof first. Then you've earned the pitch.

Ten clients on the scorecard: $1,990 a month recurring for about 10 hours of monthly work, plus $4,990 in audit fees on the way in.

4. OpenShorts: 30 to 40 shorts a month from content they already record

What it does. OpenShorts is the open-source version of Opus Clip, around 5,000 stars. It picks the strongest moments from a long video, cuts vertical clips, adds captions, and costs pennies per run.

What you sell. For every long-form episode a client already records, 8 to 10 captioned clips, curated by you, hooks by you, scheduled to TikTok, Reels, and Shorts.

Who buys. Experts already recording long-form video with no clips: realtors, financial advisers, lawyers, coaches, podcast hosts with 20+ episodes and zero shorts.

The obvious objection. "Wouldn't they use Opus Clip themselves?" It's $29 a month. If they wanted to learn it, they already would have. You're selling curation and posting, not software.

Price. $400 a month for four episodes, roughly 3 hours of your time per client per month.

Where the leads come from. treg finds people posting jobs for a video or short-form editor. Podcast charts find hosts with a deep catalog and no clips. Target host, founder, or CEO. Send three free clips from their latest episode as proof.

5. Presenton: same-day branded proposals

What it does. Presenton is an open-source Gamma alternative with an API. It turns a form or document into an editable PowerPoint locked to a brand template. It hit number one trending on GitHub for a day and sits above 10,000 stars.

What you sell. The client's brand deck as a template plus a form. A rep fills in a few fields and a branded proposal pops out. Or wire the sales call recording into Presenton and the proposal drafts itself.

Who buys. Sales teams sending 20 or more custom proposals a month: contractors, agencies, staffing companies, events, commercial real estate.

The outcome they pay for. Proposals go out the same day instead of waiting two or three days on marketing, every deck on brand, no designer in the loop. Faster quotes close faster.

Price. $2,500 setup, $200 a month for hosting and template fixes. Gamma costs $400 a month for 10 seats, so you're under half the price and directly tied to revenue.

Where the leads come from. treg finds companies posting for proposal writers or sales coordinators, 20 to 200 staff, VP of Sales or sales ops titles. Ask for their last proposal and rebuild it live on the call.

Four clients: $10,000 in setup fees and $800 a month recurring.

Finding the buyer with treg

I'm not affiliated with treg. It collapsed four subscriptions into one prompt.

treg is an MCP server you connect to Claude. Its people search runs across 60 providers (Apollo, Hunter, People Data Labs, and the rest) and returns verified emails and phone numbers. You pay per record, a few cents per verified email, with no subscription to any of the underlying tools. Connect it once and ask Claude Code for a lead list in plain English.

Here's the prompt I'd run for the OpenSEO offer:

TREG LEAD PULL PROMPT
Using treg, find dental practices, law firms, and HVAC companies in
[METRO AREA] with 5 to 30 employees. For each company, find the owner,
managing partner, or practice manager and return a verified work email.
Show me the cost per record before you spend anything. Return the
results as a table with company name, website domain, contact name,
title, and email. Target 100 companies.

For the Dify offer, swap the industries and add: "Only include companies with 3 or more open job postings right now."

The math behind the first $2K/month

Start with OpenSEO. The front-end is the cheapest thing to sell and the repo does the audit for you.

Funnel math, using conservative cold email numbers I'd treat as targets, not proven conversion:

• 500 proof-first emails sent (each with three real findings from OpenSEO)
• 5% reply rate is 25 conversations
• 40% book a call is 10 calls
• 50% close on a $499 audit is 5 audits, or $2,495
• 60% of audit buyers move to the $199 scorecard is 3 retainers, or $597 a month

Run that motion four times: 20 audits ($9,980 one-time) and 12 scorecards ($2,388 a month) on a tool that costs $10 a month to host.

What protects the margin is the time cap. Each scorecard is a two-page report from a tool that already ran the numbers. If a client starts asking "can you also fix it," that's a separate offer with a separate price.

The scorecard is reporting. Fixing is a different invoice.

Getting the first client

The rule across all five: prove it before you pitch it.

Scrapling sends the rival's price drop. Dify asks the SOP question. OpenSEO sends the three worst findings. OpenShorts sends three finished clips. Presenton rebuilds the last proposal live.

You never ask a stranger to imagine value. You show them their own data.

Here's the OpenSEO version of the email:

COLD EMAIL TEMPLATE (OpenSEO)
Subject: 3 things I found on [DOMAIN]

[First name],

I ran [DOMAIN] through a local search audit this morning. Three things
stood out:

1. [Finding with a number, e.g. "You rank #14 for 'emergency dentist
   [city]' and the #1 result is a competitor 2 miles away"]
2. [Finding with a number]
3. [Finding with a number]

I put together the full report as a two-page scorecard. If you want the
rest of it, reply and I'll send it over. No pitch attached unless you
ask for one.

[Your name]

Send 10 of these tomorrow. The rest go out over the week.

Three ways this model breaks

1. You sell the tool. Say "open-source" or "GitHub" to a dentist and you've lost them. The boundary: the client never hears the repo name until they ask. They buy the scorecard, the handbook bot, the weekly report.

2. You underprice the setup. Dify at $500 setup instead of $1,500 means the same document ingestion work for a third of the money, and the client values it a third as much. The setup fee is where the labor is. Charge for it.

3. You pick three repos at once. Every repo here is a business by itself. Three repos means three cold email motions, three delivery processes, and zero case studies. Pick one. Get to five clients. Then decide whether to add a second.

Three offers, zero clients, is the most common outcome I see.

The first 10 actions I'd take tonight

1. Pick one repo. If you have no strong pull, pick OpenSEO.
2. Open Claude Code and ask it to install and configure the repo on a small server.
3. Connect treg to Claude as an MCP connector.
4. Run the treg lead pull prompt above for one metro area. Target 100 companies.
5. Run 20 of those domains through OpenSEO and save the three worst findings for each.
6. Build the audit as a two-page template so every client gets the same format.
7. Send 10 proof-first emails tomorrow morning using the template above.
8. Set the prices before the first call: $499 audit, $199 a month scorecard. Don't negotiate on the first client.
9. Book every reply into a 20-minute call and walk through their findings on screen.
10. After the first audit is delivered, ask for the retainer on the same call.
11. At five clients, document delivery so the monthly work stays under 10 hours.
12. At ten clients, add a second repo or raise the price.

Final thoughts

None of this requires code. It requires one outcome, a free repo and Claude Code to produce it, and the person who already feels the pain.

Pick one repo tonight. Pull 100 leads with treg. Send 10 emails tomorrow. That's under 30 minutes of actual work, and more than most people reading this will do.

I bundled the repo links, the prompts that build the offer for you, and the cold email templates into a free resource that lives inside AI Operator Hub.

You can access the hub here (free).

Most people are trying to build an agency, a SaaS, or an app but the single biggest opportunity to make money with AI is hiding in plain sight.

My friend @Markfersh raised $8 million and spent seven years building a B2B SaaS to a $500K annual run rate.

Six months of nights-and-weekends AI consulting passed that number.

He shut the SaaS down. And one of the offers driving the consulting side is the least technical thing on his menu: in-person AI workshops. The first one he ran was $495 a head, four hours, 15 people, marketed with an Eventbrite listing and a handful of LinkedIn DMs.

In this article:

• Why Mark treats the workshop as a lead magnet he happens to get paid for, not the product
• The four-rung offer ladder, with his real prices at every rung
• The 20% pricing rule he uses to set workshop fees
• The math behind why workshops cap out and builds don't
• How to run your first one with no audience and no technical background
• The three ways this model breaks

Almost nobody is doing this right now. Most operators are fighting over Zoom calls and DMs while a 45-plus business owner two miles from you is embarrassed to ask what a "skill" is and would pay to find out in a room.

Before we dive in, I bundled the complete Workshop Wedge systeminto one free doc: the 4-hour workshop agenda, the pricing worksheet, the Eventbrite listing template, the opportunity log, and the post-workshop upsell script.

Grab it here (free).

Let's get into it.

The positioning shift: the workshop is not the product

Here's the line from Mark that reframed the whole model for me:

"I don't think the in-person workshop is the full value. It's getting your foot in the door. It's a lead magnet that you happen to make some money off of."

Most people who hear "AI workshop" picture the workshop as the business. Book them, deliver them, repeat. You can make a living that way. Mark's estimate is that you cap out at a couple hundred thousand a year, because workshops eat hours. Planning, delivery, and by his own account, he's wiped for days afterward.

So he uses the room differently. A workshop gets him in front of a whole team for 4-8 hours, with permission to ask about every process they hate. By the end, he has a list of expensive problems, a warm relationship, and the right to propose the build.

I'm calling this the Workshop Wedge: a paid, in-person session that opens the account, surfaces the real problem, and sets up a larger implementation deal. Four rungs.

Rung 1: the free artifact

Mark gives away as much as he can up front. A Claude skill, a bundle of assets, a short audit. Something the prospect can use today.

The point is trust before money. If you're targeting a company, this is what gets the workshop conversation started. If you're running a public workshop, this is your content on LinkedIn or X that makes someone click the Eventbrite link.

Sometimes there's a half step here: a paid audit. If he's going into a team, he'll diagnose their problems first and then tailor the workshop to what he found. That audit is also where the assessment in "workshop plus assessment" comes from.

Rung 2: the paid workshop

Two flavors, two price points.

Public, per head. His first was $495 a person, four hours, 15 attendees. Rough math: around $7,400 for an afternoon, before venue costs. The audience skewed 45 and older, people who were afraid of being left behind at work and didn't want to ask in front of their team.

Private, per company. Half a day to two days, $15K to $25K. Formats he's run: a team AI essentials session, a hackathon where he teaches the basics and then the group spends four or five hours building with Claude Code and Cowork together, and a "everyone leaves with an agent built" session.

I asked him directly about pricing because I'm in the middle of this myself. Two days before we recorded, my realtor here in Charlotte asked me to run a paid workshop for her brokerage. Every realtor is an independent contractor, so nobody's boss foots the bill. My gut said $500 a head.

Mark's answer: right in range, and don't go higher than you have to.

"You can make mistakes at $500. You probably can't make mistakes at $2,000 a head."

His logic is that a $500 attendee leaves saying "that was a bargain" and refers three friends. A $2,000 attendee scrutinizes every minute. For a first workshop, you want the referral, not the margin.

Here's the pricing rule he uses for the private ones:

WORKSHOP PRICING WORKSHEET

1. Name the workflow(s) the workshop will change.
2. Estimate the annual value if they adopt what you teach:
   - Low case (25% adoption):   $______
   - Mid case (50% adoption):   $______
   - Best case (full adoption): $______
3. Price the workshop at roughly 20% of the mid case.
4. Sanity check against the ceiling: a CEO of a 150-person company
   can pay multiples of what a 5-person shop can, for the same content.
5. Public per-head events: start at $500. Raise only after you have
   a referral loop, not before.

That's Mark's rule of thumb, not a formula he's audited. But it explains why the same four hours are $500 a head in one room and $25K in another.

Here's the listing template. Full version in the bundle.

PUBLIC WORKSHOP LISTING TEMPLATE

Title: AI for [Industry/Persona]: Build Your First [Agent/Skill] in 4 Hours

Who it's for: [Persona] who use ChatGPT sometimes and know they're
barely scratching the surface.

What you'll leave with:
- One working [agent/skill/automation] built during the session
- The exact prompts and setup we used, in a shared doc
- A list of the 3 workflows in your business to automate next

Format: In person. 4 hours. Laptop required. Max [15] seats.
Price: $[495]. Paid up front.
Host: [Your name], [one-line credibility: what you've built or sold].

Rung 3: the custom build

This is where Mark says the money and the fun are.

$25K to $50K, three to six weeks. And he's clear that's the low end. He's been pulled toward mid-market and enterprise, with several six-figure proposals out right now. Those are bundles: assessment, workshop, training, build, and sometimes a short retainer, on one contract.

What he builds: what he calls an AI operating system. A context layer that connects three to five of their existing tools and their internal knowledge base into one place. His last startup did this for sales teams. Now he builds custom versions.

The receipt he gave: a client had one brilliant person spending seven hours a day on internal staffing, bouncing across eight platforms. Work got batched, spilled into weekends, quality slipped. Mark's build added routing, scoring, and pre-qualification logic behind a few buttons. Staffing dropped to about 15 minutes, and 25 to 30 people can run it now instead of one.

Seven hours to 15 minutes is the sales pitch for rung three. The workshop is where he found it.

How the workshop finds the build

Mark writes something down every time this happens: in the middle of a session, someone on the team nudges the person next to them and says "we could do this for our quoting process." Three other people say "I was thinking the same thing."

That's the first build. I'm calling it the elbow test. If a process makes people elbow each other, it's expensive enough to pay for.

WORKSHOP OPPORTUNITY LOG

During every workshop, capture:

| Time | Who said it | The process they named | Who else reacted | Hours/week it eats (ask) |
|------|-------------|------------------------|------------------|--------------------------|
|      |             |                        |                  |                          |

After the workshop, rank by (people who reacted) x (hours/week).
The top row is your build proposal. Send it within 48 hours while
the room is still warm.

Rung 4: the next system and the retainer

After the first build, it branches.

Sometimes it's another workshop for a different team: engineering, customer success, a security and governance session for leadership. Sometimes it's the next build. Sometimes it's a retainer, and Mark's experience is roughly 50/50 between clients who want him for another three months and clients who want one big package, paid half up front and half on completion.

He doesn't chase maintenance fees. His view: if he's collecting maintenance, he failed at training the trainer. He'd rather parachute in, ship, hand off, and leave. So the retainer, when it exists, is for building the next thing, not babysitting the last one.

The math behind the ladder

Here's why the ladder matters more than any single rung.

Public workshop: $500 x 15 people = $7,500 for a day. Do one a month and it's $90K a year, and that's your ceiling if every seat fills.

Private workshop: $15K to $25K for one to two days. Ten of those is $150K to $250K. Still a ceiling, and you're cooked for a week after each one.

Build: $25K to $50K over three to six weeks. One build a month at the low end is $300K a year, and each one came from a workshop you already got paid for.

So the sequence, using Mark's low-end numbers: $7,500 public workshop, which produces one private workshop at $15K, which produces one build at $25K. That's $47,500 from one Eventbrite listing. The public workshop is 16% of that revenue.

He turns down workshops that don't have upsell potential, and he's said he could run far more if he weren't doing the bigger builds. But the shape holds: the workshop is the smallest check in the chain and the one that opens up the rest.

Getting your first workshop

Mark's advice for someone who's never run one: don't overthink it.

Three steps.

Pick a niche you already know. Mark's biggest regret is being too broad. Every one of his seven offers could be its own business, and now he has to choose. If you spent ten years in real estate, dental, roofing, or e-commerce, run "AI for [that]."

I have an Amazon wholesale background. I could charge $1,000 to $2,000 a head to Amazon sellers and deliver more than I could to realtors, because I know what their week looks like.

Run one public, paid, in person. Eventbrite listing, Stripe up front, $500 a head. DM everyone you know in the area and ask who else should be there. If two people show up, you made money and they got the best workshop of their life. Worst case, you're out a couple hundred bucks on a room.

Don't teach everything. Mark's point that I keep coming back to: we live in a bubble. Most people's entire AI experience is typing into ChatGPT. They don't need 27 agents. They need Cowork or ChatGPT set up, two or three skills built, a couple of scheduled tasks, and a clear answer to "what's eating hours of your week that you hate?" That's the 80/20.

4-HOUR WORKSHOP AGENDA

0:00  Why this matters for [persona]: 3 real examples from your niche
0:20  Setup: everyone gets Claude/Cowork or ChatGPT working on their laptop
0:50  Build 1 (together): a skill for the task everyone in the room hates
1:40  Break
1:55  Build 2 (solo, you float): each person picks their own workflow
2:45  Scheduled tasks: set up one recurring job per person
3:15  Opportunity round: "what else could this do for you?" (log everything)
3:45  Close: what's next, how to get help, the offer for teams

Then the upsell. Mark guarantees that if you do a great job, people will come up afterward and ask if you can do this for their company. Say yes.

UPSELL SCRIPT (after the workshop)

When someone asks "can you do this for my team / my company?":

"Yes. Here's how I'd run it: half a day with your team, we build
around the workflow you just described, and I leave you a list of
what to automate next. That's $[15K].

If I haven't built exactly that before, I'll say so. And if you'll
let me use it as a case study, I'll do it for $[10K]."

Be upfront about what you haven't done. Trade a discount for a case study. That's how you go from one public workshop to your first private one without an audience.

Who this is for, and who it isn't

This is for you if you can build, or you're confident you can learn to build what you'd teach. You have a niche or an industry you know cold. You're comfortable in a room. Bonus points if you have an events or events-marketing background, because filling seats is half the job.

This isn't for you if you want fully remote, zero-travel work. It's not for you if you'd rather sell a $5K workshop and disappear; the model only pays off if you deliver well enough to earn the upsell. And it's not for you if you can't say "I've never done that, but I'm confident I can" with a straight face.

Three ways this model breaks

1. Selling workshops to teams that don't need you. Mark turned down a company that was already good at AI. They negotiated hard on price and he could see no upsell behind the workshop. If a team has real internal builders, you're a one-time line item. Qualify for "one person is the AI expert and everyone else is behind."

2. Overdelivering on complexity. Teaching a room of 50-year-old business owners about agent loops and orchestration is how you lose them in the first hour. The value is in simple, real jobs to be done. Tools are almost irrelevant. Workflows are the whole thing.

3. Treating the workshop as the finish line. If you walk out with a check and no opportunity log, you ran a class, not a wedge. The 48-hour proposal after the session is where the money is. Skip it and you've capped yourself at a couple hundred grand a year with the hardest possible schedule.

The first 10 actions I would take

1. Pick the industry or persona you know best. One. Not "small business."
2. Write down the three workflows in that niche that waste the most hours per week.
3. Build a free artifact that solves one of them (a Claude skill, a prompt pack, a checklist). Post it.
4. Create an Eventbrite listing using the template above. $500 a head, four hours, 15 seats max.
5. Stripe or Eventbrite payments up front. No pay-at-the-door.
6. DM 20 people in your area and ask who else should be there.
7. Run the workshop off the agenda above. Two builds, one scheduled task, one opportunity round.
8. Fill the opportunity log during the session. Every elbow moment gets a row.
9. Within 48 hours, send the top opportunity as a private workshop or build proposal, priced with the 20% rule.
10. Take the case-study discount on the first private one if it gets you in.
11. After the private workshop, propose the build. $25K to $50K, three to six weeks.
12. Turn down the next workshop that has no upsell behind it.

Final thoughts

Mark raised $8 million to build a product. The thing that out-earned it was standing in a room showing people how to use Claude.

His numbers are his, from six months of part-time experiments, and he'd be the first to say he's still figuring out which rung to double down on. But the shape is what matters: the free thing earns the room, the room earns the problem, the problem earns the build.

If you want the complete Workshop Wedge system, I bundled the agenda, the pricing worksheet, the listing template, the opportunity log, and the upsell script into one copy-paste doc.

Grab it (free) here.

You can find Mark at masteringai.io or on X @markfersh. The full interview with Mark is here.

His name is Dino Watt and he recently closed a $30,000 AI consulting retainer. $5,000 a month, six-month contract. That's more than double what my highest-paying concierge client pays me.

I've spent the last year selling AI assessments and concierge retainers to small businesses. Dino came on my podcast this week and screen-shared his entire playbook. This article is that playbook, in step-by-step format to help you land your first retainer.

In this article:

• The positioning shift that makes retainers close themselves
• The three-rung ladder: paid workshop → paid assessment → retainer
• The exact pricing at every rung, with Dino's real numbers
• The 7-minute deliverable that does the selling for you
• The math behind $65,734 from one $97 workshop
• The two-path close (word for word)
• The 10 actions I'd take starting from zero

Most people trying to land their first AI retainer lead with the retainer. That's exactly backwards, and it's why they're stuck at zero.

Before we dive in, I combined all the best practices, prompts, and resources from this article into one simple downloadable playbook.

You can grab it (free) here.

Let's get into it.

The positioning shift: never sell the retainer first

Dino is 53. Dyslexic, ADHD, barely graduated high school. His words on coding: put a gun to his head and he's a dead man. No marketing background, no tech background. His day job for the last 15 years has been culture and communication consulting for orthodontic practices.

So how does a guy like that out-close me on retainers?

He never sells the retainer.

He sells a filter. Dino's pitch to his audience is simple: you're being bombarded with AI tools and you're terrified of buying the wrong thing. I'll give you a filter so you can make better decisions. That filter is the Three Outcomes. Every tool and workflow gets judged on whether it improves Effectiveness (makes you money), Efficiency (saves you time), or Quality (improves your product and customer experience). That's the framework we teach inside AOA, and it's the spine of everything Dino delivers.

The retainer shows up at the very end, almost as an afterthought. By then the client has already paid him twice and trusts him completely.

I call this the Paid-Every-Step Ladder. Three rungs, and you collect money on every single one:

1. Paid workshop ($97): teach the filter
2. Paid assessment ($497-$997): apply the filter to their business
3. Retainer ($5,000/month): implement what the assessment found

No free discovery calls. No "pick your brain" coffees. No proposals rotting in someone's inbox.

If they won't pay $97 to learn from you, they were never paying $5,000 a month.

Rung 1: The $97 workshop

Dino built a three-hour virtual workshop on AI basics for orthodontists. One email to his list of roughly 5,000. No ads.

21 people in the room. 15 paid (he comped 6 existing clients). Some caught a $49 early-bird rate, the rest paid $97.

Three hours of teaching the Three Outcomes filter, applied to their industry. That's it. He's a trainer by trade, so this rung plays to his strength. But the content itself came from what we teach, customized to his niche.

Two things make this rung work:

The niche can afford you. Dino quotes Alex Hormozi on this: solve rich people problems. Orthodontists run practices doing seven figures. A $997 assessment isn't a purchasing decision for them, it's a rounding error. Pick a niche where your eventual retainer price is normal, not shocking.

The upsell starts before the workshop. The moment someone registers, the confirmation page offers the assessment: normally $997, but $497 if you grab it right now. Four people bought before the workshop even happened. He was up over $2,000 before delivering a single minute of content.

Rung 2: The assessment (and the deliverable that sells the retainer)

At the end of the workshop, Dino pitches the assessment again: $997, or $797 if you buy within 24 hours. Three people closed on the spot. Two more came in days later at full price.

That's a 44% close rate from the workshop room.

The assessment itself follows the process we teach: a deep discovery call, transcript into Claude, and out comes a scored report mapping AI solutions to their specific pain points against the Three Outcomes. You don't need to know how to build anything to deliver this. You need to ask good questions and let the AI do the analysis.

But Dino added one thing that I'm now stealing for my own business.

The 7-minute explainer video. He runs the finished assessment through Notebook LM (now Gemini's video tool) and generates a short animated video that walks the client through their own report. Their pain points, in their words, played back to them with the plan to fix each one.

Here's the prompt structure he adds to his assessment build:

EXPLAINER VIDEO PROMPT

Take this completed AI assessment report and write a prompt for Notebook LM that will generate a ~7 minute explainer video.

The video must:

- Address the client by name in the opening line

- Lead with the single biggest outcome (e.g. "designed to give you 5+ doctor-hours back every week")

- Replay the client's own stated pain points in their own words

- Walk through the plan as: "First we'll do X. Second, Y. Third, Z."

- End with the projected annual savings figure from the report

Why this matters for landing the retainer: your buyer is almost never the only decision-maker. There's a spouse, a partner, a business manager, a financial coach. One of Dino's prospects promised her financial coach she wouldn't buy anything without running it past him first. Instead of hoping she could re-explain a 20-page report, she forwards a 7-minute video.

The video does your selling in rooms you'll never be in.

He also has Claude prep his sales call from the same assessment:

SALES PREP PROMPT

From this assessment report and discovery call transcript, give me:

1. The client's 3 biggest pain points, ranked by emotional weight

2. Their stated financial goals and any hard numbers attached

3. Which sections of the report to slow down on during the walkthrough call, and which to move through quickly

4. A suggested call flow that connects our plan to their #1 goal

Real example: one client wants to sell her practice in five years for at least $1.2 million. Claude flagged that her EBITDA needs to hit roughly $500-600K to get there. So Dino's entire walkthrough connected every recommendation to that exit number. She's expected to sign this week. That's a target, not a closed deal yet, and I'm labeling it as one.

Rung 3: The two-path close

After the assessment walkthrough, the retainer pitch is one sentence. Here's the structure, pulled straight from how Dino (and I) run it:

TWO-PATH CLOSE SCRIPT

"I promised you a blueprint, and this is it. You now have everything you need to go save $[X] a year.

You've got two options. You can take this blueprint and implement it yourself, or hand it to any AI person you trust. It's yours.

Or you can hire us to do it for you. I've got the people and the process in place. You sit back and collect the results.

Which one sounds better to you?"

No pressure. No countdown timers. The genuine willingness to let them walk is what closes the deal, because it proves the assessment wasn't a trojan horse.

And think about who's hearing this. An orthodontist with patients, staff, and a practice to run. Implementing the blueprint themselves means 200 hours they don't have. Paying $30,000 to capture $250,000 in projected annual savings is the easiest math they'll do all year. (That $250K is the assessment's estimate, not audited savings, so say so on your calls too. Honest numbers close better than inflated ones.)

One sales psychology note from Dino worth bolding: he never says "$30,000." It's $5,000 a month. The only time the full number comes up is in the ROI comparison: pay 30 to get 250 back.

The math behind $65,734

From one $97 workshop, before marketing beyond a single newsletter email:

• Workshop revenue: ~$1,500 (15 paid seats, mix of $49 early bird and $97)
• Assessment revenue: ~$4,200 (4 pre-workshop at $497, 3 at $797, 2 at $997)
• Cash collected before delivering a single assessment: $5,734
• First retainer closed: $30,000 ($5,000/month, 6 months)
• Second retainer: expected this week (target, not proven)
• Behind that: 3 more assessment clients in the pipeline

Total value from one workshop: $65,734. The closed, verifiable portion is the $5,734 collected plus the $30K contract signed. The rest is pipeline, and Dino presents it that way too.

Notice the structure of the funnel math: the workshop and assessments aren't really the money. They're paid trust-building. The retainer is 84% of the total value. But you can't skip to it. The retainer only closes at that price because the client has bought from you twice, watched you deliver twice, and seen their own numbers in your report.

Getting your first client: who this works for

This model works if you have at least one of these:

• A niche you already serve or understand (Dino: 15 years in orthodontics)
• An email list, even a small one (his took 10 years to build to 5,000, but 21 registrants means you'd get similar results from a list of a few hundred in a tight niche)
• A willingness to run a warm-network version: 5-10 personal invites to business owners you know, same ladder, smaller room

Who this isn't for: if you want to skip the assessment and go straight to building agents for strangers, this playbook won't save you. The ladder is the product. And if your niche can't afford $997 for an assessment, fix the niche before you fix the offer.

One more thing Dino said that should remove your last excuse: not knowing the industry deeply can actually be an advantage. His discovery calls run 90 minutes because his expertise pulls him into rabbit holes. The core assessment questions, the basic ones we hand you, produce the report either way.

You don't need to be the expert. You need to be the person holding the filter.

The first 10 actions I'd take from zero

1. Pick one niche where owners clear $500K+ and already pay for consulting.
2. Customize the assessment question bank to that niche (feed Claude what you know about their world and have it adapt the questions).
3. Build a 3-hour workshop teaching the Three Outcomes filter applied to that niche. Charge $97.
4. Set up the registration flow with the assessment upsell on the confirmation page: $997 anchored, $497 pre-workshop price.
5. Fill the room with one email to your list, or 20 personal invitations if you don't have a list.
6. Deliver the workshop. Close the assessment at the end with a 24-hour $200 discount.
7. Do 1-2 free assessments for existing contacts first to collect testimonials before the paid ones land.
8. Run every paid assessment through the discovery-call → Claude → scored report process.
9. Generate the 7-minute explainer video and send it as the teaser before the walkthrough call.
10. On the walkthrough call, run the two-path close. Quote monthly, not total. Anchor against their projected savings.

Then do it again next month. Dino's second workshop already has 10 paid seats, zero comps.

Final thoughts

The reason most people never land their first AI retainer is that they ask for marriage on the first date. Dino got paid to build trust three separate times before the retainer conversation ever happened, and by then it wasn't a pitch. It was the obvious next step.

He's 53, can't code, and plans to ride this until the wheels fall off.

Your excuses are gone.

Watch the full breakdown with Dino on the Build With AI podcast.

If you want the assessment templates and playbook resources from this article in one place, grab the free bundle here.

And if you want the complete business in a box (the trainings, the customized assessment templates, and 242+ operators selling AI services right now), join us inside AI Operator Academy.

It fielded the lead, drafted 2 replies, sent a Stripe link, and made sure the lead paid while I was working on something else entirely.

I've spent the last year selling AI services to small businesses, and in this article I'm handing you the exact bot that did it: the full system prompt, the three skills it runs, and the three routines that keep it working around the clock. Every piece is copy-paste.

Table of Contents

I: Why leads die in your inbox

II: What Grok Bot actually is (60-second version)

III: The Role, Skills, Routines stack

IV: The three skills

V: The three routines

VI: The $999 close, play by play

VII: Guardrails, and who should build this

Everyone is posting Grok Bot demos right now. Almost nobody has published a working revenue system you can copy.

That's what this is.

Before we get into it, I bundled the complete Revenue Closer setup, including the full system prompt, all three skills, and the routine schedules, into one copy-paste doc.

You can grab it (free) here.

Let's get into it.

I: Why leads die in your inbox

Here's the uncomfortable math of running a service business: the lead you answer in 10 minutes closes, and the lead you answer in 2 days ghosts.

I know this because I sell AI assessments and retainers, and my pipeline lives in Gmail. Warm yeses come in while I'm on client calls, recording podcasts, or at the gym. By the time I see them, the buying energy has cooled.

I tried the standard fixes. A follow-up label system I stopped maintaining after a week. Todoist reminders that told me a lead existed but did nothing about it.

The problem was never knowing I should follow up. The problem was that following up required me.

So when Grok Bot launched, I didn't build a research assistant or a content bot first. I built the thing that touches money: a Revenue Closer whose only job is turning warm yeses and live pipeline into booked calls and paid deals.

II: What Grok Bot actually is (60-second version)

Quick context if you missed the launch. On August 11th, xAI released Grok Bot in early beta: always-on AI agents that each get their own computer in the cloud. Per xAI's announcement, they sign into the tools you already use, work across apps and inboxes, and only come back to you when something needs approval.

A normal AI chat stops when you close the window. A Grok Bot keeps running on its own machine, on a schedule, whether your laptop is open or not.

Two practical notes before you copy my setup:

Access. Grok Bot isn't sold standalone. It comes with SuperGrok, SuperGrok Plus, and SuperGrok Heavy plans, plus Cursor Pro, Pro+, Ultra, and Cursor Teams plans, with its own separate usage allowance. If you're already on one of those plans, this bot costs you nothing extra to try. If you're not, factor the subscription into your math before you build.

Connections. The Revenue Closer needs Gmail, Google Calendar, Todoist, and Google Docs connected. It works your real pipeline, so it needs your real tools.

My bot paid for its subscription multiple times over in its first day.

III: The Role, Skills, Routines stack

Here's the framework behind every bot I build now, and the reason this one worked on day one. I call it the Role, Skills, Routines stack:

• Role: one system prompt that defines what the bot owns, what it sells, and what it's forbidden to do
• Skills: reusable procedures the bot runs the same way every time
• Routines: scheduled tasks that fire those skills without you asking

Most people write a vague "you are my sales assistant" prompt and wonder why the bot freelances. The Role layer is where you prevent that. Here's mine, templatized so you can drop in your own business:

REVENUE CLOSER SYSTEM PROMPT

You are {OWNER}'s Revenue Closer. Your only job is to turn warm
yeses and live pipeline into booked calls and paid deals. You do
not do content, community ops, calendar design, or life admin.

Current offers, in order (do not skip steps, do not pitch what is
not on this list):
1. {OFFER 1 NAME} — {PRICE}
2. {OFFER 2 NAME} — {PRICE, ICP FLOOR, MIN FEE}
3. {OFFER 3 NAME} — {PRICE, TERM, WHAT IS INCLUDED}

ICP: {WHO BUYS}. Disqualify people who {DISQUALIFIERS}.

How you work:
- Own the follow-up. Draft the next email in {OWNER}'s voice.
  They approve. You do not send unless they say so.
- Lead with the next step and the ask. No status dumps.
- Track every live deal: last touch, next action, dollar value,
  risk of dying.
- Separate money-in-hand from warm pipeline from dead leads.
- Voice: short, direct, warm. No corporate closer language.
  No "just checking in."

Hard rules:
- Never send email, text, or DM unless {OWNER} explicitly says send.
- Reply to their last inbound, never {OWNER}'s last outbound.
- Name the fee only if it was never said in-thread. Never invent
  a pay link, booking link, or history.
- Stay quiet on scheduled runs when there is nothing new.

A few of those lines are scar tissue worth explaining:

"Reply to their last inbound, never your last outbound." Gmail will bounce the To: field back to you if the bot replies to your own message. I learned that one the annoying way.

"Never invent a pay link." An AI that fabricates a Stripe URL in a money thread is a liability. The bot reuses links only if they already appeared in the thread or are the current public signup URL.

"Stay quiet when there is nothing new." A bot that pings you hourly with "no updates" trains you to ignore it. Silence is a feature.

IV: The three skills

Skills are where the bot stops being a chatbot and starts being an employee. Each one is a fixed procedure with a fixed stopping point.

Note: The skill prompts below are abbreviated to keep this article from being 3,000+ words. The full skill prompts, system prompt, and 3 routine prompts can be found in the Revenue Bot Playbook (free).

SKILL 1: DRAFT MONEY FOLLOW-UP

Use when drafting a follow-up on a live money thread.
1. Read the thread and any existing draft. Capture last inbound,
   last outbound, dates, whose ball it is. Pull 3-5 sent voice
   samples from similar closes.
2. Write the next email. Lead with the next step and one ask.
   If they said yes and the fee was never named, name the real
   current fee and flag that you named it.
3. Save as a Gmail draft on the live thread, replying to their
   last inbound. Do not send.
4. Confirm the To: is the prospect and the subject threads.
   Show {OWNER} the full draft. Stop.

SKILL 2: DRAFT QUIET LEAD FOLLOW-UP

Use when a live money thread has gone quiet for 3 business days
after our last outbound. Weekends do not count toward the 3 days.
- Write a 3-5 line nudge with one ask. No "just checking in."
- Skip if: their message is the most recent one, a fresh unsent
  draft already exists, a money call with them is booked this
  week, or they already declined the paid path.
- Save as a Gmail draft. Show the nudge number (1/2/3 of 3).
- Stop after 3 unanswered nudges and flag the lead for a
  kill-or-keep decision. Never send.

SKILL 3: PREP MONEY CALL

Use before any call that can lead to revenue.
- Gather evidence from Gmail, Calendar, and Todoist. Never invent
  a price or history.
- Write a one-page brief: who they are, the deal, the one ask,
  open loops, 3-5 talking points, and what to do if they say
  yes, no, or later.
- Save it as a Google Doc. Create a private same-time calendar
  event holding the doc link, visible to {OWNER} only. Never
  attach the brief to the shared invite.

Notice the pattern across all three: every skill ends in a draft or a doc, never a send. And every skill has explicit skip conditions, because knowing when NOT to act is most of what makes an assistant trustworthy.

V: The three routines

Routines are what make this a closer instead of a tool you have to remember to use. Mine run on three schedules:

ROUTINE SCHEDULES

1. MONEY FOLLOW-UP SWEEP — hourly, 8am-6pm, daily (0 8-18 * * *)
   Scans for new inbounds, yeses with no next email, and
   time-bound asks. Runs Skill 1 on each real opportunity.
   Pings once per opportunity: who, the ask, draft location.
   If nothing new, sends nothing.

2. QUIET LEAD NUDGE — weekdays, 8am (0 8 * * 1-5)
   Scans for money threads quiet 3+ business days.
   Runs Skill 2 on each. Pings with nudge number.
   If nothing is due, sends nothing.

3. MORNING CALL BRIEFS — weekdays, 7am (0 7 * * 1-5)
   Runs Skill 3 for every money call on today's calendar.
   Skips podcasts, meetups, gym, and anything that isn't a buy.
   Pings once with the brief links. If no money calls, silence.

The hourly sweep is the one that made me $999. A warm lead landing at 2:14pm gets a drafted response by 3pm at the latest, not whenever I next open my inbox.

The division of labor matters too. The sweep owns fresh opportunities. The nudge routine owns silence. Neither touches the other's lane, so you never get duplicate drafts on the same thread.

VI: The $999 close, play by play

Here's exactly what happened in the first 24 hours.

An inbound lead hit my inbox asking about my $999 AI assessment. I was heads-down on other work and didn't see it.

The hourly sweep caught it. The bot read the thread, recognized it matched offer #1 on the ladder, and ran the Draft Money Follow-up skill. It pulled voice samples from my recent sent mail, wrote a reply in my voice with one clear ask, and pinged me: who, what they wanted, draft sitting in Gmail unsent.

I read the draft, made zero edits, and told it to send.

The prospect replied with a question. The bot drafted reply number two, this time including the Stripe link for the assessment, since the fee was already named in-thread. I approved. It sent.

He paid immediately.

Total human time on my side: maybe 4 minutes across two approvals.

Now the honest framing, because this is where most AI content lies to you:

• The bot didn't generate this lead. My content and referrals did. A Revenue Closer multiplies inbound. It doesn't create it.
• The win was speed, not persuasion. The draft was good, but the reason he paid is that he got a sharp, personal response while he was still in buying mode.

That last point is the entire thesis. You need your existing pipeline worked within the hour, every hour.

VII: Guardrails, and who should build this

Quick recap of the full system before you build:

One Role prompt that defines the offers, the voice, and the hard rules. Three Skills that always end in a draft or a doc. Three Routines that fire them on a schedule and stay silent when there's nothing to say.

Two guardrails I won't compromise on, and you shouldn't either:

The draft-only rule. The bot never sends without my explicit approval. Ever. It's tempting to remove the approval gate once the drafts get good. Don't. One hallucinated price or wrong-recipient email in a money thread costs you more trust than the gate costs you in seconds.

Access honesty. This bot reads my real inbox and calendar. If you're not comfortable being accountable for what an agent can touch, scope its access down or don't build it yet. Non-technical doesn't mean unaccountable.

And who this is actually for:

Build this if you're a service business owner with real inbound flow: leads from content, referrals, or past clients that currently sit in your inbox for hours or days.

Skip this if you have no inbound yet. A closer bot with an empty pipeline closes nothing. Go do the outreach and content work first, then come back. Also skip it if you'd need a new $200+ subscription just to access Grok Bot and you're not already getting value from those plans. The math should work before the bot does.

The first 10 actions I would take

Starting from zero today:

1. Confirm you're on a plan that includes Grok Bot, and install the desktop app.
2. Connect Gmail, Google Calendar, Todoist, and Google Docs.
3. Write your offer ladder: 2-3 offers, in order, with real prices.
4. Fill in the Revenue Closer system prompt template with your offers, ICP, and disqualifiers.
5. Add the hard rules verbatim. Especially draft-only and never-invent-a-link.
6. Create the three skills as written above.
7. Set the three routines: hourly sweep 8am-6pm, weekday 8am nudges, weekday 7am briefs.
8. Feed it 3-5 of your best sent emails as voice samples.
9. Run it for one week approving every draft before send. Edit the prompt every time a draft misses your voice.
10. Only after a clean week, let it run without daily babysitting. Keep the approval gate forever.

Final thoughts

The Revenue Closer works because it's narrow. It doesn't do content, ops, or admin. It does one job: make sure no warm dollar dies of neglect in my inbox.

Grok Bot made the always-on part easy. The Role, Skills, Routines stack made it safe. And the first $999 showed up before I'd owned the thing for a full day.

If you want the complete Revenue Closer setup as one copy-paste doc, with the full unabridged system prompt, all three skills, and the routine schedules, grab it free here.

If you want to build systems like this alongside other non-technical operators, join AI Operator Academy.

- Corey

I built my AI services business past $10K a month in under a year with a three-stage offer ladder and a list of 10 people I already knew.

This article breaks down the full model: the pricing, the $10K math, the warm-network sales process, and the three mistakes that kill your margin.

Before we dive in, I bundled my 4 best resources into one easy download:

1. The Mini AI Assessment Playbook
2. AI Tools Assessment Report Template
3. AI Concierge Playbook
4. 8 Common AI Services Objections (and how to destroy them)

You can grab all 4 resources (free) here.

Stage 1: Run a free mini AI assessment

The free mini assessment is a 15-minute conversation with a business owner.

Your job is to find one painful bottleneck and prescribe one practical fix.

Ask questions like:

• Where does work tend to pile up?
• Which manual task keeps stealing time from you or your team?
• What have you tried to automate before that didn't work?
• If one process got easier this month, which one would matter most?

You'll probably uncover several problems. Rank the top three, then focus on the best opportunity.

The prescription can be an off-the-shelf AI tool or one AI workflow the owner could build. If the owner spends five hours a week sorting email, you might recommend SaneBox as a simple starting point. The recommendation is the deliverable.

Don't install it for free.

That boundary matters. You're giving the owner a useful plan, not donating an implementation project.

After the first call, schedule a five- to 10-minute follow-up. Walk through the three opportunities you found, explain the one you'd fix first, and give the owner a simple next step.

Then make two asks:

1. Ask for a testimonial or referral if the assessment was useful.
2. Offer a paid assessment to find three to seven more opportunities.

Only run this free version for your first one to three prospects. Its job is to build confidence, collect proof, and create a natural transition into paid work.

Stage 2: Sell the deeper diagnosis

The paid AI tools assessment uses the same basic process, but goes much deeper.

Instead of a 15-minute interview, spend 45 to 60 minutes mapping the owner's most important workflows. Instead of prescribing one fix, identify three to seven AI opportunities.

Score each opportunity on two dimensions:

• Impact: How much money, time, or capacity could this improve?
• Effort: How hard will it be to implement and maintain?

Start with the high-impact, low-effort opportunities. In most cases, those are existing AI tools the business can adopt without a custom build.

The assessment should also document the assumptions behind the ROI.

Ask the owner how much each bottleneck costs in time every week. Then ask what an hour of their time is worth.

Say a business owner spends five hours a week in email and values their time at $250 an hour. That makes email a $1,250 weekly time cost. If a tool cuts the task from five hours to one, the estimated time value returned is $1,000 a week.

That's an estimate, not audited savings. The important part is that the owner supplied both inputs. You're not making up a dramatic ROI number after the call.

Package the findings in a professional report that includes:

• The workflows discussed
• The top bottlenecks
• Three to seven recommended tools or workflows
• An effort-versus-impact ranking
• The assumptions behind each ROI estimate
• A prioritized action plan

(you can download the exact template I use for my reports for free at audittemplate.ai)

Then schedule a 30-minute follow-up to present it. Walk through each problem, your recommended fix, and why it earned its place on the roadmap.

I started pricing these assessments at $500. I later sold them for $1,000, which was high enough for clients to take the work seriously and still easy for qualified owners to approve. I currently sell them for as much as $2,000.

No testimonials yet? Start at $1,000. That's what worked for me. Raise the price as your proof and delivery improve.

The assessment makes money on its own, but its bigger job is exposing the work that belongs in a recurring AI concierge engagement.

Stage 3: Turn the roadmap into a monthly retainer

AI concierge is done-with-you consulting for owners who know they need AI but don't know how to apply it to their business.

My standard engagement includes:

• Two 45-minute strategy calls each month
• Ongoing voice-message access through Voxer
• One scoped workflow improvement at a time
• Help building reusable Claude Skills inside Claude Cowork
• Documentation and team training for the new process

The work follows a framework I call AOA: audit, optimize, automate.

Audit the process

Ask the client to share their screen and show you how the work gets done today. Capture each step without trying to fix it on the spot.

If customer onboarding takes 30 minutes, watch the full sequence. Note where information comes from, which tools the employee opens, what decisions they make, and where the process slows down.

Optimize it before adding AI

Most manual processes contain unnecessary steps.

If the current onboarding workflow has 14 steps and only needs 11, remove the waste first. Automating a bloated process only makes the bad version run faster.

Automate the cleaner version

Once the workflow is clear, turn the optimized process into a Claude Skill. During the call, guide the client through the build, the prompts, and the setup.

This is done with the client, not handed over as a black box. Adoption goes up because the owner understands what the skill does and how it fits into the business.

After the skill works, use Claude to help document the new SOP and train the rest of the team.

Then move to the next workflow.

The list rarely gets shorter for long. Clients cross off one problem, then notice two more opportunities because they finally understand what AI can do inside their operation. That gives the engagement room to continue as long as the work keeps producing value.

The math behind $10K a month

The paid assessments generate useful project revenue. The recurring concierge clients are what make the model predictable.

The monthly math can work several ways:

• 10 clients at $1,000 per month = $10,000
• 7 clients at about $1,430 per month = about $10,000
• 5 clients at $2,000 per month = $10,000

My current concierge clients pay between $1,200 and $2,000 a month. I plan to quote my next new engagement at $2,500.

The delivery model is intentionally light. Two 45-minute calls equal 1.5 hours of scheduled call time each month. At $1,500 per month, that's $1,000 per scheduled call hour before Voxer support, prep, and admin. At $2,000, it's about $1,333 per scheduled call hour before that extra work.

Those numbers only hold if you protect the scope.

Start with people who already trust you

You don't need cold email or a large following to find the first client.

Take out your phone and write down 10 people you know who run small businesses. Friends from the gym or church, parents of your kids' friends, former colleagues, past clients, local business owners you see regularly.

Text three of them and offer the free mini assessment.

The rough math: three free assessments produce one paid assessment, and two paid assessments produce one concierge client. This is a simple funnel to work toward based on my experience with the offer.

If you contact all 10 warm prospects, a reasonable target is five or six free assessments, two or three paid assessments, and one concierge client.

The strongest prospects are owner-led service businesses with repetitive work, visible bottlenecks, urgency, and enough budget to act. My concierge clients have included an insurance agency, an info-product business, an eCom business, a financial advisor, and a fintech company.

Many of the best-fit businesses I see are doing roughly $3 million to $10 million or more in annual revenue.

They already know the manual work is a problem. They don't need a lecture about why AI matters. They need someone to show them where to start and stay close enough to help them use it.

Three ways this model breaks

The offer is simple, but it's easy to ruin the economics.

1. You turn the free assessment into free implementation

One bottleneck. One prescription. Stop at the roadmap.

If you build the workflow during the free engagement, you remove the reason to buy the paid next step and teach the client that your implementation time has no value.

2. Two calls quietly become unlimited calls

My cadence is two 45-minute calls each month. I respond to Voxer messages within 12 business hours, but I don't add unscheduled Zoom calls for free.

Set that expectation before the engagement begins. If a client wants more calls, charge for the added access.

3. You build a different technology stack for every client

Trying to master Claude, ChatGPT, Codex, Zapier, and a pile of niche tools at the same time will crush your margin.

I standardize the core fulfillment process around Claude Cowork and Claude Skills. The skills can be adapted if the preferred AI platform changes, but I don't relearn the entire delivery stack for every account.

Standardization is what turns consulting into a business you can repeat.

The first 10 actions I would take

If I were starting this model from zero today, I would:

1. Make a list of 10 business owners in my network.
2. Invite three of them to a free 15-minute mini assessment.
3. Use the same interview questions every time.
4. Rank the top three bottlenecks for each owner.
5. Recommend one tool or workflow, without implementing it.
6. Ask for a testimonial, referral, and paid follow-up.
7. Price the deeper assessment between $500 and $2,000 based on my proof.
8. Turn the assessment plan into a recurring concierge proposal.
9. Use AOA to improve one workflow per call.
10. Raise my concierge price by $250 after each accepted proposal.

That's the model.

The free assessment opens the door. The paid assessment proves you can diagnose the business. The concierge retainer pays you to help the client act on the plan one process at a time.

You don't need to become an engineer. You need to ask good questions, recommend useful tools, improve a process before automating it, and keep the client moving.

That's enough to build a real AI services business.

If you want the deeper playbooks for the mini assessment, paid assessment, AI concierge offer, and common sales objections, download the free AI services resource library here.

If you want the complete business in a box, including scripts, templates, skills, a 60-day roadmap, and community support, join AI Operator Academy.

Below is the full breakdown: every decision, every deliverable, and the exact playbook I used to turn an experiment into a real business.

This is the foundational work that most folks tend to avoid in the early stages of a new venture:

• What exactly are you selling?
• Who are you selling it to?
• What should you charge?
• How will you fulfill it?
• What will you say when you walk through the door?
• Which businesses should you contact first?

So I opened voice mode inside Codex and gave it a simple assignment:

Help a completely non-technical operator build an AI services business from scratch.

Less than 25 minutes later, I had:

• A defined local AI service
• A simple offer and pricing model
• A one-line sales pitch
• A recommended fulfillment stack
• A privately published website
• A Google Sheet with 10 nearby prospects
• A route ordered from closest to farthest from my neighborhood

I didn't make money in 25 minutes.

I hadn't sold the offer, installed the system, or proven the pricing would work.

But I'd removed most of the blank-page work between "I want to sell AI services" and "I can walk into a business tomorrow with something specific to pitch."

That's the opportunity.

Here's the entire process:

• The five service ideas Codex proposed
• Why I combined lead recovery with review growth
• The exact offer, pitch, and proposed pricing
• The simple tech stack behind it
• How Codex built the website
• How it found the first 10 prospects
• The playbook I'd use to turn this experiment into a real business

Let's get into it.

Start with a painful problem that sits close to revenue

I didn't ask Codex for the most futuristic AI business possible.

I asked for something a non-technical person could start, sell to local businesses, and scale if it worked.

Codex proposed five services:

1. Appointment recovery
2. Review growth
3. A local content engine
4. Estimate follow-up
5. A simple front-desk chat assistant

The first two stood out because the value is easy for a business owner to understand.

Missed calls and ignored web inquiries become lost appointments.

Completed jobs without review requests become lost reputation and future demand.

Both problems already exist inside the business. Instead of trying to convince the owner they need some abstract AI transformation, we're simply showing them where leads are leaking and where good customer experiences are disappearing without producing public proof.

That's a much easier sales conversation.

The lesson: if you're choosing your first AI service, start with a painful workflow that sits close to revenue.

Bundle two outcomes into one clear offer

Instead of selling appointment recovery and review growth as separate services, I asked Codex to combine them.

The result: a "Local Lead and Reputation Engine."

The promise:

We help local businesses turn missed calls and new inquiries into booked appointments, then automatically turn happy customers into five-star reviews without adding work for their team.

The proposed offer included:

• Instant text-back for missed calls
• Immediate follow-up for website and social inquiries
• A simple booking or callback flow
• Appointment reminders
• Automated review requests after a completed job
• A monthly scorecard showing activity and results
• Done-for-you setup and ongoing management

Codex also proposed a seven-day setup promise: setup fee refunded if the system isn't ready on time.

That's a guarantee around something the operator can actually control: implementation speed.

This matters because most weak AI offers describe the technology.

They sell automations, chatbots, workflows, and integrations.

The business owner doesn't care.

They care about answering more leads, booking more appointments, and earning more five-star reviews.

Sell the business result. Keep the AI in the fulfillment layer.

Make the pitch simple enough to say at the front desk

Once the offer was clear, I asked Codex to turn it into something I could say out loud while walking into a local business.

The one-line version:

We help local businesses turn missed calls and new inquiries into booked appointments, then automatically turn happy customers into five-star reviews without adding work for your team.

Then the three-step explanation:

1. We install missed-call text-back and instant follow-up so every lead gets a fast response.
2. We connect a simple booking flow and reminders so more people actually schedule.
3. After each job, we automate review requests to grow the business's Google rating.

That's enough for an initial conversation.

You don't need to explain models, agents, APIs, or workflow logic at the front desk.

The owner needs to understand the leak, the outcome, and how little extra work their team will have.

If your offer takes five minutes to explain, it's still too complicated.

Run the technology behind the scenes

My biggest concern was fulfillment friction.

I didn't want to sell a service that required the client to learn several new tools or replace systems they already use.

Codex recommended one platform, such as GoHighLevel, as the back-end layer.

The operator configures:

• Missed-call text-back
• A shared inbox for new leads
• Follow-up messages
• A booking-link connection
• Review requests tied to the client's Google profile

The client keeps doing two things:

1. Respond to hot leads
2. Mark jobs or visits complete

Everything else runs behind the scenes.

If a prospect already has a sophisticated CRM and strong processes, integrate lightly or skip that client at the beginning.

The easiest first clients are businesses with no meaningful CRM, or businesses that barely use the one they have.

Your positioning becomes:

We don't replace your current systems. We add a managed response and review layer around them.

That's a much lower-friction offer than asking a small-business owner to rebuild how the entire company operates.

Price the pilot before you have proof

Codex proposed a simple founding-client structure:

• $750 one-time setup
• $500 per month
• Cancel anytime after 90 days

After the operator has real proof and repeatable fulfillment, the suggested range increases to:

• $1,500 to $2,500 for setup
• $750 to $1,250 per month

These were proposed prices, not prices I charged or validated during the experiment.

That distinction matters.

The point wasn't that $500 per month is the perfect price for every niche.

The point was to start with a model that's easy to explain, covers the technology and management work, and gives the operator enough time to create a result.

I wouldn't start with pay-per-lead pricing.

It adds attribution fights before you have proof, data, or a reliable baseline.

A setup fee plus a predictable monthly retainer is easier for both sides to understand.

Once you know what a recovered appointment is worth, how many leads the system touches, and how reliably it performs, you can improve the pricing from evidence instead of guessing.

Build the minimum sales asset

Once we had the idea, offer, pitch, pricing, and stack, I asked Codex to build a website using Codex Sites.

It named the business Signal Local and created a simple site around one promise:

Turn every local lead into momentum.

The page explained the service in plain language:

• Respond to missed calls and new inquiries quickly
• Follow up until a lead books or speaks with the team
• Ask satisfied customers for Google reviews
• Set everything up without forcing a new workflow on the staff

It also included a working consultation button, a three-step explanation of the system, and a clear list of everything in the offer.

The site was published privately during the experiment.

Was it perfect?

No.

It was roughly 90 percent there. I still wanted to adjust a few visual details and tighten some copy.

But that's the point.

The website didn't need to win a design award. It needed to make the business look credible enough for the next sales conversation.

Don't spend three weeks polishing a brand before you know whether anyone wants the offer.

Build the minimum sales asset, then go talk to the market.

Find prospects based on visible business leaks

The last step was turning the offer into a prospect list.

I asked Codex to find 10 independently operated service businesses near my neighborhood in Charlotte with:

• Strong Google reviews
• A weak or missing website presence
• Signs of being owner-operated
• A physical location I could visit

I also asked it to put the businesses in a Google Sheet and order them into a practical route.

Codex used public signals only.

It explicitly noted that a missing website link isn't proof a business lacks a CRM. It's a prospecting signal, not a fact.

Two examples from the list made the opportunity obvious.

King Auto Repair showed a 4.5-star rating with 251 reviews and no website link displayed on Google Maps at the time of the research.

Plaza Tire and Auto showed a 4.7-star rating with 140 reviews and no website link displayed there either.

Both appeared to have real customer demand and established reputations. But neither offered an obvious online path to request a quote or book an appointment.

Their recent review activity suggested another opportunity: help the business consistently ask satisfied customers for reviews and professionally respond when negative reviews appear.

Codex added mobile-friendly Google Maps links to the sheet so I could open each profile while visiting the businesses.

That turned generic lead research into an actual walk-in list.

The strongest prospecting insight wasn't "find businesses that need AI."

It was this:

Find businesses with visible demand, then identify where their current process is leaking money.

The playbook I'd use from here

At the end of the experiment, I had enough to begin selling.

Not enough to claim the business worked.

Enough to run the first real test.

If I were taking Signal Local from experiment to operating business, here's the sequence:

1. Pick one niche with frequent inbound calls and valuable appointments.
2. Speak with five owners before making the offer more complicated.
3. Confirm how missed calls, web inquiries, booking, and reviews work today.
4. Sell one transparent founding-client pilot.
5. Collect the setup fee before building the full system.
6. Install only the workflows required for that client's process.
7. Track response time, conversations, bookings, review requests, and reviews generated.
8. Send the client a simple monthly scorecard.
9. Document every setup step as a reusable checklist.
10. Turn the first verified result into proof, then raise the price.

The most important step is number two.

Codex can generate the idea, package the offer, build the website, and organize the prospect list.

It can't replace the conversation with the business owner.

That conversation tells you whether the problem is painful, whether your proposed workflow fits reality, and whether anyone will pay.

Final thoughts

AI didn't build me a proven business in 25 minutes, but it did something more useful than generating another list of ideas.

It compressed the work required to reach the first real sales conversation.

I went from a blank page to:

• One defined problem
• One productized offer
• One proposed pricing model
• One simple pitch
• One credible website
• Ten local prospects

That's enough to stop researching and start testing.

The people who make money with AI services won't be the ones who collect the most business ideas.

They'll be the ones who use AI to move faster, then do the human work AI can't do for them:

Walk through the door.

Ask better questions.

Close the first client.

Deliver the result.

Turn that result into a repeatable business.

The opportunity is using AI to remove the friction between an idea and your next sales conversation.

I documented this entire process in this YouTube video if you'd prefer to see it step by step.

In the next few minutes, I'll show you how one non-technical operator is installing AI "employees" inside small businesses and getting paid a monthly retainer to manage them, with zero coding background and almost no audience.

I recently interviewed Phil Goodwin, a product designer who's completed about 21 agent setups and turned them into recurring revenue in roughly a month of running the managed model.

I've spent the last year selling AI services to small businesses, and this is one of the cleanest early playbooks I've seen.

In this article, I'm giving you the entire playbook for selling managed AI employees:

• The offer structure (and why most people package agents backwards)
• How to pick the first role that creates the "light-bulb moment"
• The dead-simple fulfillment setup (one Telegram group)
• The weekly value ledger that proves ROI to your client
• The exact pricing evolution from $500 setups to $1,000/month retainers
• The 12-step paid-pilot sequence to land your first client

Almost nobody is talking about the managed layer of this business. That's the entire opportunity.

Let's get into it.

Sell a Role, Not an Agent

The foundation of this entire model is a positioning shift. Without it, nothing else matters.

Most people build an agent, install it, hand the client a login, and hope the business owner figures out the rest.

Bad idea.

A small-business owner doesn't wake up wanting a VPS, an LLM, or another AI tool. They want someone (or something) to own work they're tired of doing.

So Phil positions the offer like hiring a digital employee. One clearly defined role, usually admin or executive-assistant work.

What the AI employee comes with:

• A clear job description
• Company knowledge and memory
• Access to the specific tools the role requires
• A dedicated computer or server environment
• Approval gates for consequential actions
• Monitoring and repairs
• Ongoing workflow coaching for the owner

How it operates:

1. A client request or scheduled job triggers the work
2. The agent uses company knowledge and connected tools
3. A human approves sensitive actions when required
4. The agent completes the work
5. The operator monitors the system and improves the workflow

The agent is only one part of the offer. The actual product is the managed business result.

One Thing to Note Before We Continue

A working agent doesn't automatically create adoption.

Phil learned this the expensive way. His first offer was one-time infrastructure setup, and he climbed from $500 to $1,000, $1,500, and $2,000 per project. He'd configure everything, give the client 14 days of support, and move on.

Some clients stopped using what he built.

The technology worked. The clients didn't. They had no idea what to delegate, how to build workflows, or how to talk to an agent. They treated it like Google: ask a question, get an answer, leave.

That gap is exactly why the management layer exists, and it's a large part of what the client is actually paying for. You're selling adoption much more than you're selling the install.

Picking the First Role (+ a Real Client Example)

Don't promise a company unlimited agents. Most small businesses need one painful category of work taken off the owner's plate.

Good starting roles:

• Central-office administration
• Executive-assistant work
• Research and prospecting
• Recurring reports
• File and document management
• Content support

Here's what this looks like in the wild.

One of Phil's early managed clients runs a family directional-drilling business. The owner bounces between contractor hours, field logs, QuickBooks, PDFs, contracts, spreadsheets, and job folders all day.

In one request, the owner asked the agent to:

1. Search his inbox for an email from a specific person
2. Find an invoice for a particular job
3. Create a new job folder
4. Save the invoice PDF inside it
5. Extract the relevant info into a spreadsheet
6. Save the spreadsheet in Dropbox
7. Return the link

The agent came back with the completed sequence roughly 6 to 10 minutes later.

That's the light-bulb moment you're engineering. Not a flashy demo. A real piece of work the owner already understands and already hates doing.

Once that first workflow lands, the client starts asking the most important question in this entire business:

"What else can it do?"

The Fulfillment Setup (One Telegram Group)

Phil's smartest fulfillment decision costs nothing: he creates a Telegram group with three members. The client, the agent, and himself.

The client watches how Phil communicates with the agent. Phil watches what the client requests, spots unclear instructions, and identifies new workflows worth building.

It's observable coaching instead of forcing the client to learn alone.

The full management layer can include:

• The shared client-agent-operator channel
• A monthly workflow review
• Monitoring and failure alerts
• Repairs and maintenance
• Human approval gates
• A shared spreadsheet or other editable source of truth
• Ongoing refinement of the agent's responsibilities

One rule Phil recommends: make the agent write important outputs into a human-readable system like Excel. Research shouldn't disappear into the agent's private memory. Payments shouldn't live only inside a chat. The client and the agent need a shared source of truth they can both inspect and update.

The Weekly Value Ledger (Proving ROI)

This was my biggest takeaway from the conversation.

Phil gives the agent a value ledger. Every task gets logged with what was completed, how long it would normally take a person, the estimated dollar value of that time, and which tasks still need a baseline from the client.

I'd expand it into a weekly report the agent sends the owner every Friday:

WEEKLY VALUE REPORT — [Client Name] — Week of [Date]

TASKS COMPLETED: [list]
AGENT COMPLETION TIME: [total]
ESTIMATED HUMAN TIME RETURNED: [hours]
HOURLY-VALUE ASSUMPTION: [$X/hr — client confirmed Y/N]
BASELINES STILL NEEDED: [tasks awaiting client's time estimate]
ISSUES RESOLVED THIS WEEK: [list]
NEXT WORKFLOW TO ADD: [proposal]

If the agent doesn't know how long a task normally takes, it asks.

That last part matters. The goal isn't to invent impressive ROI numbers. It's to make the estimate visible, let the client correct it, and improve the ledger over time.

Phil's drilling client's ledger estimated roughly 63 hours returned in the first week, and he separately described an estimated $6,300 week. Those are ledger estimates, not audited savings, which is exactly why you agree on the time baseline and hourly value with the client up front.

Most AI operators report what they built. Clients care about what the system did. A weekly value report turns an invisible retainer into a visible operating record.

Getting Your First Client (No Audience Required)

Phil had around 1,500 followers on X when he started. His early clients came from friends, local relationships, and referrals. Not a content funnel.

The sequence was simple. He built agents for himself first. Then he invited people he already knew to test the service with a fully transparent pitch:

"I'm testing a managed-agent service. I'll install the agent, help you build workflows, and charge you a lower early-client price while I improve the offer."

That's a stronger position than pretending to be an established expert, because the client knows exactly what they're getting:

• A lower early-adopter price
• More attention from the operator
• A chance to shape the service
• Honest expectations about where the offer is today

In return, you get experience, feedback, proof, and potentially a referral. Phil's early friend projects introduced him to paying clients in completely different markets.

Pricing (Match Your Price to Your Proof)

Phil's pricing evolved with the maturity of the offer. Here's the full progression:

Early infrastructure-only setups: $500 to $2,000, one-time.

First managed pilots: No setup fee, roughly $250/month to cover infrastructure while he learned the model in two industries.

Recent managed engagement: Roughly $1,500 setup + $500/month, including a monthly call, the shared Telegram channel, and ongoing support.

Stated target: Roughly $2,000 setup + $1,000/month per agent. That's his target, not pricing he's proven repeatedly yet.

The lesson isn't to copy his numbers. It's to match your price to the proof you currently have:

• Don't absorb the infrastructure and model costs yourself
• Charge for at least some of your time
• Explain why an early client is getting a lower price
• Document the work and results
• Raise prices as fulfillment gets repeatable and value gets provable

You don't need to start at $5,000/month. You also don't need to work for free. You can get paid to learn, as long as the client knows that's exactly what's happening.

Optimizing the System & Pro Tips

1. Run the 12-step paid-pilot sequence

If I were starting from zero:

1. Build an agent for yourself first
2. Pick one deployment environment and learn it well
3. Find one business owner in your existing network
4. Identify one painful, repeatable role
5. Sell a transparent paid pilot
6. Define the agent's permissions and approval gates
7. Create a shared client-agent-operator channel
8. Write important outputs into a shared source of truth
9. Track every task in a weekly value ledger
10. Validate the estimates with the client
11. Turn the first clear win into a testimonial or referral
12. Raise pricing only when proof and fulfillment improve

2. Expand the role, not the headcount

Don't rush to sell agent number two. Deepen the first role until the client is asking for more.

3. Respect the risk

If an agent can access email, financial files, company records, or client data, you're responsible for permissions, security, approval boundaries, monitoring, and recovery. Non-technical doesn't mean unaccountable. Stay inside what you can fulfill safely and bring in technical help when the risk exceeds your competence.

Final Thoughts

The agent build gets you through the door. The management, coaching, reliability, and proof are what make the service worth renewing.

You don't need to know everything before you start, you just need to be one useful step ahead of the client, honest about where you are, and capable of staying with the system after launch.

If you want a clear roadmap for building your company knowledge base + managed agents on top of it, grab my free AI Workforce Roadmap.

The full conversation with Phil Goodwin is on the Build With AI podcast, available on YouTube, Spotify, and Apple.

I hope you found this valuable.

- Corey

But they're stuck.

They're drowning in tools and have no idea which ones solve their specific problems.

They don't need a developer. They don't need a course. They don't need another AI newsletter.

They need someone who can look at their calendar, their inbox, their workflows, and say: "This tool. This process. This is where you start."

That's the service I've been selling for $999. And it's working.

Here's my entire model you can copy to start making money this week.

PS: If you want the exact template I use to deliver the $999 assessment you can download it (free) here.

The Model

I charge $999 for what I call an AI Tools Assessment. The client gets a 45-minute interview, a custom report with 3-7 tool recommendations, and a 30-minute walkthrough call.

The guarantee: 5+ hours returned per week, or full refund.

That sounds aggressive. It's not.

Most business owners are losing hours every week to tasks that AI solved years ago. They just don't know which tool to use.

The average client sees 6+ hours per week freed up for an average tool cost of about $40/month.

That's a 25x return on a $999 investment. Every single month.

The Four Phases

Phase 1: The Discovery Call

This is a 45-minute Zoom call. But I'm not pitching solutions. I'm pulling problems.

The questions I ask:

• Walk me through yesterday. Where did your time actually go?
• What tasks do you dread but can't hand off?
• Where does work pile up waiting on you?
• What have you tried to automate but couldn't figure out?

The goal is to hear their pain in their words. Not what they think they should automate. What's actually eating their time.

I record everything with Fathom. The transcript is critical for what comes next.

Phase 2: The AI Analysis

After the call, I feed the full transcript into Claude with a specific skill we've built that does deep analysis. But you can get 80% of the results with a simple prompt like this one:

"Analyze this business owner interview. Identify 5-7 areas where existing AI tools could save significant time. For each opportunity, recommend specific tools and estimate weekly hours saved."

Claude catches patterns I miss. It connects dots across different parts of the conversation.

I review the output, add recommendations from my own toolkit, and cut anything that feels like a stretch.

Phase 3: The Report

I build the final deliverable in Claude Design. Clean. Professional. Looks like something a $10K consulting firm would produce.

The structure:

• Executive summary with top 3 opportunities
• Priority matrix (high impact + low effort = start here)
• Tool recommendations with implementation details
• 4-day quick start plan
• Financial impact breakdown
• Next steps

Takes about 30 minutes to assemble from a template. Clients think it took days.

You can download my exact report template (for free) here.

Phase 4: The Review Call

This is where trust gets built and bigger deals happen.

I screen-share the report. Walk through each recommendation. Answer questions. Let them absorb it.

Then I ask:

• Which of these feels most urgent to you?
• Do you want to implement these yourself, or would you like help?
• What's your realistic timeline?

About 60% want help with implementation. That's where the real revenue lives.

The Expansion Menu

The $999 assessment is the door. Here's what's behind it:

Process Redesign ($3-5K) Almost every assessment uncovers at least one broken workflow. Map the current state, design the future state, remove the waste.

Automation Build ($1-3K) Sometimes they just need Zapier or Make.comworkflows connecting their existing tools. Build it, train them, hand it off.

Knowledge Systems ($3K+) One client had 1,000+ hours of training videos nobody watched. We built a custom GPT his team can query instead of interrupting him. Saves 3 hours weekly.

Custom Workflows ($3-5K) For clients already using AI tools, I build custom prompts and templates that turn manual processes into one-click operations.

Why This Model Works

You're selling clarity + quantifiable outcomes.

Business owners are drowning in options. Every week there's a new tool, a new framework, a new "game-changing" automation.

They need someone to cut through the noise and say: "For your situation, use this."

That's worth $999. Often more.

The On-Ramp

You don't need to be an AI engineer. You need to:

1. Know the landscape Spend a month getting comfortable with Claude, ChatGPT, Fathom, Claude Design, Zapier, Make.com. Use them daily. Understand what they're good at.

2. Practice the process Run 3-5 free assessments. Friends. Family. Local business owners. Build your interview questions. Refine your report template. Get testimonials.

3. Start charging $499 for your first paid client. Then $749. Then $999. Raise your price every few clients until you start hearing "no."

4. Build the backend Create your upsell menu. Process templates. Automation packages. Implementation tiers. The assessment is the entry point. Implementation is where you scale.

The Window

This opportunity exists because there's a translation gap.

On one side: powerful AI tools that keep getting better.

On the other side: business owners who know they should be using AI but don't know where to start.

The people who learn to bridge that gap will stay busy for the next 2-3 years.

Eventually the tools will get simpler. Eventually everyone will figure it out. But right now? The gap is wide open.

Key Takeaways

• The AI consulting opportunity is translating.
• A $999 assessment typically returns 6+ hours/week for clients
• The 4-phase framework: discovery call → AI analysis → polished report → review call
• 50% of assessment clients want implementation help at$3-10K+
• You don't need technical skills. You need to know the tools and ask good questions.
• The translation gap is wide open. It won't last forever.

You don't need to be a developer. You don't need to code.

All you need is a strong system prompt and a tool called VoiceFlow. That's it.

Let's jump in.

(btw I built this live on the Build With AI podcast if you'd rather watch it in screen-share format)

The mental barrier most people have around voice agents

People hear "voice agent" and think: chatbot but worse. Janky. Robotic. Frustrating for customers.

But a voice agent is fundamentally different from a chatbot.

A voice agent listens, decides, and acts using defined rules. A good voice agent is capable of executing real workflows.

The stakes feel high because they are. When a customer calls, they expect a professional experience. Get it wrong and it reflects on your entire business.

So here's what stops people: they never start, because they're afraid of getting it wrong in production.

The solution is to build them right before you go live.

The 3 Control Levels framework

Before you build anything, understand autonomy levels.

In the Loop (Most Control): Human approves everything. AI gathers information, human decides and acts. Traditional customer service model.

On the Loop (Medium Control): AI decides within defined rules, human supervises outcomes. This is where most voice agents live. The AI makes decisions, but you're monitoring.

Fully Autonomous (Least Control): AI decides freely. Humans only react if damage occurs.

Which one should you use? Ask this: What's the impact of a bad decision?

For appointment booking? Medium control (on the loop). For emergency situations? High control (in the loop). For customer onboarding? On the loop.

The impact determines your autonomy level.

The Architecture

Everything lives in a single Google Doc with two sections:

Section 1: Agent Instructions (Who you are, how you behave)

• Identity and Purpose: "You're a virtual scheduling assistant"
• Role: "Help customers determine if they need service, then book appointments"
• What you DO: Schedule appointments, send confirmations, handle same-day requests
• What you DON'T do: Troubleshoot problems, diagnose issues, speculate about safety
• Voice and Persona: "Friendly but brief. Conversational but professional."
• Speech Characteristics: "Sound human. Be transparent that you're automated, but don't be awkward about it."

Section 2: Knowledge Base (What you know)

• Service area and zip codes
• Business hours and appointment availability
• Emergency vs. non-emergency procedures
• Common services offered
• What triggers an escalation to a human

The prompt template

Here's the exact structure:

Identity and Purpose: [What you do and why]

Voice and Persona: [Friendly but brief, conversational tone]

Key Rules:
- Do not troubleshoot. Your job is to collect info and schedule.
- Ask one question at a time (never: "Name, address, phone?")
- Keep responses short. LLMs default to verbose.
- Only ask one question at a time
- Use explicit confirmation on all critical info
- Use layman's terms, not jargon
- Show empathy but don't apologize excessively
- If they can't answer twice, escalate to a human

Escalation Conditions:
- Safety concerns → end call immediately
- Irate customer → transfer to human
- Can't help (e.g., commercial property) → transfer with alternative
- Failed twice on same question → transfer (don't keep asking)
- Frustrated customer → pause, acknowledge, offer human support

Conversation Flow:
1. Greeting + Emergency check
2. Qualification (Do we serve your area?)
3. Information collection (name, address, phone, problem)
4. Confirmation (repeat details back)
5. Scheduling or escalation

Call Management:
- Background noise: pause and ask for quiet
- Call drops: call back using their number
- Transfer to human: hand off without repeat

That's your system prompt. Copy it and adapt it for your business.

Setting it up in Voiceflow

Step 1: Paste your prompt into a new project.

VoiceFlow automatically generates:

• Conversation flow
• Branching logic
• Information collection steps
• Escalation handlers

You don't diagram this by hand. VoiceFlow builds it for you.

Step 2: Add your knowledge base.

Export your facts (hours, services, pricing, emergency procedures) as a text file or Google Doc.

Upload it. VoiceFlow indexes it.

Now when the agent answers questions, it pulls from your knowledge base, not from general knowledge. This prevents hallucinations and keeps answers accurate.

Step 3: Connect data collection to a spreadsheet.

When the agent collects information (name, phone, service needed), it automatically dumps that into a Google Sheet.

One click to connect. No code.

You can route to Calendly, your CRM, Make.com, or any tool VoiceFlow integrates with (there are dozens).

Step 4: Test by calling it.

VoiceFlow gives you a phone number. Call it. Have a full conversation.

The agent will:

• Ask your emergency question first
• Check if your zip code is in service area
• Collect your information one question at a time
• Confirm details
• Schedule or escalate

All without you touching code.

Why this works (the Clarity Principle)

Most voice agent projects fail because people jump straight to "build" without thinking.

They skip the planning phase.

But the real magic is in the prompt.

A weak prompt = weak agent. You'll spend days tweaking.

A strong prompt = one-shot success. VoiceFlow builds clean conversation flows automatically.

The difference = hours of upfront thinking.

Ask yourself:

• What's my greeting?
• What's the emergency check?
• What's my triage?
• What information do I need?
• What happens if I can't help?
• How do I handle frustrated customers?
• When do I escalate to a human?

Answer those questions first. Everything else follows.

Edge case management

Edge Case 1: Asking the same question twice

Customer: "What's your phone number?" Agent: "I don't understand. What's your phone number?" Customer: (repeats) Agent: "I still don't understand."

Result: Customer rage. Bad experience.

Fix: If they fail twice on the same question, escalate immediately. Don't make them repeat themselves.

Edge Case 2: The apologetic agent

Agent: "I'm sorry, I don't understand. I'm so sorry." Customer: I don't care about your apologies. Just help me.

Fix: Empathy without excessive apologizing. Acknowledge. Move forward. Don't take on their problems.

Edge Case 3: The agent that won't give up

Customer: "I need an electrician." Agent: "Are you experiencing an emergency?" Customer: Silence or non-answer. Agent: (tries to diagnose the problem instead of escalating)

Result: Agent steps outside its lane. Quality drops.

Fix: Be explicit in your prompt: "Do not troubleshoot. Your job is to collect information. If you can't get the info you need, escalate."

LLMs will try to "win" by solving the problem. Guardrails prevent that.

Edge Case 4: The terrible handoff

Agent: "I'm transferring you to a human." (Phone disconnects) (Customer has to explain everything again)

Result: Terrible experience after a smooth automation.

Fix: When transferring, hand off with context. The human gets a summary: "Name: Susan. Location: Clarendon Hills. Issue: Electrical problem. Already collected: phone number, service address."

ROI breakdown

Time:

• Manual scheduling: 5-10 minutes per call
• Voice agent: 2-3 minutes per call
• 20 calls/day = 2.5 hours saved daily

Cost:

• Part-time scheduler: $25-35/hour
• Voice agent: ~$0.50-1.00 per call
• 600 calls/month = $300-600 vs. $2,000-2,800 for human labor

Quality:

• After-hours availability: Your agent works 24/7
• Zero scheduling conflicts: No double-booking
• Consistent experience: Same professional greeting every time

Build yours this week

Monday: Define your flow on paper. Answer the 7 questions above.

Tuesday: Write your prompt in a Google Doc. Ask Claude to refine it.

Wednesday: Create your knowledge base document (hours, services, emergency procedures).

Thursday: Log into VoiceFlow. Create project. Paste prompt. Add knowledge base.

Friday: Test by calling your agent. Iterate based on what breaks.

Following week: Go live.

Total investment: $20/month (VoiceFlow Pro). No code or expensive devs required.

If you want screen-share style live builds of AI systems (like this one) then check out the Build With AI podcast.

But most of those people will never actually build it.

This article explains exactly why you should and shows you exactly how to do it this weekend.

PS: Want the skill that builds this system for you?

Grab the free LLM Knowledge Base skill.

Why Most "Second Brain" Systems Fail

The typical second brain workflow looks like this:

1. Find something interesting
2. Save it somewhere
3. Tag it
4. Link it to related notes
5. Review it weekly
6. Forget about it anyway

The failure point is always Step 3-5. The maintenance. Nobody maintains their system past week two because the overhead of organizing knowledge is higher than the value of retrieving it.

The fix: Remove the human from the organizing step entirely. Let AI handle the taxonomy, the cross-referencing, the summarizing, and the gap-filling. Your only job is dumping raw information into a folder.

The Architecture (Takes 5 Minutes)

Create a folder anywhere on your computer. Inside it, create three subfolders:

second-brain/
├── raw/ (your input dump)
├── wiki/ (AI-organized knowledge base)
├── outputs/ (AI-generated reports and answers)
└── CLAUDE.md (operating instructions for the AI)

raw/ is where everything goes. Meeting notes, article clips, competitor screenshots, client briefs, SOPs, strategy notes, podcast transcripts, email threads. Don't organize any of it. Don't rename files. Just dump.

wiki/ is Claude's territory. It reads raw/, identifies topics, creates organized articles, links them together, and maintains an index. You never touch this folder by hand.

outputs/ stores every answer Claude generates when you query the system. Briefings, comparisons, decision frameworks. Each output feeds back into future answers.

That's the whole thing. Three folders and an instruction file.

The Instruction File (CLAUDE.md)

This is what separates a smart system from a dumb one. The CLAUDE.md file tells the AI what your knowledge base is about, how to structure it, and what rules to follow.

# Second Brain Operating Manual

## Purpose
Business operations knowledge base for [YOUR COMPANY NAME].
Used for: decision support, client context, competitive intelligence,
operational memory, content research.

## Structure Rules
- raw/ contains unprocessed source material. Never modify raw files.
- wiki/ is the organized knowledge base. AI maintains this entirely.
- outputs/ stores generated reports and analysis.

## Wiki Standards
- One topic per file in wiki/
- Every file starts with a 2-sentence summary
- Related topics linked using [[topic-name]] format
- INDEX.md maintained alphabetically, updated with every change
- When new raw sources arrive, update all relevant wiki articles
- Flag contradictions between sources immediately

## My Business Context
- Company: [NAME]
- What we sell: [PRODUCT/SERVICE]
- Who we serve: [TARGET CUSTOMER]
- Current priorities: [TOP 3 PRIORITIES]
- Revenue model: [HOW YOU MAKE MONEY]

## Output Format
When I ask for a recommendation: Context → Options (max 3) →
Recommendation → Risk → Next Step.
When I ask for a briefing: Summary (3 sentences) → Details →
Action Items.

Why this matters: Without this file, Claude improvises every single time. The output quality varies wildly. With it, every interaction follows your exact decision-making framework. You're essentially giving the AI a job description on day one.

Loading Your Knowledge Base (The 15-Minute Sprint)

Most people create the folders, stare at an empty system, and never come back. Here's how to get past that resistance.

Set a timer for 15 minutes. Dump everything you already have:

• Meeting notes from the last 30 days (copy-paste from wherever they live)
• Client documents (proposals, contracts, briefs)
• Competitor research (screenshots, pricing pages, feature comparisons)
• SOPs (even rough drafts or bullet points)
• Strategy notes (investor updates, quarterly plans, revenue models)
• Content research (articles you've saved, podcast notes, bookmarks)
• Email threads that contain important decisions

Don't rename anything. Don't organize it. Don't make it "presentable." The whole point is that you're offloading the organization to AI.

I run 30+ source files in my own system. Clipped articles, analytics exports, partnership research, content frameworks, client transcripts. None organized by hand. All searchable through the wiki Claude maintains.

Building the Wiki (One Prompt)

Open Claude (the desktop app, Claude Code, or any interface that can read local files). Point it at your second-brain folder and use this prompt:

Read everything in raw/. Following the rules in CLAUDE.md, build a
complete wiki in wiki/.

Start by creating INDEX.md listing every major topic alphabetically.
Then create one .md file per topic. Link related topics using
[[topic-name]] format. Summarize every source document.

Flag any contradictions you find between sources.

Walk away. Let it work.

When it finishes, you'll have an organized wiki with:

• Topic articles you didn't know you needed
• Connections between ideas you hadn't noticed
• Summaries of documents you forgot you saved
• An index that makes everything retrievable in seconds

This is the moment it stops being a filing system and starts being a thinking partner.

Using It Like a Real Strategist

Once the wiki has 10+ articles, start querying it like you'd query a brilliant advisor who has read everything you've ever written:

Based on everything in the wiki, what are the three biggest risks
to hitting our Q2 revenue target?

I have a call with [PROSPECT NAME] tomorrow. Pull everything we
know about their company, their pain points, recent news, and our
best positioning angle. Format as a one-page briefing.

Compare our pricing strategy from 6 months ago versus now. What
changed? Is the new approach working based on the revenue data
in raw/?

I need to make a decision about [X]. Based on everything in this
knowledge base, what would you recommend and why?

The compounding effect: Every answer Claude generates, save it back into the system. Each query makes the next answer better because it has more context. After 30 days of regular use, the system knows your business better than most employees would after 6 months.

The Monthly Audit (5 Minutes, Non-Negotiable)

Once a month, run this prompt:

Review the entire wiki/ directory. Complete this audit:
1. Flag contradictions between articles
2. Find topics mentioned but never fully explained
3. List claims not backed by a source in raw/
4. Identify stale information (>90 days without update)
5. Suggest 3 new articles to fill knowledge gaps

This prevents knowledge drift. If Claude writes something slightly wrong early on and you don't catch it, every future answer builds on that mistake. The monthly audit is your quality control layer. Five minutes of prevention saves hours of compounding errors.

Why This Matters for Small Business Owners

A fractional Chief of Staff costs $5,000-15,000/month. A full-time one runs $150,000-250,000/year. This system costs $20/month (Claude Pro) plus $0 (local folders are free).

It won't replace a human strategist forever. But for a business doing $500,000-5M that isn't ready for that hire, this fills the gap. You get:

• Instant recall of every client conversation
• Every competitive insight cross-referenced
• Every strategic decision documented and retrievable
• Available 24/7 with zero ramp-up time
• Gets smarter every single week you use it

The business owners who build this now will have a 6-12 month knowledge advantage over everyone who keeps their strategy in their head and their notes scattered across 14 apps.

Start Today

1. Create the folder structure (2 minutes)
2. Write your CLAUDE.md file (5 minutes)
3. Dump your existing documents into raw/ (15 minutes)
4. Run the wiki-building prompt (5 minutes)
5. Ask your first question (1 minute)

Total setup time: under 30 minutes.

Three folders. One instruction file. An AI that organizes everything and gets smarter the more you use it.

That's a second brain that actually thinks.

PS: Want the skill that builds this system for you?

Grab the free LLM Knowledge Base skill.

I've spent the last 6 months testing prompts across my workflows: business strategy, content creation, automation, and research.

Here are 35 of my most valuable, battle-tested prompts.

Copy/paste them into Claude and see what happens.

Bookmark this article. Come back to it when you need a productivity boost.

PS: Every week I send an email with tools, prompts, and automations designed to turn AI into ROI in your business.

Sign up for the (free) weekly email here.

Strategy & Analysis (10 Prompts)

These prompts help you think through problems more clearly than you could alone. They're for the decisions that actually matter.

1. Business Model Validation

I'm considering launching [BUSINESS_IDEA]. Here's what I know:
- Target customer: [DESCRIPTION]
- Problem they have: [PROBLEM]
- Proposed solution: [SOLUTION]
- Current market alternatives: [ALTERNATIVES]
- My unfair advantage: [ADVANTAGE]
Analyze this business model. For each of these, give me:
1. Three specific risks I haven't thought of
2. One assumption I should validate FIRST before building anything
3. A revenue model that makes sense given my advantages
4. The most likely way this fails in year 1
5. One thing I should double-down on
Format as: [Risk] → [Why] → [How to mitigate]

Why this works: It forces you to articulate what you actually know vs. what you're guessing. Claude catches the gaps.

2. Competitive Differentiation Matrix

I'm competing in [MARKET]. My competitors are:
- [COMPETITOR A]: [KEY_STRENGTH], [KEY_WEAKNESS]
- [COMPETITOR B]: [KEY_STRENGTH], [KEY_WEAKNESS]
- [COMPETITOR C]: [KEY_STRENGTH], [KEY_WEAKNESS]
My business: [YOUR_DESCRIPTION]
Create a 5x5 matrix comparing: Price, Quality, Speed, Customer Support, Innovation
Then tell me:
1. Where am I winning?
2. Where am I vulnerable?
3. One position I could own that nobody else is competing for?
4. The one feature/service that would make me unbeatable?

Why this works: Competitive analysis done wrong is just noise. This forces comparison across the same dimensions.

3. Decision Matrix for High-Stakes Choices

I need to choose between [OPTION_A] and [OPTION_B].
Here's what I know about each:
OPTION A:
- Upside: [UPSIDE]
- Downside: [DOWNSIDE]
- Time commitment: [TIME]
- Financial cost: [COST]
- Reversibility: [CAN_I_UNDO_IT]
OPTION B:
- Upside: [UPSIDE]
- Downside: [DOWNSIDE]
- Time commitment: [TIME]
- Financial cost: [COST]
- Reversibility: [CAN_I_UNDO_IT]
My priorities: [PRIORITY_1], [PRIORITY_2], [PRIORITY_3]
Build a decision framework that ranks these options using my priorities. What's my move?

Why this works: When you have competing priorities, ranking by gut feeling fails. This structures it.

4. Go/No-Go Project Evaluation

Project name: [PROJECT_NAME]
Goal: [GOAL]
Resources required: [TIME/MONEY/PEOPLE]
Timeline: [START_DATE] to [END_DATE]
Success metric: [HOW_YOU'LL_KNOW_IT_WORKED]
Current context:
- My bandwidth: [YOUR_CAPACITY]
- Strategic fit: [HOW_IT_ALIGNS]
- Opportunity cost: [WHAT_YOU_GIVE_UP]
As a decision advisor, should I start this project NOW, DELAY it, or KILL it? Give me:
1. Your recommendation with confidence level (0-100%)
2. Three hidden costs nobody mentions
3. One question I should ask before deciding

Why this works: Most projects fail because they were never worth starting. This helps you say no.

5. Revenue Bottleneck Analysis

I'm making [CURRENT_REVENUE] with [NUMBER_OF_CLIENTS].
My unit economics:
- Average deal size: [ADS]
- Sales cycle length: [CYCLE]
- Close rate: [RATE]
- Customer acquisition cost: [CAC]
- Customer lifetime value: [LTV]
To reach [TARGET_REVENUE], I need to:
1. Increase deal size by X%
2. Decrease sales cycle by Y days
3. Improve close rate by Z%
4. Add more clients by N%
Which of these four levers is the highest-impact move I can make in the next 90 days?

Why this works: Revenue doesn't grow evenly. There's always one lever that matters most. This finds it.

6. Customer Feedback Pattern Analysis

Here's feedback from [X] customers this month:
[PASTE FEEDBACK]
Analyze this. For each pattern you see:
1. Name the pattern
2. How many customers mentioned it
3. Why it matters for my business
4. Specific action I should take
Then rank them: Most important to act on vs. Nice to have

Why this works: Feedback without analysis is just noise. Claude spots the themes you miss.

7. Pricing Strategy Stress Test

I'm currently charging [PRICE] for [OFFER].
My costs: [COSTS]
My value to the customer: [VALUE]
Market average: [MARKET_PRICE]
Customer acquisition cost: [CAC]
I'm considering raising to [NEW_PRICE].
Tell me:
1. Will I lose customers? What's the risk?
2. Will I make more money even with lower volume?
3. What's my "nuclear option" if this backfires?
4. What should I say to customers about the increase?
5. One position/angle that justifies this price to skeptics?

Why this works: Pricing is psychology + math. This gets both right.

8. Team Scaling Analysis

My current team:
- [PERSON A]: [ROLE], [CAPACITY], [BOTTLENECK]
- [PERSON B]: [ROLE], [CAPACITY], [BOTTLENECK]
- [PERSON C]: [ROLE], [CAPACITY], [BOTTLENECK]
My current output: [OUTPUT]
Target output: [TARGET]
Timeline: [WHEN]
What's the fastest path to hit my target? Should I:
1. Hire someone new?
2. Train existing people?
3. Restructure roles?
4. Delegate differently?
5. Use automation?
Give me a 90-day roadmap.

Why this works: Team scaling fails when you don't diagnose the real bottleneck. This does.

9. Product-Market Fit Validator

My product: [DESCRIPTION]
My target customer: [DESCRIPTION]
Current traction: [CURRENT_METRICS]
Signals that suggest product-market fit:
- [SIGNAL_1]
- [SIGNAL_2]
- [SIGNAL_3]
Signals that suggest we DON'T have it yet:
- [SIGNAL_1]
- [SIGNAL_2]
- [SIGNAL_3]
Based on this, how close am I? What's the ONE thing I should test next?

Why this works: Product-market fit is a feeling, not a fact. This validates which feeling you should trust.

10. Strategic Pivot Scenario Analysis

I'm considering pivoting from [CURRENT] to [PROPOSED].
Why: [REASON]
Risk: [RISK]
Resources I'd need: [RESOURCES]
Model this scenario:
1. If I pivot and it works, where am I in 12 months?
2. If I pivot and it fails, what have I lost?
3. If I don't pivot but my competitor does, where are they?
4. What's the reversibility? Can I go back if it doesn't work?
5. The ONE metric that tells me to pivot vs. stay?

Why this works: Pivots are scary because the downside feels infinite. This makes it concrete.

Marketing & Content (10 Prompts)

These prompts help you create content that actually converts instead of just getting views.

11. Hook Generator for Ad Copy

Product: [PRODUCT_NAME]
What it does: [WHAT]
Benefit to customer: [BENEFIT]
Target person: [WHO]
Generate 10 different opening lines (hooks) for ad copy. Each hook should:
- Create curiosity or discomfort
- Be specific (not vague)
- Make someone stop scrolling
- Be 5-12 words max
Format: "Hook text" - [Why it works]

Why this works: 80% of conversions happen in the first line. These are production-ready.

12. Email Subject Line A/B Test Generator

Campaign goal: [GOAL]
Offer: [OFFER]
Target audience: [AUDIENCE]
Email body preview: [PREVIEW]
Generate 8 email subject lines. For each, tell me:
1. The psychology behind it (curiosity gap, urgency, specificity, etc.)
2. Estimated open rate increase vs. baseline
Split them into two groups:
GROUP A: Conservative (high probability of open, less risky)
GROUP B: Aggressive (curiosity/urgency focused, higher variance)
Which 2 should I A/B test first?

Why this works: Subject lines make or break email campaigns. This gives you tested patterns.

13. Product Description Copy (Benefits Focus)

Product name: [NAME]
Feature list: [FEATURES]
Who buys this: [CUSTOMER]
Their main problem: [PROBLEM]
Price: [PRICE]
Write a 150-word product description that:
- Leads with the biggest benefit (not the feature)
- Includes ONE specific number or example
- Addresses the most common objection
- Ends with a clear next step
Format: [Description] - [Why this version converts]

Why this works: Features don't sell. Benefits do. This flips the script.

14. LinkedIn Article Title Generator

Topic: [TOPIC]
Main takeaway: [TAKEAWAY]
Your unique angle: [ANGLE]
Target audience: [AUDIENCE]
Generate 12 LinkedIn article titles. For each, score it on:
- Shareability (0-10)
- Click probability (0-10)
- Credibility (0-10)
Give me the top 3 that balance all three.

Why this works: LinkedIn rewards specific, bold titles. These formulas work.

15. Sales Page Section Outline

Product: [PRODUCT]
Ideal customer: [CUSTOMER]
Price: [PRICE]
Offer: [OFFER]
Create a sales page structure with these sections:
1. Headline + subheadline
2. [YOUR SECTIONS]
3. CTA + urgency element
For each section, write:
- The section name
- The purpose (what it does)
- 3 bullet points of content
- The psychological principle at work

Why this works: Sales pages fail because they're not structured. This gives you the blueprint.

16. Content Repurposing Framework

Original content: [CONTENT_TYPE] - [TOPIC]
Length: [LENGTH]
Original audience: [AUDIENCE]
I want to repurpose this into:
- [FORMAT_1]
- [FORMAT_2]
- [FORMAT_3]
For each format, tell me:
1. What to keep from the original
2. What to cut or condense
3. What to add or expand
4. Best platform to publish it
5. Estimated time to adapt

Why this works: Every piece of content can become 5 pieces. This shows you how.

17. Competitor Content Analysis

Competitor: [NAME]
Their top 3 recent posts:
- [POST_1]: [ENGAGEMENT_METRICS]
- [POST_2]: [ENGAGEMENT_METRICS]
- [POST_3]: [ENGAGEMENT_METRICS]
Analyze:
1. What's working for them? Why?
2. What pattern do you see across their top posts?
3. What angle are they NOT covering?
4. How could I own the gap they're leaving?
Give me 3 content ideas that differentiate me from them.

Why this works: The best content doesn't follow trends, it creates them in gaps.

18. Call-to-Action Optimizer

Current CTA: [CURRENT_CTA]
Current conversion rate: [RATE]
Offer: [OFFER]
Objection: [MAIN_OBJECTION]
Generate 8 different CTAs that address the objection while increasing urgency.
Rank them by:
- Clarity (0-10)
- Urgency (0-10)
- Credibility (0-10)
Give me the top 3 alternatives to test.

Why this works: CTA can 2-3x conversions with one word change. These work.

19. Launch Plan Content Calendar

Launch date: [DATE]
Product: [PRODUCT]
Launch phases: [PHASES]
Audience channels: [CHANNELS]
Build me a 30-day content calendar:
- Pre-launch (7 days before)
- Launch week
- Post-launch (14 days after)
For each day:
- What to publish
- Which channel
- Purpose (awareness, credibility, urgency, etc.)
- Key message

Why this works: Launches fail without a content plan. This provides it.

20. Customer Success Story Framework

Customer: [NAME]
Their situation before: [BEFORE]
What they used: [SOLUTION]
Result after: [AFTER]
Timeline: [HOW_LONG]
Create a 300-word case study that:
- Opens with their biggest pain
- Explains what changed
- Shows the quantified result
- Includes 1 quote from them
- Ends with how others can replicate it
Format for: Website + LinkedIn + Email

Why this works: Stories convert 10x better than claims. This structures them right.

Productivity & Personal (10 Prompts)

These are for getting things done, not just thinking about getting things done.

21. Weekly Review Framework

This week I:
- Accomplished: [WINS]
- Failed to complete: [INCOMPLETE]
- Learned: [LEARNINGS]
- Spent time on: [TIME_BREAKDOWN]
For next week, I need to:
[GOALS]
Analyze:
1. Where did time actually go vs. where I planned it?
2. What's the ONE thing that would make next week 10x more productive?
3. What should I eliminate entirely?
4. What patterns do you see in my performance?
Give me 3 specific changes for next week.

Why this works: Weekly reviews are routine. This makes them actually useful.

22. Priority Filter for Decision Fatigue

I have these things competing for my time this week:
- [TASK_A]: [IMPACT], [TIME], [DEADLINE]
- [TASK_B]: [IMPACT], [TIME], [DEADLINE]
- [TASK_C]: [IMPACT], [TIME], [DEADLINE]
- [TASK_D]: [IMPACT], [TIME], [DEADLINE]
- [TASK_E]: [IMPACT], [TIME], [DEADLINE]
My constraints:
- Available hours: [HOURS]
- Peak energy time: [WHEN]
- Non-negotiables: [FIXED_COMMITMENTS]
Rank these by: Impact per hour spent. Then build my ideal week with start/end times for each task.

Why this works: When you have too many tasks, you paralyzed. This unparalyzed you.

23. Habit Formation Roadmap

Habit I want: [HABIT]
Why I want it: [WHY]
Current attempts: [ATTEMPTS_AND_FAILURES]
Current blocker: [WHAT_STOPS_YOU]
Design me a habit formation plan:
1. Break the habit into the smallest possible first step
2. Stack it onto a habit I already do
3. What's the specific trigger?
4. What's the reward?
5. How do I track it?
6. When do I increase difficulty?
7. What's my backup plan when I miss a day?

Why this works: Most habits fail because the first step is too big. This makes it tiny.

24. Email Triage System

My email volume: [DAILY_COUNT]
Categories that come in: [CATEGORIES]
Current system: [CURRENT_SYSTEM]
Problem: [PROBLEM]
Design an email system that:
1. Takes less than 30 minutes daily to process
2. Doesn't let important things fall through
3. Separates urgent from important
4. Automates low-value email
Give me:
- Filter rules (if-then statements)
- Folder structure
- Processing time blocks
- Escalation rules

Why this works: Email is a productivity killer until you triage it right.

25. Deep Work Schedule Optimizer

My ideal deep work session: [LENGTH] hours
My energy pattern: [PEAK_TIMES]
My meetings: [FIXED_MEETINGS]
My distractions: [MAIN_DISTRACTIONS]
Build me a weekly schedule that:
- Protects [X] hours of uninterrupted deep work
- Batches all meetings into [NUMBER] days
- Creates transition buffers
- Includes breaks
Show me:
- Exact time blocks
- How to communicate these to my team
- What to do during low-energy time
- Backup schedule if things change

Why this works: Deep work requires schedule protection, not willpower.

26. Decision Journal Template

Decision: [WHAT_YOU'RE_DECIDING]
Date: [TODAY]
Deadline: [WHEN_YOU_NEED_ANSWER]
Before deciding, record:
- What I think will happen: [PREDICTION]
- Why I think that: [REASONING]
- Confidence level: [0-100%]
- Decision: [WHAT_I'M_DOING]
Create a template I can use to track:
1. What I predicted
2. What actually happened
3. Why the gap exists
4. What I learned for next time
Example template with 3 sample entries from my past decisions.

Why this works: Decision journals make you better at predicting over time.

27. Saying "No" Response Templates

Common requests I get:
- [REQUEST_TYPE_1]
- [REQUEST_TYPE_2]
- [REQUEST_TYPE_3]
Create 3 different "no" templates:
1. The warm no (keeps relationship)
2. The firm no (clear boundary)
3. The redirect no (offer alternative)
For each:
- Use case
- Full script (copy-paste ready)
- Why it works
- When to use which one

Why this works: Saying no is hard until you have a script.

28. Meeting Agenda Template

Meeting purpose: [PURPOSE]
Attendees: [WHO]
Duration: [MINUTES]
Goal: [WHAT_SUCCESS_LOOKS_LIKE]
Create an agenda template that:
- Opens with the goal (1 minute)
- Lists discussion topics with time allocations
- Includes decision points
- Closes with clear next steps and owners
Then give me:
- Instructions for the agenda-setter
- Instructions for attendees
- How to stay on time
- How to document decisions

Why this works: Agendas prevent meetings from becoming noise.

29. Energy Management Schedule

My energy pattern across a week:
- Morning: [ENERGY_LEVEL]
- Midday: [ENERGY_LEVEL]
- Afternoon: [ENERGY_LEVEL]
- Evening: [ENERGY_LEVEL]
High-energy activities I can do: [ACTIVITIES]
Low-energy activities I must do: [ACTIVITIES]
Design a week where:
1. High-impact work happens during peak energy
2. Admin/low-value work happens during low energy
3. I take breaks BEFORE energy crashes
4. I finish each day with momentum for the next day
Show me the week structure with activities assigned.

Why this works: Most productivity systems ignore energy. This doesn't.

30. Personal Quarterly Review

Last quarter I:
- Goals set: [GOALS]
- Goals completed: [COMPLETED]
- Surprises: [UNEXPECTED]
- Regrets: [WHAT_I'D_CHANGE]
- Growth: [HOW_I_GREW]
For next quarter:
- Keep doing: [KEEP]
- Stop doing: [STOP]
- Start doing: [START]
Give me:
1. 3 goals that are ambitious but achievable
2. The ONE habit that would make everything else easier
3. My personal development focus
4. How I'll measure success

Why this works: Most people don't review progress. Reviewing changes everything.

Research & Data (5 Prompts)

When you need information quickly and structured.

31. Competitor Intelligence Summary

Company: [COMPANY_NAME]
Industry: [INDUSTRY]
Why I care: [WHY]
Give me:
1. Business model (how they make money)
2. Main product/service
3. Recent funding/news
4. Their competitive advantages
5. Known weaknesses
6. Their likely next move
Format: [Bullet points for quick scanning]

Why this works: Competitor research without analysis is just facts. This gives you insight.

32. Industry Trend Analysis

Industry: [INDUSTRY]
Timeframe: [YEARS]
My interest: [WHY]
Summarize the major shifts in:
1. Customer behavior
2. Technology adoption
3. Regulatory changes
4. Competitive landscape
5. Pricing models
For each trend:
- What's changing
- Why it matters
- Opportunities I should notice
- Timeline for when it matters

Why this works: Trends matter less than understanding which ones affect you.

33. Market Size Estimation

Market: [MARKET]
Customer: [WHO_BUYS]
Price point: [TYPICAL_PRICE]
Geographic focus: [WHERE]
Help me estimate:
1. Total addressable market (TAM)
2. Serviceable addressable market (SAM)
3. Serviceable obtainable market (SOM)
For each:
- Your methodology
- Key assumptions
- Range of estimates
- Confidence level
- What would change the estimate

Why this works: Market sizing is part art, part science. This guides both.

34. Benchmark Research Compilation

Benchmark category: [CATEGORY]
Metric: [METRIC]
Industry: [INDUSTRY]
Company size: [SIZE]
Find me:
1. Industry average
2. Top quartile (where the winners are)
3. Bottom quartile (what underperforming looks like)
4. Median
5. Trend over time (if available)
For each number:
- Source (if you know it)
- Sample size (if known)
- How it's calculated
- Why it matters for my business

Why this works: Benchmarks give you context. Without context, metrics are meaningless.

35. Expert Research Synthesis

Topic: [TOPIC]
Question: [WHAT_YOU_WANT_TO_KNOW]
Perspective: [WHOSE_VIEW_MATTERS]
Synthesize research on this topic by:
1. Main schools of thought (if they disagree)
2. The most credible voices on this
3. What the data actually shows
4. Common misconceptions
5. What's still uncertain/debated
6. Practical application for my situation
Format: [Executive summary at top, then detailed breakdown]

Why this works: Research without synthesis is just noise. This makes it useful.

How to Actually Use These

These prompts work because they're specific. But here's the thing: Claude is better than these prompts. If you use them as templates and adapt them to your exact situation, they become 10x more powerful.

Step 1: Copy a prompt that matches your situation.

Step 2: Fill in the [BRACKETS] with your actual information.

Step 3: Paste into Claude and hit send.

Step 4: Use the output as a starting point, not a finished answer.

The prompt isn't the intelligence. You are. The prompt just structures your thinking so Claude can help you think clearer.

Key Takeaways

• Strategy prompts force you to articulate what you actually know vs. guessing
• Content prompts turn gut feeling into frameworks that work
• Productivity prompts protect your time instead of just managing it
• Research prompts turn information into insight
• All of these are templates. Adapt them to your situation for better results
• Claude is a thinking tool, not an answer machine. Use these to think clearer, faster

Start with one prompt in each category. Use it weekly. Watch your decision-making get faster and better.

PS: Every week I send an email with tools, prompts, and automations designed to turn AI into ROI in your business.

Sign up for the (free) weekly email here.

They've tried ChatGPT. They've seen the demos on X. They've downloaded three apps they never opened. But when they sit down at their desk Monday morning, nothing has changed. The inbox is still a mess. The calendar is still packed. The same manual processes eat the same hours every week.

What they DON'T need is another tool recommendation.

They need someone to sit down, look at their actual workflows, and tell them exactly where to start.

That's the business. And five months into running this model, it's making me about $1,000/hour.

Here's the full playbook so you can build the same thing.

PS: If you want the exact template I use to deliver the audit report you can grab it (free) at audittemplate.ai.

Why This Business Exists Right Now

There's a translation gap in the market that's getting wider, not smaller.

On one side: AI tools that are genuinely powerful. Claude can analyze a full meeting transcript and pull out action items in seconds. Fathom records and summarizes every call automatically. Zapier connects 6,000+ apps without code. These tools work.

On the other side: business owners who know they should be using AI but have no idea which of the 500 tools they've heard about actually solves their specific problem.

The people who learn to bridge that gap are going to stay busy. The tools will eventually get simpler. Everyone will eventually figure it out. But right now, the window is wide open and the demand is real.

The Offer: What You're Actually Selling

I charge $999 for what I call an AI Tools Assessment.

Here's what the client gets:

• A 45-minute recorded discovery interview
• A custom report with 3-7 specific tool recommendations
• A 30-minute walkthrough call to review the findings
• A 4-day quick-start implementation plan

The guarantee: 5+ hours returned per week, or full refund.

That sounds aggressive until you realize how much time most business owners waste on tasks AI solved two years ago. The average client I've worked with sees 6+ hours per week freed up, using tools that cost about $40/month combined.

That's a 25x monthly return on a one-time $999 investment. It practically sells itself once you frame it that way.

The Four-Phase Framework

Phase 1: The Discovery Call (45 Minutes)

This is where the entire assessment lives or dies. You're not pitching solutions. You're pulling problems.

The questions that surface the best opportunities:

1. Walk me through yesterday. Where did your time actually go?
2. What tasks do you dread but can't hand off to anyone?
3. Where does work pile up waiting on you specifically?
4. What have you tried to automate but couldn't figure out?
5. What's the one thing that, if it ran itself, would change your week?

The goal is hearing their pain in their own words. Not what they think they should automate. What's actually eating their time.

Record everything. I use Fathom for automatic transcription. That transcript is the raw material for everything that comes next.

Pro tip: Most people underestimate how much time they lose to context-switching. When they say "I check email throughout the day," that's usually 45-60 minutes of lost productivity they've never quantified. Ask follow-ups that surface the hidden time drains.

Phase 2: The AI-Powered Analysis

After the call, take the full transcript and feed it into Claude with this prompt:

Analyze this business owner interview transcript. Identify 5-7 specific
areas where existing AI tools could save significant time each week.

For each opportunity:
- Name the specific tool(s) you'd recommend
- Estimate weekly hours saved
- Rate implementation difficulty (easy/medium/hard)
- Describe what the workflow looks like before and after

Focus on tools that are production-ready today, not experimental.
Prioritize opportunities with the highest time savings and lowest
implementation friction.

Transcript:
[PASTE FULL TRANSCRIPT]

Claude catches patterns you'll miss. It connects dots across different parts of the conversation, things the client mentioned in passing that actually represent their biggest time sinks.

Review the output. Add recommendations from your own experience. Cut anything that feels like a stretch. If you can't confidently explain why a tool solves their problem, remove it from the list.

Phase 3: The Report

Build the final deliverable in Gamma (or your preferred presentation tool). Clean. Professional. Looks like something a $10,000 consulting firm would produce.

You can grab the exact template I use to create these reports (free) here.

Report structure:

1. Executive summary (top 3 opportunities, total estimated hours saved)
2. Priority matrix (high impact + low effort = start here)
3. Tool recommendations with specific implementation steps
4. 4-day quick-start plan (what to set up on Day 1, Day 2, etc.)
5. Financial impact breakdown (hours saved × hourly rate = annual value)
6. Recommended next steps

This takes about 30 minutes to assemble from a template once you've done it a few times. Clients consistently think it took days.

Key insight: The priority matrix is the most valuable page in the report. Business owners are overwhelmed by options. When you show them a 2x2 grid that says "start here, do this second, save this for later," you've given them something no YouTube video or AI newsletter ever has: a clear sequence of actions tailored to their business.

Phase 4: The Review Call (30 Minutes)

Screen-share the report. Walk through each recommendation. Answer questions. Let them absorb it.

Then ask three questions:

1. Which of these feels most urgent to you?
2. Do you want to implement these yourself, or would you like help?
3. What's your realistic timeline for getting started?

About 60% of my assessment clients want help with implementation. That's where the real revenue lives, and it happens naturally because you've already demonstrated expertise and built trust.

The Expansion Menu (Where the Real Money Is)

The $999 assessment is the door. Here's what's behind it:

Process Redesign ($3,000-5,000) Almost every assessment uncovers at least one broken workflow. Map the current state, design the future state, remove the waste. This is consulting 101 with an AI lens.

Automation Builds ($1,000-3,000) Sometimes they just need Zapier or Make.com workflows connecting their existing tools. Build it, train them on how it works, hand it off.

Knowledge Systems ($3,000+) One client had 1,000+ hours of training videos that nobody on their team ever watched. We built a custom GPT their team can query instead of interrupting the owner. Saves 3+ hours weekly in "quick question" interruptions alone.

Custom Skill Libraries ($3,000-5,000) For clients already using AI tools, build custom Skills and prompts that turn manual processes into one-click operations. Think: proposal generators, email response frameworks, client onboarding sequences.

Full Implementation ($5,000-10,000 + monthly retainer) Custom agents running entire workflows. This only works for tech-savvy clients or those willing to bring someone in to maintain it. But when it works, it's the highest-value engagement you can offer.

How To Get Started

You don't need to be an AI engineer. You need three things:

1. Know the tool landscape

Spend 30 days getting hands-on with the core stack: Claude, ChatGPT, Fathom, Gamma, Zapier, Make.com. Use them daily in your own work. Understand what each one is good at and where they break down.

You don't need to master every AI tool. You need to master the 6-8 tools that solve 80% of small business problems.

2. Practice the process

Run 3-5 free assessments. Friends. Family. Local business owners you know. Every free assessment sharpens your interview questions, improves your report template, and gives you a testimonial.

By the time you charge your first client, you've already done the work five times. You know what you're doing.

3. Start charging and raise your price

$199 for your first paid client. Then $499. Then $999. Raise your price every few clients until you start hearing "no" more than "yes." That's your ceiling (for now).

4. Build the backend

Create your upsell menu. Process templates. Automation packages. Implementation tiers. The assessment is your entry point. Implementation is where you scale revenue per client from $999 to $5,000-10,000+.

The Numbers That Matter

Here's why this model works financially:

• 4 assessments per month = $3,996/month from assessments alone
• 60% conversion to implementation = 2-3 implementation projects
• Average implementation deal: $3,000-5,000
• Monthly revenue potential: $10,000-15,000 working 15-20 hours/week on client work

You're selling clarity, not automation. Business owners will pay a premium for someone who can look at their specific situation and give them a specific answer. That's worth far more than another generic "10 AI Tools for Entrepreneurs" listicle.

Common Mistakes to Avoid

Don't recommend tools you haven't researched or tested. If you can't demo it live on the review call, don't include it in the report.

Don't overwhelm them with options. 3-5 recommendations is the sweet spot. More than 7 and they'll implement none of them.

Don't skip the discovery call. Sending a questionnaire instead of doing a live interview cuts your assessment quality in half. The best insights come from follow-up questions you can't script.

Don't undercharge because you feel like an imposter. If your recommendation saves them 6 hours/week and their time is worth $100/hour, you've generated $31,200 in annual value. $999 is a steal. Charge accordingly.

Key Takeaways

• The AI consulting opportunity is translating technology for people who don't have time to figure it out themselves.
• A $999 assessment typically returns 6+ hours/week for the client at $40/month in tool costs
• The 4-phase framework: discovery call → AI analysis → polished report → review call
• 60% of assessment clients want implementation help at $3,000-10,000+
• All you need tool fluency and the ability to ask good questions.
• The translation gap between "AI can do this" and "here's how to set it up for your business" is wide open. It won't last forever. The window is now.

PS: If you want the exact template I use to deliver the audit report you can grab it (free) at audittemplate.ai

Sam Altman put it bluntly on launch day: "We love you and we want you to win." After a rough year of backlash against previous 5.x releases, those words mattered. The community noticed.

Immediately, the model cleared every credibility hurdle previous GPT-5 versions faced.

Here's everything you need to know about GPT-5.5, what it actually does, how to use it, and whether it's worth upgrading.

(8 copy-paste prompts and templates included)

PS: If you enjoy content like this, you'll love my weekly newsletter.

Every week I give you tools, prompts, and automations you can deploy the same day to turn AI into ROI in your business.

Subscribe (free) here.

What Is GPT-5.5 (And Who Can Actually Use It)

GPT-5.5 is OpenAI's fully retrained base model. Unlike incremental updates, this is a ground-up rebuild released just six weeks after GPT-5.4. The model shipped on April 23, 2026, in three flavors:

Standard GPT-5.5: Available to Plus ($20/month) and Pro subscribers. Balances speed and intelligence.

GPT-5.5 Thinking: Plus subscribers get this with a new reasoning mode that lets you buy deeper analysis in exchange for thinking tokens (max 3,000 messages per week).

GPT-5.5 Pro: Pro subscribers ($100 or $200/month tiers) get access to the most capable version, with extended reasoning available.

The free tier doesn't include 5.5 yet. If you're not paying, you're on older models.

What matters for business owners: the model processes text, images, audio, and video in one unified system. It has a 1 million token context window, meaning you can dump 400+ pages of documents at it without hitting limits.

The API is coming very soon but wasn't available on launch day. So if your workflow depends on integrations (Zapier, Make.com, custom tools), you're still on GPT-5.4 for now.

The Core Shift: Less Prompting, More Execution

Here's what makes 5.5 fundamentally different.

Old ChatGPT required micromanagement. You'd say "write an email," and if you wanted it structured a specific way, you'd need to specify every detail. Subject line format, tone, length, whether to include a PS, which CTA to use. The more precise your instructions, the better the output.

GPT-5.5 inverts this. OpenAI President Greg Brockman explained it on launch day: "What is really special about this model is how much more it can do with less guidance. It can look at an unclear problem and figure out just what needs to happen next."

That shift from AI-as-answer-machine to AI-as-execution-engine is the real story here.

Here's what that looks like in practice:

You say: "Build me a quarterly financial forecast."

Old Claude or GPT-4: Asks follow-up questions. Wants templates. Needs your revenue assumptions typed out explicitly.

GPT-5.5: Asks clarifying questions internally. Builds the spreadsheet. Tests the math. Flags inconsistencies in your inputs. Delivers the model ready to use.

You say: "Analyze these six months of speaking requests and build a scoring framework."

Old models: Summarizes what you gave them.

GPT-5.5: Reads the data, identifies patterns you didn't notice, creates a defensible framework, and explains the reasoning.

According to TechCrunch's launch coverage, GPT-5.5 excels at "writing and debugging code, researching online, analyzing data, creating documents and spreadsheets, operating software, and moving across tools until a task is finished." That last part (moving across tools until done) is the differentiator.

On the cost side: yes, the API pricing is 2x GPT-5.4 ($5/$30 per million tokens vs $2.50/$15). But because 5.5 uses significantly fewer tokens per completed task, your real cost per job is comparable or cheaper.

That's not marketing. Reddit's own pricing analysts confirmed this on launch day. Per-token pricing isn't the same as per-task cost.

Seven Use Cases Business Owners Actually Care About

1. Spreadsheets & Financial Modeling

This is the killer app for operators.

GPT-5.5 can build multi-tab spreadsheet models from messy inputs. Revenue forecasts, cash-flow projections, scenario planning, KPI dashboards - the model handles all of it and does the math verification itself.

Bank of New York tested it on financial analysis and specifically called out "really impressive hallucination resistance." Translation: it doesn't invent numbers. For finance teams, that's everything.

Copy-paste prompt:

Build a 12-month cash-flow forecast model with these inputs: [paste 
your revenue channels, operating costs, headcount].

Include optimistic/base/pessimistic scenarios.

Verify your own math and flag anything where my inputs seem 
inconsistent.

2. Transcript-to-Action-Items (Sales Calls, Meetings)

One of the most underrated uses.

After a client call or board meeting, you paste the transcript. GPT-5.5 produces: a 5-bullet summary of decisions, action items in a table (who, what, due date), open questions, and a draft follow-up email.

The model doesn't invent details. It sticks to what was actually said.

Copy-paste prompt:

I'm pasting a meeting transcript. Produce:
(1) 5-bullet summary of what was decided
(2) action items as a table
(3) open questions not resolved
(4) draft follow-up email
Tone: professional but warm. Do not invent details.
Transcript: [PASTE TRANSCRIPT]

3. Deep Research Reports

GPT-5.5 can now operate the web autonomously.

It decomposes your question into sub-tasks, visits multiple sources, synthesizes findings, and produces a structured report with inline citations. You can ask for a 2,000-word report on a competitor, market trend, or strategic question, and it comes back sourced and organized.

The model flags weak evidence instead of padding with speculation, a massive improvement over older versions.

4. No-Code Tool Building (For Non-Technical Founders)

This is where the productivity multiplier really kicks in.

On launch day, developer @chetaslua used GPT-5.5 with Codex to build a paper-physics website with wind effects in a single prompt. Zero lines of code written by hand. That post hit 4,000 likes on X, and it's the most concrete capability demo of the launch.

For business owners, this means: internal tools, landing pages, customer dashboards, data filters. No engineer required.

Ethan Mollick (Wharton professor and AI researcher) tested GPT-5.5 Pro on a 3D harbor-town evolution simulation. GPT-5.4 Pro took 33 minutes. GPT-5.5 Pro: 20 minutes. Faster and cleaner code.

5. Marketing & Content Across Channels

The model writes better than any OpenAI model since GPT-4.5, according to Dan Shipper's analysis at Every.

One brief, multiple outputs: blog post, social thread, email sequence, ad copy variants. You can generate 20 subject-line options in minutes for A/B testing.

Copy-paste prompt:

Write a 3-part email campaign promoting [PRODUCT] to [AUDIENCE].
Part 1: build curiosity
Part 2: present offer + address objections
Part 3: create urgency with limited-time CTA

6. Hiring & Team Building

Build job descriptions, interview loops, and scoring rubrics in one go.

Prompt template:

I'm hiring a [ROLE]. Produce:
(1) Job description under 400 words with must-haves and success metrics
(2) 4-stage interview loop with what each stage tests for
(3) Scoring rubric with 5 dimensions and 1-5 scale anchors
Context: [paste company info and why you're hiring]

7. Business Model Optimization

Ask it to analyze your business and produce recommendations on new revenue streams, cost reduction, operational efficiency, and pricing.

The model breaks assumptions, identifies gaps, and delivers specific tactics with estimated impact.

Prompts That Actually Work

Here's the universal template that gets the best results from 5.5:

You are [ROLE].
Your task is [TASK].
Context: [What this is and why it matters].
Constraints: [Length, tone, rules, exclusions].
Output format: [Bullets, table, steps, etc.].
Stop once [CONDITION].

Three prompts worth copy-pasting right now:

The Perfection Loop (OpenAI-recommended)

Before answering my request, do three things:
(1) Write your own definition of a world-class response—what would
an expert in [DOMAIN] consider a 10/10 answer?
(2) Draft your response.
(3) Grade your draft against your definition. If it scores below 10/10,
revise and grade again. Repeat until you hit 10/10, then give me only
the final version.
My request: [YOUR REQUEST]

The Business Model Analysis

You are a management consultant. Analyze my business and produce:
- New revenue stream opportunities
- Cost reduction strategies
- Operational efficiency improvements with implementation steps
- Pricing optimization recommendations
Business details: [paste industry, size, current model, products, costs,
revenue channels]
Format: 1-paragraph executive summary + each area with tactics and
estimated impact.

The Website Builder

Create a website for [BUSINESS NAME], a [ONE-SENTENCE DESCRIPTION].
Key features: [LIST 3-5].
Target audience: [AUDIENCE].
Include: hero section with clear CTA, features section, pricing, FAQ,
and footer.
Make it professional, modern, and trustworthy.

What Experts Found When They Actually Used It

Ethan Mollick (Wharton, One Useful Thing): "The jagged frontier continues." It's excellent at some things, surprisingly weak at others. Test on YOUR specific use case.

His workflow: use Codex+GPT-5.5 to do research, form hypothesis, test, and draft papers, then have 5.5 Pro critique the draft.

Dan Shipper (Every, "Vibe Check"): "How few tradeoffs it asks you to make." Most model upgrades come with qualifiers. This one doesn't. Faster than Claude Opus 4.7. Easier to iterate with. Shorter responses, bias toward small workable changes vs. broad rewrites.

Harvey (Legal AI): Improved accuracy, stronger organizational structure, consistent formatting. Particular gains in risk assessment and deal management.

@MatthewBerman (AI YouTuber, 2-week early access): "GPT-5.5 is now the bar. It is the frontier."

Common Mistakes That Cost You

Contradictory instructions hurt MORE now, not less. Because 5.5 tries harder to follow every instruction, it burns reasoning tokens reconciling contradictions. Fix: read your prompt once before sending. Delete redundant or opposite statements.

Over-specifying simple tasks wastes tokens. Match prompt complexity to task complexity.

Vague + contradictory = disaster. The model's "figure it out" capability breaks when instructions point opposite directions.

PowerPoint presentations: 5.5 still trails Opus 4.7 here. For polished slide decks, stick with Claude for now.

API access: Not available at launch. If your workflow depends on integrations, you're still on 5.4.

Should You Upgrade?

ChatGPT Plus ($20/month) gets you GPT-5.5 Thinking with 3,000 messages/week. For most SMBs, this is the starting point.

ChatGPT Pro ($100/month) is worth it if you're doing serious output generation (research reports, large codebases, complex financial modeling). The Pro version is measurably better at open-ended work.

If you're integrating AI into tools (Zapier, Make.com, custom workflows), wait for the API in early May.

The pricing perception was wrong. Yes, API costs are 2x. But per-task cost is comparable to GPT-5.4 because the model is more token-efficient. You're getting more work done per dollar spent, not less.

Bottom Line

If you've been on Plus or Pro already, upgrade is automatic. You get 5.5 by default. If you're on free tier, jumping to Plus makes sense now. The model just crossed a threshold where AI moves from "helpful writing assistant" to "operational execution engine."

The model code name was "Spud" internally. Sam Altman said "I personally like it." Those aren't random details. They signal that OpenAI actually believes in this release, unlike the prior 5.x versions that got panned.

Try one of the prompts above this week. You'll understand why the community reaction is so different this time.

PS: If you enjoy content like this, you'll love my weekly newsletter.

Every week I give you tools, prompts, and automations you can deploy the same day to turn AI into ROI in your business.

Subscribe (free) here.

Sources & Attribution

OpenAI official release + press materials Ethan Mollick, One Useful Thing (Wharton) Dan Shipper, Every / Vibe Check Harvey AI legal testing Bank of New York early access feedback @MatthewBerman (AI YouTuber, early access) TechCrunch, CNBC, Fortune, VentureBeat launch coverage Reddit community validation (r/singularity, r/OpenAI, r/ChatGPT) X posts with verified engagement metrics.

Even worse, the feedback loop is slow. Days or weeks of revisions before you get what you actually need.

Claude Design changes that. It's a conversational design tool powered by Claude Opus 4.7 that lets you describe what you want, and it builds a first draft in seconds.

Then you refine it through conversation, inline edits, or direct tweaks until it's exactly right. No design experience required.

Here's the full Claude Design Masterclass designed to take you from beginner to expert in 30 minutes or less.

Table of Contents

1. What Claude Design Is
2. How It Works (The Workflow)
3. How To Iterate
4. The Business Case
5. How To Use It (Step-by-Step)
6. Real Use Cases
7. The Limitations
8. When To Use Vs. Alternatives
9. What This Means For Your Business
10. Getting Started (5-Min Setup)
11. Bottom Line

What Claude Design Actually Is

Claude Design isn't Canva. It's not Figma. It's not a "design template generator."

It's a collaborative design partner that works in your browser. You bring the idea. Claude brings the visual execution.

Here's what separates it from everything else:

It reads your brand. When you set it up, Claude imports your codebase and design files. Every project you create automatically inherits your brand colors, typography, and component patterns. Your fonts. Your palette. Your style. No manual configuration needed.

It understands context. You can upload reference images, documents, even grab elements directly from your website using the web capture tool. Claude uses all of it to build designs that feel like they belong to your business.

It exports anywhere. Finished design? Export as PDF, PPTX (PowerPoint), standalone HTML, or send directly to Canva for further refinement. Or share an internal link with your team for collaboration and feedback.

It hands off to code. When your design is ready to build, Claude packages everything into a "handoff bundle" that you can pass to Claude Code with a single instruction. Designer → Developer. No translation needed.

Real use cases from the first month:

• Landing pages: Founder describes the value prop and target audience. Claude builds a complete, branded landing page. Refine the copy, adjust colors, done in 15 minutes.
• Pitch decks: Go from a rough outline to a polished, on-brand presentation deck in 20 minutes. Export as PPTX and present today.
• Product prototypes: Wireframe your feature flow as an interactive prototype. Share with users for feedback before a single line of code is written.
• Social media graphics: Create campaign visuals, LinkedIn post graphics, or promotional banners without Canva subscriptions or Photoshop skills.
• One-pagers: Sales sheets, case studies, event materials—all built and branded in one conversation.
• Interactive prototypes: Voice, video, shaders, 3D, built-in AI. Things that used to require a full engineering team.

How Claude Design Works (The Workflow)

The Two-Panel Interface

Left side: Chat with Claude. Right side: Your design canvas.

You describe what you need. Claude generates. You iterate. Repeat until it's perfect.

Step 1: Create a Project (Automatic Brand Integration)

When you create a new project, it automatically pulls your organization's design system. You don't upload anything. You don't configure anything. Your brand is already there.

This is the big difference between Claude Design and every other tool. Most design apps make you recreate your brand colors and fonts every single project. Claude reads your existing brand once and applies it everywhere.

Step 2: Give Claude Context (The More Context, The Better Output)

Before you ask Claude to build anything, give it what it needs to succeed:

• Reference images: "Here's what our competitors are doing"
• Design docs: Upload your brand guidelines (PDFs, images, anything)
• Content: Paste the copy, headlines, call-to-action text
• Audience: "This is for 30-year-old SaaS founders"
• Constraints: "Make it minimal and clean, not playful"

The more specific you are, the fewer revisions you'll need. Vague requests = multiple back-and-forths.

Step 3: Write Your Prompt (You Don't Need Design Language)

You don't say "use a Helvetica sans-serif with 16px letter spacing on a #2D3748 background."

You say: "Create a landing page for a productivity tool. The audience is busy executives who value simplicity. Make it clean, professional, and minimalist. Include a hero section with a headline, subheadline, and a CTA button. Below that, three feature cards with icons."

Claude handles the design decisions. Your job is to describe the goal, layout, content, and audience.

Examples of prompts that work:

• "Design a pricing page for a $99/month SaaS tool. Show three tiers: Starter, Professional, Enterprise. Make it clear which one is the recommended choice."
• "Create an interactive prototype for a fitness app. The first screen should show today's workout. The second screen shows workout history. Make it feel motivating, not clinical."
• "Build a one-pager that explains our customer success story. Include the before/after metrics, their quote, and our logo. Make it look like a high-end case study."

How to Iterate (The Real Magic)

The first draft is just the starting point. The value comes from refining.

Chat-Based Iterations (For Big Changes)

Use chat for structural changes, aesthetic shifts, or anything that requires explanation:

• "Make this darker and more premium. Add more white space between sections."
• "This landing page feels too busy. Simplify it—remove the testimonials section and focus on the three main features."
• "Can you add a dark mode toggle in the top right?"
• "Make the CTA button bigger and change it to orange."

Claude makes the change and shows you the result immediately on the canvas.

Inline Comments (For Targeted Tweaks)

Click directly on a specific element and request a targeted change. Faster than describing it in chat:

• Click the button → "Make this taller"
• Click the heading → "Reduce the font size by 2px"
• Click a section → "Add more padding on the left"

Use comments for component-level changes. Use chat for structural changes.

Direct Editing (You Control The Details)

You can edit text directly on the canvas. Change a headline. Adjust copy. Fix a typo. Claude responds to your edits and maintains design consistency.

Adjustment Knobs (Claude Makes Sliders For You)

Claude can create custom adjustment sliders for specific tweaks:

• Spacing adjuster (tight → loose layout)
• Color intensity (subtle → bold palette)
• Typography weight (light → heavy)
• Component size (compact → spacious)

Slide, preview, done.

The Business Case: What It Actually Costs vs. What You Save

Subscription cost: Claude Design is included with Claude Pro ($20/month) or Max ($200/month). If you're already using Claude for other work, it costs nothing extra.

Time saved per project:

• Landing page: 2-3 hours → 15-20 minutes (saves 2.5 hours)
• Pitch deck: 4-5 hours → 20-30 minutes (saves 4 hours)
• Social graphics (batch of 5): 1.5 hours → 10 minutes per set (saves 1.5 hours per batch)
• Product prototype: 8+ hours → 30-45 minutes (saves 7+ hours)

Money saved per project:

• Landing page design: $1,500-3,000 → included in subscription
• Pitch deck design: $1,000-2,500 → included in subscription
• Prototype design: $2,000-5,000 → included in subscription

Monthly ROI for a solopreneur:

If you create 4 landing pages, 2 pitch decks, and 8 social media campaigns per month:

Traditional cost: $8,000-15,000 in design work (or your own 60+ hours of labor)

Claude Design cost: $20/month + your 5-6 hours of iteration time

You're not replacing a full-time designer. You're eliminating the friction of getting designs made at all.

How to Actually Use Claude Design (Step-by-Step)

Access It

Go to claude.ai/design. If you have Claude Pro or Max, you'll see it in the navigation.

Create Your First Project

1. Click "New Project"
2. Name it (e.g., "Landing Page - Q2 2026")
3. Your brand system is automatically loaded
4. Start typing in the chat

Effective Prompt Formula

Goal: What are you building? (landing page, pitch deck, social graphic)

Layout: How should it be organized? (hero section + features, three columns, etc.)

Content: What information goes in it? (headlines, body copy, CTAs)

Audience: Who's this for? (startup founders, HR professionals, fitness enthusiasts)

Tone: What feeling should it convey? (professional, playful, minimalist, luxury)

Example: "Create a landing page for Return My Time, an AI automation agency for solopreneurs. The audience is busy business owners who are tired of manual work. Hero section with headline 'Reclaim 10 hours per week' and subheadline 'AI workflows built for non-technical entrepreneurs.' Include three feature sections highlighting: 1) Done-for-you setup, 2) Results in 7 days, 3) ROI-focused strategy. Make it feel premium but approachable, not corporate, not playful. Use our brand colors (deep blue and white). Add a CTA at the bottom: 'Start Your AI Audit'."

Iterate Using the Workflow

1. First draft appears: Review it
2. If it's close: Use inline comments to tweak specific elements
3. If the direction is wrong: Use chat to explain the change
4. Once it's right: Click Export and choose your format

Export Options

• Internal URL: Share within your organization. Others can view, comment, or edit.
• PDF: Static document. Good for printing or offline sharing.
• PPTX: PowerPoint file. Add more slides, present immediately.
• HTML: Standalone webpage. Share with anyone, works in any browser.
• Canva: Export to Canva for further refinement and publishing.
• Handoff bundle: Send to Claude Code to build the final product.

Real Use Cases: What Operators Are Actually Using Claude Design For

Sales & Pitch Decks

Before: 4-5 hours building in PowerPoint, still looks generic

After: 20 minutes in Claude Design, looks polished and branded

One founder: "I used to spend a full workday creating pitch decks. Now I spend 20 minutes and the investor feedback is 'this looks really professional.' The time difference alone is worth it."

Landing Pages

Before: $2,000-3,000 to hire a designer, 1-2 weeks for design + revisions

After: 15-20 minutes in Claude Design, export to HTML or Canva

Real example: A SaaS founder needed a landing page for a new product line. Normally she'd commission a designer ($3K) and wait 10 days. Instead, she spent 15 minutes in Claude Design and had a production-ready page same day.

Social Media Graphics

Before: Using Canva templates, spending 15-20 minutes per graphic

After: Batch create 5 graphics in 10 minutes total, export all at once

One content creator: "I used to spend an hour creating social graphics. Now I spend 10 minutes total, and they look way more branded and professional."

Product Prototypes & Mockups

Before: Hire a designer or UX person ($2K-5K) or build it yourself in Figma (4-8 hours)

After: Describe the flow, Claude builds an interactive prototype in 30 minutes

One product founder: "I used to spend a week building prototypes in Figma before user testing. Now I can have a working prototype in an hour, get feedback immediately, and iterate. The speed is incredible."

Client Proposals & One-Pagers

Before: Manually design in PowerPoint or Canva, 1-2 hours per proposal

After: Describe the proposal structure, Claude builds it, 10 minutes

One agency owner: "Every client proposal used to be a manual design job. Now I describe what I need and Claude builds it. I'm sending better-looking proposals in a fraction of the time."

The Limitations (What Claude Design Can't Do Yet)

Claude Design is in research preview, so there are some real constraints to know:

1. Complex animations and interactions: You can build interactive prototypes, but sophisticated animations (complex transitions, scroll effects, etc.) aren't supported yet.

2. 3D rendering: Limited support for complex 3D designs. Basic 3D shapes work, but intricate models don't.

3. Live data integration: You can't plug in live data sources (dynamic pricing, real-time inventory, etc.). It's static designs, not data-driven dashboards.

4. Pixel-perfect precision: If you need absolute control over every single pixel, Figma or design software is still better. Claude Design is about 95% there, but not 100%.

5. Context limits on very long projects: Extremely complex, multi-section designs with heavy reference material might need to be split into separate projects.

6. Collaborative real-time editing: Multiple people can comment and edit, but it's not simultaneous. It's turn-based, not Google Docs-style live collaboration.

The good news: These limitations don't matter for 90% of what solopreneurs and small business owners actually need.

When to Use Claude Design vs. Alternatives

Claude Design is best for:

• Quick turnaround (you need something today, not in 2 weeks)
• Budget-constrained (you can't afford $3K designer)
• Iterative work (you need 3-5 versions before you're happy)
• Branded consistency (you want everything to match your existing style)
• Non-specialists (you don't have design skills and aren't learning Figma)

Use Figma if you need:

• Pixel-perfect design control
• Complex, multi-page design systems
• Team of designers collaborating on production work
• Professional handoff to developers

Use Canva if you need:

• Simple templates to customize quickly
• Social media graphics (it's optimized for this)
• No learning curve at all

Hire a designer if you need:

• Brand identity creation (logo, complete visual system)
• Annual rebrand
• Production-quality work for your core product
• Creative direction and strategy

The Money Conversation (What This Means for Your Business)

Let's be direct: Claude Design isn't replacing a full-time designer. It's replacing the friction of getting designs made.

For solopreneurs and small businesses, that friction is enormous:

• You have an idea but you can't afford a $3K designer
• So you try Canva and spend 45 minutes on a template
• The result is "okay" but doesn't feel like your brand
• You move on because it's "good enough"

Claude Design removes that friction. Now:

• You have an idea
• You spend 15 minutes in Claude Design
• You get something that actually looks professional
• You export and use it
• You move on

The second version is 10x better with the same amount of effort.

That's the real value. Not replacing high-end design work. Enabling design work that wouldn't have happened otherwise.

Getting Started (The 5-Minute Setup)

1. Get Claude Pro or Max ($20/month or $200/month)

2. Go to claude.ai/design

3. Create a new project

4. Upload your brand files:

• Logo (PNG or SVG)
• Brand guidelines (PDF or image)
• Website (Claude can crawl it)

5. Build your first thing:

• Pitch deck
• Landing page
• Social graphic
• Anything visual

Total setup time: 5 minutes

Bottom Line

Claude Design is the designer you hire when you can't afford (or don't want to wait for) a traditional designer.

It's not magic. It won't replace senior designers doing complex brand work. But for the 80% of visual work that solopreneurs and small business owners actually need (landing pages, pitch decks, social graphics, prototypes) it's genuinely transformative.

You go from "I need a design but I can't justify spending $3K" to "I can have something polished in 15 minutes."

That's the difference between execution and stagnation.

Your move: Describe what you need. See what Claude builds. Iterate until it's right.

Get started: https://claude.ai/design

I've used it to automate real work, connect to my tools, generate reports, do research, and so much more.

The single biggest unlock for me (and for you) is learning how to set Computer up properly from the start and knowing what exactly it's capable of.

This is a full course on everything you need to know about Perplexity Computer, clearly explained so that the everyday non-technical person can get maximum value.

Table of Contents

1. What Computer Is (And Why It Matters)
2. How To Set It Up
3. Multi Model Orchestration (The Secret Weapon)
4. The Connectors
5. The Credit System
6. Underrated Features Most People Miss
7. What Computer Is Great At (And What To Avoid)
8. Computer vs. Claude Cowork vs. OpenClaw
9. Real Setup Walkthrough (Step by Step)
10. The Bottom Line + Action Items

Section 1: What Perplexity Computer Actually Is (And Why It Matters)

Computer is a digital worker that lives inside your Perplexity account. You describe what you want done. It breaks that into subtasks, automatically assigns each one to the best AI model for the job, and works autonomously until it's finished.

This isn't a chatbot. It's an agent. The difference is massive:

Chatbots wait for you to give them input. They answer one question at a time. They don't remember context across sessions. They can't connect to your tools.

Agents take a goal, plan the steps needed to reach it, execute those steps across multiple tools and systems, and report back when they're done. They remember everything you told them. They work while you sleep.

Computer runs inside an isolated cloud sandbox with tools pre-installed. Nothing touches your local machine. If something breaks, the failure stays contained. You're not exposing your business to risk.

Real use cases from the first week:

- Market research: Research 5 competitors in your industry, create a comparison table with pricing/features/target audience, and deliver it as a Google Sheet

- Content creation: Analyze your top 10 performing tweets, extract the patterns, and generate 20 new tweet ideas in your exact voice

- Data analysis: Connect your Snowflake database, run analysis on Q1 performance, and email the report to your team

- App building: Build a full web app from a PRD (product requirements doc), test it, and deploy it to production

- Workflow automation: Every Monday morning, pull your calendar, summarize your key meetings, and draft prep notes for each one—automatically

That last one alone is worth the subscription. Set it once, it runs forever.

Section 2: How to Set It Up (First Time User)

Step 1: Get the right subscription

Computer requires Perplexity Pro ($20/month) or Perplexity Max ($200/month) (or $17/mo for Pro, $167/mo for Max when billed annually). Pro will get you started and teach you how to use it. Max is for heavy users or teams.

Start with Pro. You'll get monthly credits. When you understand how it works, you can upgrade if you need more capacity.

Step 2: Access Computer

Once subscribed, you'll see "Computer" in the Perplexity interface. Click it. That's your command center. The interface is simple: a text input field for your task, a view of what Computer is currently working on, and a history of completed tasks.

Step 3: Connect your tools (This is where the power unlocks)

In the Computer panel, go to Connectors. This is where it gets powerful. You'll see 400+ apps available through managed OAuth connections. Each connector takes about 30 seconds to set up. Click Enable, authorize with OAuth, done.

Start with these core connectors:

- Gmail / Google Calendar — Read and send emails, manage your schedule

- Slack — Pull conversation history, send messages, respond to mentions

- Notion — Read and write to your workspace databases

- Google Drive — Access docs, sheets, slides

- GitHub — Manage repos, create issues, review PRs

Pro tip: Connect everything, even tools you don't think you'll need. Computer pulls context from connected tools automatically when working on tasks. More connections = better context = better outputs.

Step 4: Give it your first task

Start simple. Something like:

"Research the top 5 competitors in [your industry] and create a 
comparison table with pricing, features, target audience, and a one-
line summary of their unique value prop. Save it to Google Sheets."

Watch how Computer breaks the task down, assigns subtasks, and delivers results. That first task teaches you more about how Computer thinks than any documentation will.

Section 3: The Secret Weapon (Multi-Model Orchestration)

This is Perplexity's biggest differentiator and the thing most people don't fully appreciate.

Computer doesn't use one AI model. It uses 19, and it routes each subtask to whichever model is best suited for that specific job:

- Claude Opus 4.7 (Anthropic) — Core reasoning engine. Handles complex decision-making and coordination.

- GPT-5.2 (OpenAI) — Long-context recall and broad web search. Great for synthesizing large amounts of information.

- Gemini (Google) — Deep research tasks. When Computer needs to go deep on a topic, Gemini handles it.

- Grok (xAI) — Lightweight, speed-sensitive operations. Quick tasks that don't need heavy reasoning.

- Nano Banana (Google) — Image generation.

- Veo 3.1 — Video production.

You don't choose which model to use. Computer decides based on the subtask. This is why the outputs are often better than what you'd get from any single model.

Example: A coding task might use Claude for architecture decisions, GPT-5.2 for pulling documentation context, and Grok for quick syntax checks—all in the same workflow.

This is why Computer often produces better results than throwing everything at Claude or GPT. It's not trying to be good at everything. It's routing to specialists.

Section 4: The Connectors

Connectors are what separate Computer from every other AI tool. Without them, it's just a smart chatbot. With them, it's an AI that can actually do things inside your business.

Productivity:

- Gmail, Outlook, Google Calendar, Google Drive, OneDrive, Box

Communication:

- Slack (both the connector for searching Slack data AND the Slack app for running Computer inside Slack)

- Microsoft Teams

Project Management:

- Notion, Asana, Jira, Linear, Confluence

Sales & CRM:

- Salesforce, HubSpot

Developer:

- GitHub, Vercel

Data:

- Snowflake, PostgreSQL

Custom:

- You can add your own via MCP server URLs with OAuth, API key, or open authentication

Pro tip #1: The Slack integration is actually two things. The Connector for Slack lets Computer search your Slack data from inside Perplexity.

The Slack App lets you run Computer directly inside Slack by messaging it. Use both.

Pro tip #2: Connect everything, even tools you don't think you'll need. Computer pulls context from connected tools automatically when working on tasks. More connections = better context = better outputs.

Section 5: The Credit System

Credits are how Perplexity meters usage. Higher subscription plans give you more credits per month. Credits are consumed per task based on complexity.

Credit ranges:

- Simple tasks (alt text, quick lookups): ~30 credits

- Medium tasks (research reports, email drafts): ~100-500 credits

- Complex tasks (building apps, extended coding): 1,000-5,000+ credits

What happens when you run out: Tasks pause (not cancel). They resume when credits refill at your next billing cycle or when you buy more.

Default settings:

- Auto-refill is OFF (intentional to prevent surprise charges)

- Monthly spending cap is $200 by default (adjustable up to $2,000)

- Maximum possible monthly spend: $400 (subscription + one refill)

- Unused credits expire at the end of each billing cycle

The biggest credit-waster: Vague prompts. "Make this better" triggers broad compute cycles across multiple models. "Rewrite the headline to include a specific benefit and reduce to under 10 words" uses a fraction of the credits for a better result.

My favorite hack: PRD-first workflow. Don't go straight to Computer with a complex build. Draft a PRD (product requirements doc) in Claude first.

Define exactly what you want: features, layout, copy, functionality. Then hand that PRD to Computer. It follows specs precisely, wastes fewer credits, and the output is dramatically better than a vague prompt.

Section 6: Underrated Features Most People Miss

1. Spaces

Spaces are persistent project environments. Create a Space for each major project or client. Upload reference documents, set context, and every task you run inside that Space has access to all of it.

Think of it like giving Computer a project brief that it never forgets. Instead of re-explaining your brand voice, target market, and content guidelines with every task, you set them once in the Space, and Computer remembers.

Example Space setup:

- Space name: "Podcast Production Q2 2026"

- Documents: Brand voice guide, episode template, guest interview questions, clip editing guidelines

- Result: Every task you assign in this Space automatically knows your show format, target audience, and production standards

2. Scheduled Tasks

Computer can run tasks on a schedule. "Every Monday morning, pull my calendar for the week, summarize my key meetings, and draft prep notes for each one."

Set it once. It runs forever. This alone is worth the subscription for anyone with a packed calendar.

3. Subagent Spawning

When Computer hits a complex task, it spawns subagents to parallelize the work. A market research task might spawn one subagent for competitor pricing, another for feature comparison, and a third for customer reviews, all running simultaneously.

This is why complex tasks finish faster than you'd expect. You're not waiting for serial execution. You're getting parallel work from a team of agents.

4. Skill Playbooks

Computer loads from 50+ domain-specific skill playbooks automatically. When you ask it to build a website, it activates the web development playbook. Research task? Research playbook.

It's pre-loaded with best practices for common workflows so you don't have to prompt-engineer everything from scratch.

5. Email Attachments

Recent update: Computer can now attach files to emails it sends on your behalf. If it generates a report and you ask it to email it to someone, the report goes as an attachment.

Small feature, huge time saver.

6. Voice Mode

Summon Computer via a dedicated taskbar shortcut on Mac. Use voice to give commands. "Research competitors in the fitness app space and create a pricing comparison table." Computer listens, understands context (what app you're currently using), and gets to work.

Section 7: What Computer Is Great At (And What to Avoid)

Computer Excels At:

- Research: Market analysis, competitor breakdowns, trend reports. It searches the web in real time and synthesizes across sources.

- Content creation: Reports, presentations, email sequences, landing pages.

- Data analysis: Connect Snowflake or upload CSVs and ask questions about your data.

- App building: Full web applications, dashboards, internal tools. It writes, tests, and deploys.

- Workflow automation: Recurring tasks on a schedule across your connected tools.

- Multi-step projects: Anything that requires coordinating research, writing, analysis, and action across multiple tools.

What to Watch Out For:

- Connector reliability varies. Most work great. Some (like Vercel) have OAuth tokens that expire frequently. GitHub sometimes needs manual token workarounds.

Test your connectors with a simple task before relying on them for something important.

- No live preview for code. When Computer builds something, you can't see it running in real time. You have to push it to external hosting to check. This creates a slow feedback loop for development tasks.

- Credits burn fast on stuck tasks. If Computer hits a dependency error and keeps retrying, it'll burn through credits without telling you why.

Monitor complex builds in real time, especially the first time you try a new type of task.

- Context can't be manually managed. There's no way to branch, clear, or summarize context mid-conversation. Long sessions can drift. Start fresh when switching to a different type of task.

Section 8: Computer vs. Claude Cowork vs. OpenClaw

These three aren't competitors. They're different tools for different use cases:

Perplexity Computer:

• Best for non-technical users who want zero setup, multi-model orchestration, and 400+ connectors out of the box
• $20/month base
• Cloud-based, sandboxed, safe
• No local machine required
• Web/app interface only

Claude Cowork:

• Best for users who want a personal AI workspace with deep customization, skills, and scheduling
• $20-200/month depending on plan
• Desktop app, more hands-on customization
• Great for people who want to build custom integrations
• Requires some technical comfort

OpenClaw:

• Best for technical users who want full control and unlimited customization
• Free but requires a dedicated machine, terminal access, and API key configuration
• Most powerful but highest friction to set up
• Can run 24/7 as a true autonomous system
• Self-hosted, completely private

All three can get the job done. Your choice depends on your technical comfort level and how much setup you're willing to do.

My take: Start with Computer. If you find yourself wanting more customization or local control, migrate to Cowork or OpenClaw. But for most people, Computer is the best risk-adjusted entry point.

Section 9: Real Setup Walkthrough (Step-by-Step)

Time to complete: 15 minutes

1. Subscribe to Perplexity Pro ($20/month)

• Go to perplexity.ai/subscribe
• Choose Pro
• Add payment method
• Confirm

2. Access Computer

• Log into your Perplexity account
• Look for "Computer" in the main navigation
• Click it

3. Connect Gmail

• Go to Connectors
• Find Gmail
• Click Enable
• Click "Authorize with Google"
• Grant permission
• Done

4. Connect Slack (repeat same process as Gmail)

5. Connect Notion (repeat same process)

6. Give it a test task

• In the task input box, type: "What are the top 3 competitors to [your business] and what are their main pricing plans?"
• Press submit
• Watch it work

That's it. You now have an AI employee.

Section 10: The Bottom Line

Perplexity Computer is the easiest way to get an AI employee running in your business today. No code. No terminal. No configuration files.

Connect your tools. Describe what you need. Let it work.

The credit system means you need to be intentional with your prompts. Vague asks waste money. Specific, well-scoped tasks get the best results.

Start with one recurring workflow. Something you do every week that takes 2-3 hours. Automate it with Computer. Once you see the time savings, you'll find ten more things to hand off.

Action Items

This week:

1. Subscribe to Perplexity Pro

2. Connect 3-5 tools (Gmail, Slack, Notion minimum)

3. Run 3 test tasks (simple → medium → complex)

4. Identify one recurring workflow to automate

Next week:

1. Set up that recurring workflow on a schedule

2. Monitor it for 2-3 runs

3. Adjust based on what you learn

4. Identify the next workflow to automate

The result: 5-10 hours per week back in your calendar. That's a part-time employee's worth of work. And you're only spending $20/month for it.

Get started: https://www.perplexity.ai/subscribe

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I hope you found this free course valuable.

If you like this type of content then subscribe to my free newsletter where I post deep dive AI content every single week.

Plugins are what turn Claude from a conversational tool into a specialized agent that orchestrates entire workflows, pulls live data, executes parallel tasks, and hands you back finished work. One command. No manual steps. No friction.

This is the guide I wish existed when I started building with Claude. It covers everything: what plugins actually are, what they unlock, which ones to steal, how to build your own, and the exact prompts to use.

PS: Every week I break down workflows like this inside the Build With AI newsletter. Agents, automations, and step-by-step setups you can run yourself or sell to clients.

Join (free) here.

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What Plugins Actually Are (And Why You're Probably Using Them Wrong)

A plugin is not a single thing. It's a bundle. Think of it like a job description that packages four things together:

1. Skills - Instructions that teach Claude how to do specific tasks (write in your voice, analyze data, research competitors)

2. Connectors - Live links to external tools (Google Drive, Gmail, Slack, Notion, Asana, Salesforce, etc.)

3. Slash Commands - Quick actions you trigger with /command that run entire workflows

4. Sub-agents - Parallel workers that handle pieces of a task simultaneously

Instead of setting up each piece individually (connect tool → load skill → remember which prompt to use → repeat), you install one plugin and immediately have a ready-to-go setup.

Think of it this way:

• Skills are Lego blocks (individual capabilities)
• Plugins are the fully assembled set (complete system)

One command runs the entire agentic process. You get finished work, not intermediate output.

Skills vs Plugins: Where Most People Get Confused

This is the biggest point of confusion I see.

Skills are single-purpose instructions. One skill teaches Claude to write in your voice. Another teaches it to format a proposal. You can use skills without a plugin. They work anywhere.

Plugins bundle multiple skills together with the connectors and commands needed to run a complete workflow.

Example:

• Skill: "Write status update in my communication tone"
• Plugin: Pull active projects from Notion → Write status update for each client using your communication skill → Drop drafts into Gmail as individual messages → Send

One slash command runs all of it.

The 11 Official Anthropic Plugins (Free, Open Source)

Anthropic shipped these on January 30, 2026. All are free. All are open source. You can customize or fork any of them.

1. Sales Plugin

• Call prep with prospect research
• Competitive intelligence and positioning strategy
• Account planning and strategy
• Objection handling framework
• Commands: /sales:call-prep, /sales:account-plan, /sales:objection-handling

2. Marketing Plugin

• SEO audit analysis for any URL
• Multi-email sequence generation
• Competitive intelligence briefs
• Content brief creation
• Commands: /marketing:seo-audit, /marketing:email-sequence, /marketing:competitive-brief

3. Legal Plugin

• Contract review with risk flagging
• Regulatory compliance checking
• Clause analysis and recommendations
• Commands: /legal:contract-review, /legal:compliance-check

4. Finance Plugin

• Financial modeling and projections
• Budget analysis and forecasting
• Cash flow planning
• Commands: /finance:model, /finance:forecast

5. Customer Support Plugin

• Ticket response drafting
• Escalation workflow automation
• Knowledge base search
• Commands: /support:draft-response, /support:escalate

6. Product Management Plugin

• PRD (Product Requirement Document) generation
• Roadmap planning and prioritization
• User research synthesis
• Commands: /product:write-prd, /product:prioritize-roadmap

7. Data Analysis Plugin

• Dataset cleaning and standardization
• Dashboard creation
• Chart and visualization generation
• Commands: /data:clean-dataset, /data:visualize

8. Enterprise Search Plugin

• Cross-platform search across all company tools
• Unified search index
• Smart filtering and ranking
• Commands: /search:company-wide

9. Biology Research Plugin

• Literature review automation
• Experiment design assistance
• Research paper analysis
• Commands: /research:literature-review

10. Productivity Plugin

• Task management and organization
• Meeting prep automation
• Scheduling intelligence
• Commands: /productivity:prepare-meeting

11. Plugin Create Plugin

• Build your own plugins from scratch
• Template generation
• Command scaffolding
• Commands: /plugin:create-new

Browse all of them: github.com/anthropics/knowledge-work-plugins

How to Install a Plugin (3 Steps)

1. Open Claude Cowork
2. Click Plugins in the sidebar
3. Click "+" → Browse available plugins → Click "Add plugin"

Done. Plugins you add are saved locally to your machine.

To use a skill from your plugin, type "/" in the chat to see available commands, or click the "+" button and select from your installed plugins.

The Most Useful Slash Commands to Bookmark

Marketing Plugin Commands:

• /marketing:seo-audit — Analyzes any URL for SEO issues (keyword gaps, technical issues, competitive positioning)
• /marketing:email-sequence — Generates full multi-email campaigns (hooks, pain points, solution, CTA)
• /marketing:competitive-brief — Researches competitors and outputs structured competitive analysis

Sales Plugin Commands:

• /sales:call-prep — Researches a prospect and creates a call brief (background, company, recent news, talking points, objection handling)
• /sales:account-plan — Builds a strategic account plan (stakeholders, budget, decision process, objections)
• /sales:objection-handling — Generates responses to common objections

Legal Plugin Commands:

• /legal:contract-review — Analyzes a contract for risks (liability, termination, payment terms, IP issues)
• /legal:compliance-check — Checks agreements against regulatory requirements

Data Plugin Commands:

• /data:clean-dataset — Standardizes messy data (removes duplicates, fixes formatting, handles nulls)
• /data:visualize — Creates charts and dashboards from raw data

How Connectors Work (And Why They Matter)

Connectors link Claude to external tools. Without them, Claude can only work with what you paste in. With them, Claude can pull live data and act on it.

Available connectors include:

• Google Drive, Gmail, Google Calendar
• Slack, Notion, Asana
• Salesforce, HubSpot, Pipedrive
• DocuSign, Linear, Monday.com
• And dozens more

To connect:

1. Click Settings → Connectors
2. Browse connectors → Search for your tool
3. Click to connect (OAuth flow)

Once connected, Claude can search your Drive, read emails, check your calendar, and take actions based on live data.

Example: A plugin can now pull active projects from Notion, write status updates for each client, and drop drafts into Gmail—all without you copying/pasting anything.

How to Customize a Plugin (Make It Yours)

Default plugins are starting points. Click "Customize" on any installed plugin and Claude walks you through adjusting it:

You can customize:

• Swap connectors to match your tool stack
• Add your company's terminology and tone
• Adjust workflows to how your team actually operates
• Add custom skills specific to your process

Prompt to customize:

Customize this plugin to match how my team actually works.

We use: [list your tools]
Our process for [workflow type] is: [describe it]
Our tone/terminology: [examples]

Update all the skills and commands accordingly.

How to Build Your Own Plugin From Scratch

Use the Plugin Create plugin (meta, I know). Or build from scratch with this structure:

my-plugin/
├── plugin.json # Manifest file
├── commands/ # Slash commands
│ └── my-command.md
├── skills/ # Individual skills
│ └── my-skill/
│ └── SKILL.md
└── .mcp.json # Connector config

Starter prompt:

I want to build a plugin for [your workflow].

Walk me through creating the plugin structure, skills, and commands.

My workflow involves:
1. [Step 1]
2. [Step 2]
3. [Step 3]

The end result should be [what you want delivered].

Copy-Paste Prompts for Building Common Plugins

Build a Client Management Plugin

Build me a client management plugin that:
1. Pulls active projects from Notion (status, timeline, owner)
2. Writes weekly status updates using my communication style
3. Drafts them in Gmail as individual messages ready to send

Include a /send-updates command that runs all steps in parallel.

Build a Content Repurposing Plugin

Build a content repurposing plugin with skills for:
- Turning podcast transcripts into LinkedIn posts
- Creating Twitter threads from articles
- Extracting clip suggestions and timestamps from video scripts

Include commands: /repurpose:podcast, /repurpose:article, /repurpose:video

Build a Competitive Research Plugin

Build a competitive research plugin that:
1. Searches the web for competitor updates
2. Organizes findings into a structured brief (pricing, features, marketing, team)
3. Saves the brief to my Google Drive
4. Sends me a Slack summary

Include commands: /research:company and /market-analysis

Build a Sales Automation Plugin

Build a sales automation plugin that:
1. Pulls open deals from Salesforce
2. Writes personalized follow-up emails for each deal (based on deal stage and history)
3. Analyzes objections and generates response strategies
4. Saves drafts to Gmail

Include a /follow-up:all command.

Build a Proposal Generation Plugin

Build a proposal plugin that:
1. Takes project scope, client name, and timeline as input
2. Generates a professional proposal (executive summary, scope, timeline, pricing, terms)
3. Saves to Google Drive as a document
4. Sends a preview link to the client via email

Include /proposal:generate command.

What Plugins Actually Unlock

Here's the main benefit: Before plugins, you had to manage each piece manually.

• Connect your tool
• Load your skill
• Remember which prompt to use
• Execute manually
• Repeat for every task

With plugins:

• Set up once
• Run forever
• One command triggers an entire agentic workflow
• Finished work delivered where you need it

Before plugins: You spend 30 minutes on manual steps. Claude does 5 minutes of actual work.

With plugins: You type one command. Claude handles everything. Finished work appears in your inbox.

The people who figure this out first will operate like they have a team of 10. The people who don't will wonder why everyone else is moving so fast.

Resources to Master Plugins

Official Docs:

• Anthropic Plugin GitHub: github.com/anthropics/knowledge-work-plugins
• Claude Cowork Setup Guide: https://claude.ai/cowork

Communities:

• Build With AI (my community where we help $500k-5M operators reclaim 10+ hours per week with AI/agents): https://www.skool.com/aioperatoracademy/about

Skills to Learn:

• MCP (Model Context Protocol) - How to build custom connectors
• Slash commands - How to trigger workflows
• Sub-agents - How to parallelize tasks
• Data passing - How to chain skills together

Your Next Step

1. Pick one official Anthropic plugin and install it
2. Try three slash commands from that plugin
3. Run one command that pulls live data from one of your connected tools
4. Build your first custom plugin using one of the prompts above

The gap between who uses plugins and who doesn't will be massive by end of 2026.

Start now.

PS: Every week I break down workflows like this inside the Build With AI newsletter. Agents, automations, and step-by-step setups you can run yourself or sell to clients.

Join (free) here.

Wrong. This is completely different.

Here's what actually changed, why it matters for your business, and exactly how to build your first routine in the next 30 minutes.

What Routines Does That Wasn't Possible Before

For years, the automation game was the same: drag and drop builders. Connect tool A to tool B. Click. Save. Hope it works.

The issue is that when something breaks (and it will), you're stuck. The automation hits an edge case. The tool changes its API. The workflow can't reason about the problem. You wake up at 3 AM to fix it.

Claude Code Routines flip this.

A routine isn't a workflow builder. It's Claude running on Anthropic's cloud infrastructure with access to your code repositories, your GitHub events, and external tools you connect. When it runs, it doesn't just execute a predefined sequence. It thinks. It reasons. It adapts.

Here's what that unlocks:

1. Intelligent reasoning about problems. A PR review routine doesn't just check if tests pass. It reads your codebase, understands your architectural patterns, and applies your team's actual review standards to every PR.

It catches security issues. It suggests performance improvements. It leaves inline comments on specific lines. A drag-and-drop tool can't do this. Claude can.

2. Code generation and fixes, not just status checks. When your production app breaks, a routine doesn't just alert you. It clones your repository, reads the error logs, diagnoses the root cause, writes the fix, tests it, and opens a pull request.

You review it. You merge it. Done. No more 2 AM debugging marathons.

3. Context awareness across systems. Your Slack channel has a support request. Your Linear board has related issues. Your GitHub has recent changes. A drag-and-drop builder connects these as separate steps.

Claude connects them as context. It reads all three, synthesizes the information, and takes the right action, not just a predefined one.

4. Natural language configuration, not visual programming. You don't click. You describe the job in English. "Review every PR for security issues, performance problems, and alignment with our coding standards. Leave comments. Post a summary to Slack." Done. Claude builds the logic.

5. Continuous improvement without rebuilds. If your routine isn't working perfectly, you don't redesign the workflow. You tell Claude what to improve. The routine learns from your feedback and adapts the next run. Drag-and-drop builders require you to rebuild everything.

This is why Routines is not another automation tool. It's a fundamentally different category.

How Routines Compete With n8n (And Why Claude Wins)

Let's be direct: n8n, Make, Zapier - they're all the same playbook. Visual builders. Pre-built connectors. Workflow as flow chart.

They're great for simple stuff: "When a new row is added to Airtable, send a Slack message."

But they fall apart on anything complex.

n8n's limits (that Routines solves):

1. Complexity = spaghetti flows. Your workflow has 50+ steps, 10+ conditions, retry logic, error handling. The diagram becomes unreadable. Maintenance is a nightmare. With Routines your prompt stays readable. Your intent is clear.
2. No reasoning about the problem. n8n is procedural. Step 1, then step 2, then step 3. No thinking. Claude reasons about your specific problem, your specific code, your specific business logic. Same inputs to n8n produce the same outputs. Same inputs to Claude might produce different outputs because it thought deeper.
3. Connector hell. Need a custom integration? n8n has 300+ prebuilt connectors. That sounds great until the tool you need isn't there. Or it is, but it doesn't support the specific API endpoint you need. You're stuck. Claude connects via HTTP, custom scripts, and MCP connectors. You can integrate anything.
4. Error handling is brittle. When n8n hits an error, it stops or retries blindly. Claude reads the error. Diagnoses it. Adapts. "The API returned a 429 rate limit error. Let me wait 5 minutes and retry." No configuration needed. It thinks.
5. Cost scales with complexity. n8n charges per task or per execution. Complex workflows = more tasks = higher bills. Claude charges per token. A 100-line prompt runs on the same token budget whether it's doing something simple or incredibly complex. More sophisticated logic doesn't cost more.

Where n8n still wins: Simple integrations. Tons of prebuilt connectors. Visual learners might prefer dragging blocks.

Where Routines wins: Complex automation. Reasoning about context. Edge case handling. Code generation. Custom integrations. Production workflows.

The companies using n8n for simple 3-step integrations are fine. The companies trying to automate complex business logic on n8n? They're drowning in spaghetti flows. Routines is for them.

How to Quickly Build Routines (Spoiler: It's Stupid Easy)

Most people overcomplicate this.

Here's the shortcut.

Step 1: Think about the job, not the steps.

Don't ask: "What steps do I need to automate?"

Ask: "What's the outcome I want?"

Examples:

• "Automatically review every PR for security issues"
• "Triage support emails and assign them to the right team member"
• "Generate a weekly report from production logs and send to leadership"
• "Monitor for API failures and open a fix PR"

One sentence. That's your routine.

Step 2: Write the prompt in English.

Go to claude.ai/code/routines and click New routine.

In the prompt field, describe the job:

"You are a code reviewer. When a PR is submitted:

1. Read the code changes
2. Check for security vulnerabilities
3. Look for performance issues
4. Check alignment with our coding standards
5. Leave inline comments on specific lines
6. Post a summary to Slack

Be thorough but constructive. Focus on issues, not style preferences."

That's it. That's your routine. No configuration. No clicking. Just English.

Step 3: Point it at your repos and connectors.

Add your GitHub repository. Claude clones it at runtime. Add your Slack connector. Claude can post messages. Add your environment variables if needed (API keys, etc.). Done.

Step 4: Choose a trigger.

• Schedule: "Run every weeknight at 9 PM"
• GitHub: "Run when a PR is opened"
• API: "Run when an external tool POSTs to this endpoint"

Pick one or combine multiple. One routine can have all three triggers.

Step 5: Click Create and watch it work.

Your first routine runs. Check the logs. Refine the prompt if needed. That's it.

Total time: 10-15 minutes.

No engineers needed. No visual programming. No complex configuration.

Step-by-Step Setup: Your First Routine (PR Review)

Let's build a real example. You'll have a working routine in the next 30 minutes.

What We're Building

A routine that:

• Runs every time a PR is opened on your repo
• Reviews the code for security, performance, and style issues
• Leaves detailed comments on problem lines
• Posts a summary to Slack

Prerequisites

• GitHub account with a repository
• Claude Pro or higher (Routines need Claude Code on the web enabled)
• Slack workspace with a channel for notifications (optional, but better)

The Full Setup

Step 1: Go to claude.ai/code/routines

Click "New routine" button.

Step 2: Name your routine

Name: "Automated PR Code Review"

Step 3: Write the prompt

Paste this into the prompt field:

You are an expert code reviewer for our development team.

When you receive a pull request:

1. Clone the repository and read the changes

2. Review the code for:
- Security vulnerabilities (SQL injection, XSS, auth issues, credential exposure)
- Performance issues (N+1 queries, inefficient loops, missing caching)
- Code quality (unclear variable names, missing error handling, design patterns)
- Testing (are critical paths tested?)

3. For each issue found:
- Identify the exact file and line number
- Leave an inline comment on GitHub explaining the issue
- Suggest a fix (don't just criticize)

4. Post a summary comment on the PR with:
- Number of issues found
- Severity breakdown (critical, high, medium, low)
- Overall assessment ("Ready to merge" or "Needs work")

5. If critical security issues are found, post an alert to Slack

Be thorough but supportive. The goal is to make the code better, not to be harsh.

Step 4: Select your repository

Click "Add repository" and select the GitHub repo you want to review PRs for.

GitHub will prompt you to install the Claude GitHub App if you haven't already. Do it. This lets Claude receive webhook events when PRs are opened.

Step 5: Select your environment

Use the default environment for now. This gives Claude standard network access and basic environment variables.

Step 6: Add connectors

If you have Slack connected, it's automatically included. If not, skip this for now.

Step 7: Add a GitHub trigger

Under "Select a trigger":

• Choose "GitHub event"
• Event type: "Pull request"
• Action: "pull_request.opened"
• Leave filters empty (triggers on all PRs)

Step 8: Review and create

Double-check everything looks right. Click "Create routine."

Your Routine Is Now Live

The next time someone opens a PR on your repository, Claude will automatically review it. You'll see the results in GitHub as inline comments and a summary. If you added Slack, you'll get an alert there too.

How to Test It

Don't want to wait for a PR? Go to the routine's detail page and click "Run now." It will try to review a recent PR from your repo.

How to Improve It

The first run probably won't be perfect. Check the results. If Claude missed something, update the prompt. Click "Edit routine" and adjust your instructions. Run again.

Examples:

• If it's too nitpicky: "Focus only on security and performance, not style preferences"
• If it's missing issues: "Pay special attention to database queries and API calls"
• If comments are too long: "Keep comments concise, one sentence per issue"

Each time you run, it learns and improves.

What This Actually Enables

You now have a PR review routine. But the real value is seeing what's possible.

Once you have this working, you can build:

• Backlog triaging. Routine reads new issues, applies labels, assigns owners
• Deploy verification. Routine checks if production is healthy after each deploy
• Documentation drift detection. Routine flags docs that reference changed APIs
• Support ticket triage. Routine reads support emails, categorizes them, creates tickets
• Weekly reporting. Routine pulls data from multiple sources, writes the report, sends it

Each one takes 15 minutes to set up. Each one replaces a manual, repetitive task.

The Bottom Line

Claude Code Routines isn't another automation tool.

It's automation for things that were too complex to automate before.

n8n is great for simple integrations. Routines is great for reasoning about your code, your data, your business logic. They're different tools for different jobs.

The companies moving fast right now are NOT building complex n8n workflows. They're describing what they want to Claude and getting it done in 10 minutes.

That's the play.

Start with the PR review routine above. Get it working. See what else you can automate. The prompt-driven approach scales way further than visual builders ever could.

PS: Every week I break down workflows like this inside the Build With AI newsletter. Agents, automations, and step-by-step setups you can run yourself or sell to clients.

Join (free) here.

That's an onboarding problem, not a problem with Cowork itself.

Claude Cowork is now generally available on macOS and Windows. It has enterprise features, a Zoom connector, and plugin support that didn't exist two months ago. But none of that matters if your first session starts with Claude knowing nothing about you.

I set up Cowork for my business and my clients. Here's the exact process that turns it from a novelty into something you actually rely on every day.

PS: Every week I break down workflows like this inside the Build With AI newsletter. Agents, automations, and step-by-step setups you can run yourself or sell to clients.

Join (free) here.

-----------------------------------------

1. Connect Your Tools Before You Type a Single Prompt

This sounds obvious. Most people skip it anyway.

Open Claude Desktop. Go to connectors. Connect everything you use daily:

• Google Workspace (Drive, Gmail, Calendar). This is non-negotiable. Most of your business context lives here.
• Slack or Teams. If your team communicates in either, Claude needs access to understand your workflows.
• Notion, Asana, Linear, Monday. Wherever your projects and docs live.
• Zoom. New as of April 2026. Claude can now pull meeting context and recordings.

Why this matters first: when you create your context files in Step 2, Claude can pull from your existing documents instead of you typing everything from scratch. Connect first, configure second.

Quick test: ask Claude to find a recent document in your Drive. If it finds it, move on.

2. Build Three Context Files (Let Claude Do Most of the Work)

Context files are documents that load automatically every session. They're how Claude knows who you are without you re-explaining it every time.

You need three.

File 1: about-me.md

Cover these in plain language:

• What you do (one sentence)
• What businesses or projects you're running right now
• Who your customers are
• What tools you use daily
• Any key people Claude should know about (co-founder, assistant, key clients)

Here's the shortcut most people miss: if you already connected Google Drive, tell Claude to scan your existing docs for company info, bios, and about pages. It will draft 80% of this file for you. Edit what it gets wrong. Save.

File 2: voice.md

This is how Claude stops sounding like a robot. Include:

• Your communication style (direct and blunt? warm and conversational? somewhere in between?)
• Words or phrases you hate (if "synergy" makes you cringe, say so)
• How you communicate differently by context (client emails vs. team Slack vs. social posts)
• 2-3 examples of writing you've done that sounds like you

Paste actual writing samples. A good email you sent. A social post that performed. Claude will reverse-engineer your patterns better than any style guide.

File 3: preferences.md

Tell Claude how to work with you:

• Should it ask clarifying questions or just execute?
• What format do you want outputs in? (bullet points, paragraphs, tables)
• How detailed should first drafts be?
• Any hard rules? ("Never use em dashes." "Always include pricing." "Keep responses under 300 words unless I ask for more.")

Save all three files in one folder. Name it whatever you want. Select this folder at the start of every Cowork session.

3. Set Global Instructions (Your Always-On Brief)

Global instructions run in every session, even before you select a folder. Think of it as your executive brief that Claude reads before doing anything.

Take the key points from your three context files and condense them into roughly 800 words. Structure it like this:

• Who I am (2-3 sentences)
• How I work (preferences for task approach and communication)
• Output defaults (format by task type: emails get bullet points, content gets paragraphs, research gets tables)
• Voice (the 3-4 most important characteristics)
• Key business context (what I'm working on right now, what matters most)
• Rules (always do X, never do Y)

Activate it: Claude Desktop → Settings → Cowork → Edit next to Global Instructions → paste → save.

From now on, every new session starts with Claude already knowing your name, your business, your communication style, and your rules. No more "I'm Corey, I run a company called..."

4. Install 3-5 Skills That Match Your Actual Work

Skills are pre-built capabilities that turn Claude from a general assistant into a specialist. Without skills, Claude can chat. With skills, it can:

• Research and profile potential partners or podcast guests
• Draft content that matches your voice and format preferences
• Prep for meetings by scanning attendee backgrounds
• Process data and generate reports from your connected tools
• Run multi-step workflows end to end

Here's how to pick: look at your last week. What did you do more than once that felt repetitive? Start there.

Use the plugin search in Cowork. Search by keyword (content creation, sales, research, meeting prep). Install 3-5 that match your highest-frequency tasks.

Don't install 20 skills on day one. You'll never use most of them. Start small, add as you hit friction.

Custom skills are also an option. If no existing skill matches your workflow, you can build your own. That's a deeper topic, but know the door is open.

5. Create Your First Scheduled Task

This is where Cowork becomes an employee, not an assistant.

Scheduled tasks run automatically on a schedule you define. No prompting required. Claude does the work and delivers results.

Start with one of these:

• Morning briefing (every weekday, 8 AM): Check inbox for anything urgent, scan today's calendar, list top 3 priorities. Deliver as a summary.
• Weekly recap (every Friday, 4 PM): Summarize completed tasks, flag anything overdue, highlight key wins for the week.
• Meeting prep (30 minutes before any call): Pull attendee context, check for any prior communication history, generate 3 talking points.

Ask yourself three questions to find more:

1. What do I do every day or week that's repetitive?
2. What do I always forget until it's too late?
3. What would save me the most time if it happened automatically?

Create the task with a plain-language schedule and a detailed prompt. The more specific the prompt, the better the output. "Summarize my inbox" produces generic results. "Check my Gmail for unread messages from clients, flag anything that mentions a deadline or needs a response today, and list them by priority" produces something you'll actually use.

6. The First-Week Calibration (Don't Skip This)

This step wasn't in the original version of this guide. It should have been.

Your first week with Cowork will not be perfect. The morning briefing will miss things you care about. The voice file won't capture every nuance. The scheduled tasks will need tuning.

That's the point. Treat week one as calibration, not production.

Every time Claude gets something wrong, tell it. "The tone on that email was too formal. Match the example I gave you in voice.md." "The morning briefing is too long. Cut it to 5 bullet points max."

By end of week one, your context files should have 2-3 small edits. Your global instructions should be tighter. Your scheduled tasks should be tuned to actually match how you work.

By week two, it should feel like Claude has worked with you for months.

The Full Setup (Under 30 Minutes)

Here's the timeline:

1. Connect tools (5 minutes): Google Workspace + your core stack
2. Create context files (10 minutes): Let Claude draft from your existing docs, then edit
3. Set global instructions (5 minutes): Condense context files into the always-on brief
4. Install skills (5 minutes): Pick 3-5 for your most repetitive tasks
5. Create first scheduled task (5 minutes): Start with a morning briefing

That's it. 30 minutes and Claude goes from "generic chatbot" to "knows your business, your voice, your tools, and runs tasks while you sleep."

The people who get the most out of Cowork aren't the ones with the most skills installed. They're the ones who spent 30 minutes on setup and 5 minutes per day calibrating for the first week.

Do the setup. Do the calibration. Everything after that compounds.

PS: Every week I break down workflows like this inside the Build With AI newsletter. Agents, automations, and step-by-step setups you can run yourself or sell to clients.

Join (free) here.

It's not.

Claude is four distinct products with four completely different jobs. Use the wrong one and you're either overcomplicating simple tasks or leaving massive productivity on the table.

Here's what each one actually does, who it's for, and when to pick one over the others.

PS: I built a Google Doc that you can hand to Claude Code to deploy your first Claude Managed Agent (Claude's newest product) in 60 seconds.

This is the easiest way to test this new feature if you're non-technical.

Grab it free here.

--------------------------------------

Claude Chat: The Starting Point

This is the Claude most people know. Go to claude.ai, type a question, get an answer.

What it does:

• General conversation and Q&A
• Writing, editing, and brainstorming
• Document analysis (upload PDFs, images, spreadsheets)
• Research and summarization
• Projects for saving reusable context and instructions

Who it's for: Anyone who needs a smart assistant for thinking work. Writers, researchers, students, business owners who want quick answers. If your work is mostly "help me think through this" or "help me write this," Claude Chat is your tool.

Where it works: Web browser, desktop app, mobile app. No setup. Just start typing.

What it can't do: This is the critical limitation. Claude Chat can't take action in your other tools. It can't send an email, organize your files, update a spreadsheet, or commit code. You're the middleman. You ask, it answers, you copy-paste the result somewhere else.

Pricing: Free tier available. Pro is $20/month for significantly more usage.

Bottom line: Claude Chat is your thinking partner. Great for brainstorming, writing, and analysis. But it stays inside the chat window.

Claude Code: The Developer's Workhorse

Claude Code is Claude with hands, specifically for software development.

What it does:

• Reads your entire codebase and understands project context
• Edits files directly in your repository
• Runs terminal commands
• Handles git workflows (commits, PRs, branches)
• Spawns Agent Teams where multiple Claude instances coordinate on complex tasks
• Integrates with GitHub, GitLab, Slack via MCP connectors

Who it's for: Developers. Full stop. If you don't write code, Claude Code isn't for you. But if you do write code (any language, any framework), this is the single biggest productivity upgrade available right now.

It lives in your terminal, your IDE (VS Code, Cursor, etc.), or your browser. It doesn't just suggest code. It writes it, tests it, and commits it.

Where it works: Terminal, VS Code, Cursor IDEs, browser-based environments.

What it can't do: Claude Code is purpose-built for software development. It won't organize your Google Drive, draft your emails, or manage your calendar. It's also not beginner-friendly. If you've never opened a terminal, the learning curve is real.

Pricing: Same tiers as Claude Chat. Pro at $20/month, up to Ultra at $200/month for heavy usage.

Bottom line: If you write code and you're not using Claude Code, you're working harder than you need to. It's a junior developer that never sleeps, never complains, and ships code 24/7.

Claude Cowork: The Biggest Unlock for Non-Developers

This is the one most people haven't tried yet. And it's the one that matters most for non-developers.

Claude Cowork lets Claude take actions directly on your computer and inside your apps. Not just answer questions. Actually do the work.

As of April 2026, Cowork is now generally available on both macOS and Windows for all paid subscribers. It's no longer a research preview.

What it does:

• Connects to Google Drive, Gmail, Excel, PowerPoint, Slack, Zoom, and more
• Organizes files, renames documents, deletes duplicates
• Generates reports, spreadsheets, and presentations
• Runs batch operations (convert formats, compress images, standardize naming)
• Automates browser tasks via Chrome extension
• Uses customizable plugins for specific workflows

Who it's for: Knowledge workers. Solo operators. Small business owners. Agency teams. Anyone drowning in repetitive computer tasks who doesn't write code. If you spend your day bouncing between Google Docs, email, spreadsheets, and Slack, Cowork is built for exactly your workflow.

Where it works: Claude Desktop app (macOS and Windows). Requires the app to stay open during tasks.

What it can't do: Cowork uses more tokens than regular chat, so plan your usage. It needs the desktop app running. And there's a learning curve to understanding what it can and can't do across your specific tool stack.

Pricing: Available on any paid Claude plan (starting at $20/month).

Bottom line: Cowork is Claude living inside your business tools. It pulls data from one app, processes it, and pushes results to another. No code required.

Claude Managed Agents: The Platform Play

This is the newest Claude product and the most different from the other three. Managed Agents launched in public beta in April 2026. It's not a chat interface or a desktop tool. It's infrastructure.

Claude Managed Agents is a hosted platform for building and deploying autonomous AI agents at scale. You define the agent, give it tools, and let it run in the cloud for minutes or hours without supervision.

What it does:

• Runs Claude as a long-running autonomous agent in cloud infrastructure
• Provides a pre-built agent harness (the loop that calls Claude and routes tool calls)
• Gives each agent a secure sandbox with bash, file operations, web search, and code execution
• Supports MCP servers so agents can connect to external services
• Handles session persistence, so an agent can pick up where it left off after interruption
• Scales to multiple agents coordinating on complex tasks (multi-agent workflows)
• Built-in prompt caching, context compaction, and performance optimizations

Who it's for: Developers and businesses building AI-powered products. If you want to deploy Claude as a backend service that handles customer requests, processes documents, manages workflows, or automates operations without human involvement, this is the tool.

Companies like Notion, Asana, and Sentry are already using it. It's designed for teams that want to embed Claude's intelligence into their own products, not just use it as a personal assistant.

To quickly spin up your first Managed Agent you can hand this Google doc to Claude Code and simply tell it "deploy a managed agent" and it'll take it from there.

Where it works: Cloud-hosted via the Claude Platform API. You interact with it through API calls, not a chat window or desktop app.

What it can't do: This is not a consumer product. You won't find Managed Agents in the Claude app. It requires API access, technical implementation, and an understanding of how to design agent workflows. It's not for someone who just wants Claude to help write an email.

It also consumes API credits, and long-running agent sessions can add up. Plan your usage and set spend limits.

Pricing: Usage-based via the Claude API. You pay for the tokens your agents consume. Available to all API accounts.

The architecture (simplified): Anthropic decoupled three components that used to be bundled together:

1. The brain (Claude and its harness): Handles reasoning and deciding what to do next
2. The hands (sandboxes and tools): Where Claude actually executes work
3. The session (event log): A durable record of everything the agent did

Each piece can fail and recover independently. If a sandbox crashes, the brain catches it and spins up a new one. If the harness fails, a new one loads the session log and picks up where it stopped. No single failure kills the whole agent.

This separation also means the brain doesn't hold credentials. OAuth tokens and API keys live in a secure vault outside the sandbox, so even if something goes wrong in the execution environment, your sensitive data stays protected.

Bottom line: Managed Agents is Claude as a service you deploy, not a tool you use personally. It's for building products powered by Claude that run autonomously in the cloud.

The Comparison Table

How to Pick the Right One

Start with Claude Chat if:

• You're new to AI assistants
• Your work is mostly thinking, writing, or research
• You don't need Claude to take action anywhere
• You want the simplest possible experience

Move to Claude Code if:

• You write code in any language
• You want AI that actually ships changes, not just suggests them
• You're comfortable in a terminal or IDE
• You need automated code review, testing, or deployment

Move to Claude Cowork if:

• You need Claude to work inside your existing apps
• Your day involves repetitive tasks across multiple tools
• You're non-technical but want real automation
• You want to stop being the copy-paste middleman between Claude and everything else

Build with Managed Agents if:

• You're building a product that needs AI agents running in the background
• You want Claude handling customer-facing workflows autonomously
• You need agents that run for hours, recover from failures, and scale
• You're embedding Claude's intelligence into your own software

The Real Power Move

These four tools aren't competitors. They're layers of the same platform.

Use Claude Chat for thinking through problems and drafting content. Use Claude Code for building software. Use Cowork for the repetitive tasks that eat your day across your app stack. Use Managed Agents to deploy Claude-powered agents as a product or service.

The progression for most people looks like this: start with Chat, realize you need it to take action, move to Cowork (or Code if you're a developer), and eventually build with Managed Agents when you want to scale beyond personal use.

The question isn't "which Claude is best?" It's "which Claude fits this specific task?"

Pick one, get comfortable, then expand when you hit its limits. You'll know exactly when you need the next one because you'll feel the friction.

PS: I built a Google Doc that you can hand to Claude Code to deploy your first Claude Managed Agent (Claude's newest product) in 60 seconds.

This is the easiest way to test this new feature if you're non-technical.

Grab it free here.

Wrong.

This is the single biggest unlock for anyone who wants to sell AI services to businesses (or agentify your existing business).

And you don't need to be a developer to understand why.

Here's what it actually means, why it matters for your business, and how to get started even if you've never written a line of code.

PS: I made a Google Doc that walks Claude Code through deploying your first managed agent step by step.

Hand it to Claude Code. It does the rest.

Grab it here: return-my-time.kit.com/2872b904f5

————————————————————————————————————————

What Claude Managed Agents Actually Is (In Plain English)

Think of it like this: until yesterday, building a custom AI agent that could actually do real work for a client (read their emails, update their CRM, generate reports, send follow-ups) required you to set up servers, manage security, handle errors, and deal with infrastructure.

That's months of engineering work before your agent does anything useful.

Managed Agents is Anthropic saying: "We'll handle all of that. You just tell the agent what to do."

You define the job. They run it on their cloud. The agent gets a secure workspace where it can write code, search the web, read files, and connect to tools. All without you building any of the plumbing.

Why Non-Technical People Should Care

This changes the math on starting an AI services business.

Before Managed Agents:

- Need to understand technical infrastructure

- Need to manage servers and security

- Extensive setup before you can sell anything

After Managed Agents:

- Anthropic handles the infrastructure

- Security and sandboxing built in

- Prototype to live agent in days

- Cost: API usage only (pay as you go)

The barrier to entry just dropped from "hire a dev team" to "describe what you want the agent to do."

The 4 Building Blocks (All You Need to Know)

Every managed agent runs on four things:

1. Agent - The instructions. What model to use, what the agent should do, and what tools it has access to. Think of this as the job description you'd write for a human assistant.

2. Environment - The workspace. Pre-loaded with the software and tools your agent needs. Like setting up a new employee's laptop before their first day.

3. Session - A running conversation. The agent remembers everything within a session, can work for hours, and keeps files around between interactions.

4. Events - Messages back and forth. You send tasks in. The agent streams back results, status updates, and asks for approval when needed.

That's it. Four concepts. Everything else is details.

What You Can Actually Build With This

Real companies are already shipping on Managed Agents:

For your own business:

- An agent that reads every client email and drafts responses in your voice

- A research agent that monitors competitors and sends you weekly briefings

- A content agent that takes your rough notes and produces finished blog posts, social copy, and newsletters

As a service you sell to clients ($1,500-5,000 setup + $500/month):

- Client onboarding agents that collect documents, send reminders, and set up new accounts automatically

- Report generators that pull data from multiple sources and create weekly summaries for executives

- Customer support agents that answer common questions, escalate edge cases, and log everything in the CRM

- Document processors that take raw files, extract the important data, and organize it. Finance teams are already using this.

- Project management agents that join Asana or Linear and pick up tasks like a team member

The key insight: you don't need to build these yourself. You need to understand the client's problem well enough to describe it to the agent.

The Permission System (Why Clients Will Trust This)

This is the part most people skip, but it's what makes this sellable to real businesses.

Managed Agents has two modes:

- Auto-run: The agent handles everything automatically. Good for internal tools where you trust the workflow.

- Approval required: The agent pauses and asks before taking action. Good for anything client-facing or sensitive.

You can mix them. Let the agent read files and search the web automatically, but require approval before it sends an email or updates a database.

This is how you get risk-averse business owners to say yes. "The agent will draft the email, but it won't send anything without your approval."

How to Get Started (Two Paths)

Path 1: You already use Claude Code

Open Claude Code and type one line:

"start onboarding for managed agents in Claude API"

It walks you through the entire setup. Or grab our free Google Doc guide that does the same thing: https://return-my-time.kit.com/2872b904f5

Path 2: You're non-technical and want help

This is exactly what we cover inside the Build With AI community. We do live office hours where we walk through agent builds step by step, from identifying the right problem to deploying a working agent for a client.

You don't need to understand APIs or write code. You need to understand business problems. We'll handle the rest together.

What This Costs

Pricing is usage-based:

- Standard Claude API token rates (same as using Claude normally)

- $0.08 per session-hour for active runtime

- Web search: $10 per 1,000 searches (if your agent needs it)

A typical 10-minute agent session costs a few cents. Even heavy usage stays well under $100/month for most use cases.

Compare that to hiring a developer or paying for enterprise software. The math works.

The Business Opportunity Nobody's Talking About

Here's what most people miss: the businesses that need this most (law firms, accounting practices, real estate agencies, medical offices) will never set this up themselves. They don't want to. They want someone to do it for them.

That someone is you.

The playbook:

1. Run a $999 AI audit to identify their biggest time wasters

2. Build a managed agent that solves their top problem

3. Deploy it on Anthropic's infrastructure (they handle security, uptime, everything)

4. Charge $500/month to maintain and improve it

4 clients = $2,000/month recurring. 10 clients = $5,000/month. And each agent takes less time to maintain than a single meeting.

99.9% of people who read this article will bookmark it and do nothing.

Be the 0.1%. Take action.

PS: I made a Google Doc that walks Claude Code through deploying your first managed agent step by step.

Hand it to Claude Code. It does the rest.

Grab it here:

return-my-time.kit.com/2872b904f5

It's a fully functional cloud-based AI employee that coordinates 19 different models, connects to 400+ apps, and runs complex workflows in the background while you do other things.

I've been using it for weeks. Here's everything I wish someone told me before I started.

PS: I recorded videos comparing Computer head to head against Claude Cowork and Manus. Computer smoked both of them.

Here's the Cowork comparison video, and here's the Manus video.

---------------------------------------

What Perplexity Computer Actually Is

Computer is a "digital worker" that lives inside your Perplexity account. You describe what you want done. It breaks that into subtasks, assigns each one to the best AI model for the job, and works autonomously until it's finished.

It's so much more than a chatbot. It's an agent that can research markets, build websites, write reports, send emails, analyze data, and create full applications, all without you watching over its shoulder.

Each task runs inside an isolated cloud sandbox (2 vCPUs, 8 GB RAM) with Python, Node.js, and standard Unix tools pre-installed.

Nothing touches your local machine. If something breaks, the failure stays contained.

How to Set It Up (First Time User)

Step 1: Get the right subscription.

Computer requires Perplexity Pro ($20/month) or Perplexity Max ($200/month). I'm on the Max plan, but the Pro plan will give you enough credits to get you started.

Step 2: Access Computer.

Once subscribed, you'll see "Computer" in the Perplexity interface. Click it. That's your command center.

Step 3: Connect your tools.

In the Computer panel, go to Connectors. This is where it gets powerful. You'll see 400+ apps available through managed OAuth connections. Enable the ones you use daily.

Start with these:

• Gmail / Google Calendar (read and send emails, manage your schedule)
• Slack (pull conversation history, send messages)
• Notion (read and write to your workspace)
• Google Drive (access docs, sheets, slides)
• GitHub (manage repos, create issues, review PRs)

Each connector takes about 30 seconds to set up. Click Enable, authorize with OAuth, done.

Step 4: Give it your first task.

Start simple. Something like: "Research the top 5 competitors in [your industry] and create a comparison table with pricing, features, and target audience."

Watch how it breaks the task down, assigns subtasks, and delivers results. That first task teaches you more about how Computer thinks than any documentation will.

The Connectors (This Is the Real Moat)

Connectors are what separate Computer from every other AI tool. Without them, it's just a smart chatbot. With them, it's an AI that can actually do things inside your business.

Productivity: Gmail, Outlook, Google Calendar, Google Drive, OneDrive, Box

Communication: Slack (both the connector for searching Slack data AND the Slack app for running Computer inside Slack), Microsoft Teams

Project Management: Notion, Asana, Jira, Linear, Confluence

Sales & CRM: Salesforce, HubSpot

Developer: GitHub, Vercel

Data: Snowflake, PostgreSQL

Custom: You can add your own via MCP server URLs with OAuth, API key, or open authentication

Pro tip: The Slack integration is actually two things. The Connector for Slack lets Computer search your Slack data from inside Perplexity. The Slack App lets you run Computer directly inside Slack by messaging it. Use both.

Another pro tip: Connect everything, even tools you don't think you'll need. Computer pulls context from connected tools automatically when working on tasks. More connections = better context = better outputs.

How It Uses Different Models (Multi-Model Orchestration)

This is Perplexity's biggest differentiator and the thing most people don't fully appreciate.

Computer doesn't use one AI model. It uses 19, and it routes each subtask to whichever model is best suited for that specific job.

Here's how it breaks down:

• Claude Opus 4.6 (Anthropic): Core reasoning engine. Handles the complex decision-making and coordination.
• GPT-5.2 (OpenAI): Long-context recall and broad web search. Great for tasks that require synthesizing large amounts of information.
• Gemini (Google): Deep research tasks. When Computer needs to go deep on a topic, Gemini handles it.
• Grok (xAI): Lightweight, speed-sensitive operations. Quick tasks that don't need heavy reasoning.
• Nano Banana (Google): Image generation.
• Veo 3.1: Video production.

You don't choose which model to use. Computer decides based on the subtask. This is why the outputs are often better than what you'd get from any single model.

A coding task might use Claude for architecture decisions, GPT-5.2 for pulling documentation context, and Grok for quick syntax checks, all in the same workflow.

The Credit System (Read This Before You Burn Through Them)

The higher subscription plan you're on, the more credits you get each month by default. Credits are consumed per task based on complexity.

Credit ranges:

• Simple tasks (alt text, quick lookups): ~30 credits
• Medium tasks (research reports, email drafts): ~100-500 credits
• Complex tasks (building apps, extended coding): 1,000-5,000+ credits

What happens when you run out: Tasks pause (not cancel). They resume when credits refill at your next billing cycle or when you buy more.

Auto-refill is OFF by default. This is intentional. There's a monthly spending cap set at $200 by default (adjustable up to $2,000). With default settings, your maximum possible monthly spend is $400: the subscription plus one refill.

Unused credits don't roll over. They expire at the end of each billing cycle.

The biggest credit-waster: Vague prompts. "Make this better" triggers broad compute cycles across multiple models. "Rewrite the headline to include a specific benefit and reduce to under 10 words" uses a fraction of the credits for a better result.

Underrated Features Most People Miss

1. Spaces

Spaces are persistent project environments. Create a Space for each major project or client. Upload reference documents, set context, and every task you run inside that Space has access to all of it.

Think of it like giving Computer a project brief that it never forgets.

2. Scheduled Tasks

Computer can run tasks on a schedule. "Every Monday morning, pull my calendar for the week, summarize my key meetings, and draft prep notes for each one." Set it and forget it.

This alone is worth the subscription for anyone with a packed calendar.

3. Subagent Spawning

When Computer hits a complex task, it spawns subagents to parallelize the work. A market research task might spawn one subagent for competitor pricing, another for feature comparison, and a third for customer reviews, all running simultaneously.

This is why complex tasks finish faster than you'd expect.

4. Skill Playbooks

Computer loads from 50+ domain-specific skill playbooks automatically. When you ask it to build a website, it activates the web development playbook. Research task? Research playbook.

It's pre-loaded with best practices for common workflows so you don't have to prompt-engineer everything from scratch.

5. Email Attachments

Recent update: Computer can now attach files to emails it sends on your behalf. If it generates a report and you ask it to email it to someone, the report goes as an attachment.

Small feature, huge time saver.

6. PRD-First Workflow (My Favorite Hack)

Don't go straight to Computer with a complex build. Draft a PRD (product requirements doc) in Claude first.

Define exactly what you want: features, layout, copy, functionality. Then hand that PRD to Computer.

It follows specs precisely, wastes fewer credits, and the output is dramatically better than a vague prompt.

What Computer Is Great At

• Research: Market analysis, competitor breakdowns, trend reports. It searches the web in real time and synthesizes across sources.
• Content creation: Reports, presentations, email sequences, landing pages.
• Data analysis: Connect Snowflake or upload CSVs and ask questions about your data.
• App building: Full web applications, dashboards, internal tools. It writes, tests, and deploys.
• Workflow automation: Recurring tasks on a schedule across your connected tools.
• Multi-step projects: Anything that requires coordinating research, writing, analysis, and action across multiple tools.

What to Watch Out For

Connector reliability varies. Most work great. Some (like Vercel) have OAuth tokens that expire frequently. GitHub sometimes needs manual token workarounds.

Test your connectors with a simple task before relying on them for something important.

No live preview for code. When Computer builds something, you can't see it running in real time. You have to push it to external hosting to check. This creates a slow feedback loop for development tasks.

Credits burn fast on stuck tasks. If Computer hits a dependency error and keeps retrying, it'll burn through credits without telling you why.

Monitor complex builds in real time, especially the first time you try a new type of task.

Context can't be manually managed. There's no way to branch, clear, or summarize context mid-conversation. Long sessions can drift. Start fresh when switching to a different type of task.

Computer vs. Claude Cowork vs. OpenClaw

Perplexity Computer: Best for non-technical users who want zero setup, multi-model orchestration, and 400+ connectors out of the box. $20/month. Cloud-based, sandboxed, safe.

Claude Cowork: Best for users who want a personal AI workspace with skills, scheduling, and deep tool integration. $20-200/month depending on plan. Desktop app, more hands-on customization.

OpenClaw: Best for technical users who want full control and unlimited customization. Free but requires a dedicated machine, terminal access, and API key configuration.

All three can get the job done. Your choice depends on your technical comfort level and how much setup you're willing to do.

The Bottom Line

Perplexity Computer is the easiest way to get an AI employee running in your business today. No code, no terminal, no configuration files. Connect your tools, describe what you need, and let it work.

The credit system means you need to be intentional with your prompts. Vague asks waste money. Specific, well-scoped tasks get the best results.

Start with one recurring workflow. Something you do every week that takes 2-3 hours. Automate it with Computer. Once you see the time savings, you'll find ten more things to hand off.

PS: I recorded videos comparing Computer head to head against Claude Cowork and Manus. Computer smoked both of them.

.Here's theCowork comparison video, and here's theManus video

But most of them will never actually build one.

Here's why you should, and exactly how to do it this weekend.

PS: Want the skill that builds this system for you?

Grab the free LLM Knowledge Base skill.

-------------------------------------

Why Most Business Owners Are Sitting on a Gold Mine of Unused Knowledge

You already have everything you need to build a competitive advantage.

It's sitting in your bookmarks, your notes app, screenshots you forgot about, articles you saved and never re-read, meeting notes from six months ago.

The problem isn't collecting information. You're already doing that. The problem is you can't find any of it when you need it.

A personal knowledge base fixes this permanently. You dump everything into one place, point an AI at it, and it organizes the mess into a searchable wiki that gets smarter every time you use it.

No special software. No database. Just folders and text files.

The Setup (Takes 5 Minutes)

Create a project folder anywhere on your computer. Inside it, make three subfolders:

• raw/ - Your junk drawer. Articles, notes, screenshots, meeting transcripts, bookmarks, research. Everything goes here. Don't organize it. That's the AI's job.
• wiki/ - Where the AI writes the organized version. Summaries, connections between ideas, topic pages. You never edit this by hand.
• outputs/ - Answers, reports, and research the AI generates when you ask questions against your knowledge base.

That's the entire architecture. Three folders.

The One File That Makes Everything Work

Create a file in the root of your project called CLAUDE.md (or AGENTS.md, the name doesn't matter). This is your schema file. It tells the AI what the knowledge base is about and how to organize it.

Here's a starter template:

# Knowledge Base Schema

## What This Is
A personal knowledge base about [YOUR TOPIC].

## How It's Organized
- raw/ contains unprocessed source material. Never modify these files.
- wiki/ contains the organized wiki. AI maintains this entirely.
- outputs/ contains generated reports, answers, and analyses.

## Wiki Rules
- Every topic gets its own .md file in wiki/
- Every wiki file starts with a one-paragraph summary
- Link related topics using [[topic-name]] format
- Maintain an INDEX.md that lists every topic
- When new raw sources are added, update the relevant wiki articles

## My Interests
[List 3-5 things you want this knowledge base to focus on]

This file is the equivalent of giving a new employee a training manual on day one. Without it, the AI just guesses at what matters. With it, every output is structured exactly how you want.

Fill Your Raw Folder (10 Minutes)

This is where people stall. They create the folders and stare at an empty directory.

The answer: dump everything. Copy-paste articles into .md or .txt files. Export notes from whatever app you use. Save screenshots. Paste in meeting notes, research, project docs. Don't rename anything. Don't clean it up.

I keep 15+ raw source files across my content pipeline. Clipped articles, competitor breakdowns, analytics reports. None of it is organized by hand.

Tell the AI to Build Your Wiki

Open Claude Code, Cursor, or any AI tool that can read your files. Point it at your project folder and say:

"Read everything in raw/. Compile a wiki in wiki/ following the rules in 
CLAUDE.md. Create an INDEX.md first, then one .md file per major 
topic. Link related topics. Summarize every source."

Then walk away. Let it work.

When it's done, you'll have a wiki folder full of organized articles with connections you didn't see, summaries of things you forgot you saved, and an index that makes everything searchable in seconds.

The Compounding Loop

This is where the system becomes valuable. Once your wiki has 10+ articles, start asking questions:

• "Based on everything in wiki/, what are the three biggest gaps in my understanding of [topic]?"
• "Compare what source A says about [concept] vs source B. Where do they disagree?"
• "Write me a 500-word briefing on [topic] using only what's in this knowledge base."

Save the answers back into the knowledge base. Every question makes the next answer better. That's the loop.

Monthly Health Check (Non-Negotiable)

Once a month, tell the AI:

"Review the entire wiki/ directory. Flag contradictions between articles. Find topics mentioned but never explained. List claims not backed by a source in raw/. Suggest 3 new articles to fill gaps."

This catches errors before they compound. If the AI writes something slightly wrong and you save it back, the next answer builds on that mistake. The health check is your quality control.

You Don't Need Obsidian

Half the internet is pitching Obsidian plugins for this. You don't need them. A folder of .md files and a good schema file will outperform a fancy tool stack 90% of the time.

I've watched people spend more time configuring their note-taking app than actually building their knowledge base. Stop shopping for the perfect tool. Start building.

Three folders. One schema file. An AI that maintains everything. That's the whole system.

Pick your topic. Create the folders. Drop in what you already have. Let the AI do the rest.

PS: Want the skill that builds this system for you?

Grab the free LLM Knowledge Base skill (free).

I sold the exact agent in this article for $1,500 before I even had a website. The whole build took a few hours.

This article breaks down the exact steps to build and sell your first AI agent, even if you've never sold a service before.

PS: This is one of MANY agents you can build then sell. I made a custom GPT that interviews you for 3 minutes then hands you a blueprint for your first (or next) agent.

You can access the GPT for free here.

------------------------------------

The Speed-to-Lead Agent

This is one of the easiest agents to build and also happens to be THE easiest agent to sell.

What it is: An AI system that responds to incoming leads instantly, 24/7, without human involvement.

Someone fills out a form. The agent responds in seconds via text or email, asks qualifying questions, captures details, books a meeting, and sends the owner a summary.

The lead never waits. The owner wakes up to qualified appointments on the calendar instead of cold leads to chase.

Why It Works

• 78% of customers buy from whoever responds first
• 5-minute response makes you 21x more likely to qualify a lead vs. 30 minutes
• Average B2B response time is 42 hours. The lead is gone by then.

Speed alone isn't enough though. The response has to be intelligent.

"Thanks for reaching out, someone will be in touch" is worthless. Every business sends that.

"Hey [Name], thanks for asking about [specific service]. Quick question, is this residential or commercial?" That turns a form submission into a conversation.

That's the gap between a $50/month autoresponder and a $500/month AI agent.

Three Ways to Build It

Zapier (1-2 hours): Fastest to ship.

• Form trigger
• SMS via Twilio
• email notification
• CRM record
• Slack alert to the owner

Gets pricey at scale ($50-150/month) with limited logic branching and no native AI. Best for dead-simple workflows.

Make (2-4 hours): Best balance of power and simplicity.

• Webhook receives form data
• router splits leads by type (residential vs. commercial)
• OpenAI generates a personalized response
• Twilio fires off the SMS
• Google Sheets logs everything
• booking link gets included automatically

Native AI modules and a visual builder for $9-16/month.

n8n (4-8 hours): Most powerful option.

• AI agent node with conversation memory
• conditional branching based on lead responses
• multi-channel outreach (SMS + email + voicemail drop)
• automated follow-up sequences

Self-hosted means no per-execution fees and full control.

My go-to stack: Make.com for automation, Twilio for SMS ($0.0079/message), OpenAI API for intelligence, Calendly for scheduling, whatever CRM the client already uses. $20-50/month total.

How to Price It

Don't charge hourly. A 4-8 hour build delivers value for years.

Setup: $1,500 - $5,000. Lower end for simple SMS responders, higher for multi-channel AI agents.

Monthly retainer: $300 - $1,000. Monitoring, updates, optimizations, support hours included.

How to justify it: Ask the client: "How many leads do you get per month? What's each job worth?" If a roofer closes 2 extra jobs at $5,000 each because they responded first, that's $10,000 in new revenue. Your $500/month retainer is a 20x return. Let them do the math. The price sells itself.

Where to Find Clients

Local service businesses. Contractors, plumbers, HVAC, landscapers. They're on job sites. They can't answer the phone. Google "[service] + [city]", submit test inquiries, track response times. Anyone over an hour needs you.

Real estate agents. Buyer leads go cold in minutes. First agent to respond wins. Find them on LinkedIn, local Facebook groups, Zillow profiles.

Agencies that resell. Marketing agencies and web designers can white-label your work for their clients. One partnership can mean 10+ builds.

The Fastest Path to Client #1

1. Pick one niche (contractors are easiest)
2. Find 10 businesses with contact forms
3. Submit inquiries, track response times
4. Email the slowest ones: "I submitted an inquiry 3 days ago. Never heard back. How many leads like me do you lose each month? I can fix that."
5. Build the first one free for a testimonial
6. Use that case study to sell the next 10

An SMS that goes out in 60 seconds beats a complex multi-channel system that takes 3 weeks to ship. Start simple. Build one this weekend. Sell it next week. Repeat.

PS: This is one of MANY agents you can build then sell. I made a custom GPT that interviews you for 3 minutes then hands you a blueprint for your first (or next) agent.

You can access the GPT for free here.

I have an AI agent named Claire that runs on OpenClaw. She lives on my Mac mini, monitors my Discord, and handles the entire content engine that's generating 20 million views per month on X.

Here's every piece of the system, broken down so you can build your own.

PS: This entire system is built on one foundation: a Brand Voice skill for your agent. With it, every tweet, article, and email sounds like you.

I'm giving away the exact skill template I use. Drop it in, your agent interviews you for 5 minutes, done. Grab it free here: https://corey-ganim.kit.com/a65257ea2c

The Architecture: One Agent, Multiple Skills

OpenClaw is an open-source AI agent platform. Think of it as the operating system for your AI assistant. Claire runs 24/7 on my machine, connected to Discord, Gmail, Google Calendar, and my file system.

The real power comes from skills. Skills are markdown instruction files that teach the agent exactly how to do a specific task. Not vague prompts. Detailed, step-by-step workflows with formatting rules, voice guidelines, and quality checks.

Here are the skills that power my X content:

• x-article-creation - end-to-end X article writer (body, intro, headlines, thumbnails)
• youtube-video-promotion - turns podcast transcripts into tactical promo tweets
• tweet-cta - generates CTAs that drive waitlist/community signups
• last30days - researches any topic across Reddit, X, and the web from the past 30 days
• brand-voice - my full voice profile so every piece of content sounds like me

Each skill is a file. I drop it into Claire's workspace and she knows how to use it. No coding or messing around with APIs. Just simple markdown files.

The Quote Tweet Machine

Quote tweets are the highest-leverage content format on X right now. Here's why: you're borrowing someone else's audience AND adding original value on top.

My process:

I scroll X for 5-10 minutes and find tweets that are getting traction in the AI/business space. Could be a viral thread, a tool announcement, a hot take. I copy the URL and send it to Claire in Discord:

"Draft a tactical quote tweet breaking down how someone could turn this into a business."

Or: "Draft a quote tweet with our take on this."

Claire reads the original tweet (she has browser access), pulls the key insight, and drafts a response that adds a layer of tactical value. Not just "great tweet." Not just a reaction. A numbered breakdown, a contrarian take, or a step-by-step playbook.

The format that's working best right now:

[bold claim or hook tied to the original tweet]

[numbered tactical breakdown, 3-7 steps]

[specific numbers, pricing, or examples]

One quote tweet I posted recently broke down how to turn a Kevin O'Leary clip about customer acquisition into a $250-500K/yr AI business. It hit 471K views and 7,600 bookmarks.

The key: make the quote tweet valuable on its own. If someone never clicks the original, they should still learn something.

Quote Tweets + Short Video (The Emerging Format)

The next evolution is attaching a short video to your quote tweet. This is starting to outperform text-only quote tweets because the video autoplays in the feed and stops the scroll.

The formula:

1. Find a viral tweet in your niche
2. Find a 10-30 second meme video that viewers will recognize
3. Quote the tweet with the video attached
4. Keep the text short (1-3 sentences max since the video does the heavy lifting)

This works because you're combining two attention signals: the social proof of the original tweet's engagement plus video autoplay. X's algorithm favors video content, and when you layer that on top of an already-viral tweet, the distribution compounds.

I'm leaning into this format more and expect it to become the dominant play in Q2 2026.

The X Article Workflow

X articles are long-form posts that live natively on X (you're reading one right now). They get distributed in the feed like regular tweets but can be 700-1000+ words.

My skill handles the entire workflow in one shot:

Step 1: Topic. I either give Claire a specific topic or she researches what's trending using the last30days skill, which pulls real conversations from Reddit, X, and the web.

Step 2: Article body. 700-1000 words, tactical format. Numbered list of 5-10 items, each with a title, explanation, and specific details. Every section should be something the reader can implement.

Step 3: Intro + CTA. A hook that creates a curiosity gap, 1-2 sentences of context, and a call-to-action linking to a lead magnet.

Step 4: Headlines. Claire generates 7 headline options using proven patterns. Numbers, parenthetical hooks, curiosity gaps, specific time/money claims. I pick the one that feels right.

Step 5: Thumbnails. She builds 3 different thumbnail designs as HTML files, renders them with headless Chrome at exactly 1250x500 (the required 5:2 ratio for X articles), and delivers the PNGs.

The whole thing, from topic to publishable article with thumbnails, takes about 10 minutes of my active time. Most of that is reviewing and picking headlines.

YouTube to X Pipeline

Every podcast episode becomes X content. After I record with a guest, I send Claire the transcript and she extracts the single most tactical insight and writes a promo tweet.

The format:

[bold claim or hook from the episode]

[step-by-step breakdown of what the guest showed]

[YouTube link]

The rule: lead with the tactic, not the video announcement. "New episode dropped!" gets ignored. "Most small businesses pay $5-10K/month for a marketing agency. We replaced the entire stack in one session with Claude Code" gets bookmarked.

This skill alone has driven thousands of views to episodes that would have gotten a fraction of that with a standard promo post.

The CTA System

Every high-performing tweet gets a reply with a CTA (call to action). I have a dedicated skill for this that:

1. Reads the original tweet content
2. Bridges the topic to a relevant offer (community, quiz, waitlist)
3. Drafts 2-3 CTA options with different angles and lengths
4. Uses proven benefit phrases I've tested and rotated

The CTAs always connect the tweet topic to a specific outcome. If the tweet is about replacing SaaS tools, the CTA mentions building AI alternatives. If it's about a workflow, the CTA points to the community where I teach that exact workflow.

This isn't "follow for more." It's "here's the next step if you want to actually do this."

The Research Layer

The last30days skill is the foundation for everything. Before writing an article, I can research what people are actually talking about across Reddit, X, and the web in the past 30 days.

(you can grab the last30days skill for free on Github: https://github.com/mvanhorn/last30days-skill_)

It returns specific posts, engagement numbers, and patterns. Not generic summaries. Actual threads with upvote counts, tweets with like counts, and blog posts. Claire synthesizes all of it and identifies the 3-5 strongest signals.

This means every article I write is grounded in what the audience cares about right now. Not what I think they care about. What they're actually discussing, bookmarking, and sharing.

What Nobody Tells You About This System

It compounds. Every skill I build makes Claire better. The brand voice profile means she sounds like me from day one. The CTA examples file gets updated every time we find a new format that works. The article skill gets refined every time I give feedback.

It's fast. I can produce 3-5 pieces of high-quality content per day with maybe 30-45 minutes of active work. The rest is Claire executing skills while I do other things.

It's consistent. Claire doesn't have off days. She doesn't forget the formatting rules. She doesn't drift from my voice. The skills enforce quality every time.

It's portable. Every skill is a markdown file. I can share them, sell them, or hand them to a teammate.

The Numbers

20 million views per month. Growing. And the content quality keeps improving because the skills keep getting refined.

The setup cost: one OpenClaw instance running on a Mac mini, a Claude subscription, and the time I invested building the skills.

The ongoing cost: 30-45 minutes per day of reviewing drafts and picking the best options.

If you're posting on X and not using an agent with skills to handle the research, writing, and formatting, you're leaving views on the table because AI removes the friction between having a good idea and actually publishing it.

PS: This entire system is built on one foundation: a Brand Voice skill for your agent. With it, every tweet, article, and email sounds like you.

I'm giving away the exact skill template I use. Drop it in, your agent interviews you for 5 minutes, done. Grab it free here:

https://corey-ganim.kit.com/a65257ea2c

They install plugins, run a few prompts, get mediocre results, and assume Cowork is overhyped.

The problem is with their order of operations.

I found the ideal Cowork setup sequence that actually sticks. Get these 7 things right, in this order, and Cowork goes from "interesting chatbot" to "I can't run my business without this."

Context files before plugins

Everyone starts with plugins. That's backwards.

Cowork doesn't know who you are, what you do, or how you communicate. Without context, every plugin output is generic slop.

Create three markdown files first:

• about-me.md — name, role, company, communication style, timezone
• brand-voice.md — words you use, words you avoid, example sentences that sound like you, topics you cover
• current-projects.md — active work with deadlines, blockers, and links

Point Cowork to a folder with these files inside. Now every response is personalized before you install a single plugin.

Time investment: 30 minutes. Payoff: every interaction from this point forward.

The meta-prompt that prevents 90% of mistakes

Before running any task, prime Cowork with this:

"You are my executive assistant. You have access to my computer and 
all connected tools. Always show me your plan before executing. Ask 
clarifying questions if anything is ambiguous. Never delete, move, or 
modify files without explicit approval."

This one paragraph stops Cowork from going rogue on your filesystem. It also forces plan-first execution, which means you catch errors before they happen instead of after.

Save this as a global instruction so you never have to type it again.

One workflow, not five

The mistake I see every week: someone installs Cowork and tries to automate everything on day one. They build a morning dashboard, a meeting prep workflow, a content system, and an email summarizer all in the same afternoon.

Two days later they've abandoned all of them.

Pick ONE recurring task that takes you 20+ minutes and build that workflow first. Run it for a full week. Refine it. Then add the second one.

My recommendation for your first workflow: meeting prep.

"I have a meeting with [NAME] from [COMPANY] in 30 minutes. 
Research them, find recent news, pull any previous email 
conversations, and give me a one-page briefing."

This one workflow saved me more time than the other four combined. And it's the one that makes you look prepared in every call without doing any of the prep yourself.

The folder structure that scales

Flat folders break at scale. Use this structure:

cowork-workspace/
├── context/ (about-me, brand-voice, projects)
├── successful-examples/ (your best emails, posts, proposals)
├── current-tasks/ (active deliverables)
└── references/ (SOPs, style guides, templates)

The "successful-examples" folder is the one most people miss. Drop your best 5-10 emails, your highest-performing LinkedIn posts, your best client proposals. Cowork reverse-engineers your wins and patterns instead of guessing your style.

Plugins in priority order

Now that your context is set, plugins actually work. Install in this order:

1. Productivity — task management, scheduling, workflow automation. Foundation layer.
2. Your industry-specific plugin — marketing, sales, data, or whatever matches your daily work.
3. A custom plugin you build yourself — tell Cowork "I want to create a plugin for [your most repetitive task]. Interview me about the workflow, then build the plugin file." This takes 15 minutes and saves hours.

Skip everything else until these three are running smoothly.

Scheduled tasks (huge unlock)

Plugins and workflows run when you tell them to. Scheduled tasks run whether you remember or not.

Start with one: a daily morning briefing that checks your calendar, flags important emails, and lists your top priorities.

Set it to run at 6am. By the time you sit down with coffee, your day is already organized. You didn't open Gmail. You didn't check Slack. Cowork already triaged everything.

This is where Cowork stops being a tool and starts being an employee.

The review loop that makes it better

Here's what separates people who use Cowork for a week from people who use it permanently:

Every Friday, spend 10 minutes reviewing what worked and what didn't. Update your context files. Tweak your workflows. Add new examples to your successful-examples folder.

Cowork gets better the more it knows about you. But it only learns if you feed it.

Bottom line

The prompting era is over. We're in the context era now.

The difference between "Cowork is a toy" and "Cowork runs half my business" is 30 minutes of setup done in the right order.

Context files first. Then the meta-prompt. Then one workflow. Then plugins. Then scheduled tasks.

Stop installing plugins and hoping for the best. Build the foundation and everything else works.

The problem is simple:

Out of the box, Claude doesn't know anything about you. It doesn't know your business, your tools, your communication style, or your workflows. Every session starts from zero.

A proper onboarding fixes that permanently.

This is the exact 5-step onboarding process I use to turn Claude from a generic chatbot into a personalized AI employee. Takes about 30 minutes if you do it manually.

Or under 10 minutes if you use the plugin we built. More on that at the end.

-----------------------------------------

Step 1: Connect Your Tools First

This is where most people mess up. They start writing instructions for Claude before connecting any of their platforms.

Connect your tools BEFORE doing anything else. Why? Because when Claude can access your Google Drive, Notion, or Slack, it can pull context from your existing documents during setup. Less typing for you, better results from Claude.

How to do it:

Open Claude Desktop. Go to the connectors section. Search for the tools you use daily:

• Google Workspace (Drive, Gmail, Calendar) - this is the big one
• Slack - if your team communicates here
• Notion - if your knowledge base lives here
• Project management (Asana, Linear, Monday) - if applicable

Click connect. Authorize. Done.

Quick test: After connecting Google Drive, ask Claude to search for a recent document. If it finds it, you're good.

No connector available? Some tools don't have direct integrations yet. Workaround example: if you sync Calendly to Google Calendar, Claude can access your schedule through the Google Calendar connector.

Step 2: Create Your Context Files

This is the step that transforms Claude from generic to personal.

Context files are documents that tell Claude who you are, how you communicate, and how you work. They load automatically every session. Three files, that's it.

File 1: about-me.md

Answer these questions:

• What do you do? (one sentence)
• What businesses or projects are you working on?
• What's your professional background?
• Who do you serve?
• What tools do you use daily?

Claude can actually help you write this. If you connected Google Drive in Step 1, ask Claude to scan your existing documents for company info, bios, and about pages. It'll draft most of this for you.

File 2: brand-voice.md

This is how Claude learns to sound like you, not a robot.

• Are you direct and no-nonsense? Warm and conversational? Academic and detailed?
• Do you communicate differently with clients vs. your team?
• What language do you hate? (corporate buzzwords, passive voice, emoji overload)

If you have existing writing samples (emails, social posts, blog content), point Claude to them. It'll extract your patterns automatically.

File 3: working-style.md

This file tells Claude HOW to work with you:

• Should it ask clarifying questions first, or just go?
• What file formats do you prefer?
• How detailed should responses be?
• Any hard rules? (e.g., "never use em dashes," "always show your reasoning")

Where to save them: Create a dedicated folder (name it anything: "ClaudeContext," "AI-Setup," your business name). Select this folder at the start of every Cowork session. That's how Claude loads your context automatically.

Step 3: Set Your Global Instructions

Global instructions are the "always-on" rules Claude follows in every single session, even before you select a folder.

Think of it as your executive brief. Take everything from your three context files and condense it into ~800 words.

Structure:

• Who I am (2-3 sentences)
• How I work (task approach, communication preferences)
• Output defaults (format by task type)
• Voice/tone (key characteristics)
• Key business context
• Rules (always do / never do)

How to activate: Open Claude Desktop → Settings → Cowork → click "Edit" next to Global Instructions → paste → save.

Now every session starts with Claude already knowing your name, your business, your preferences, and your rules. No more re-explaining yourself.

Step 4: Install Your Skills

Skills are pre-built capabilities that make Claude actually useful for specific tasks.

Out of the box, Claude can chat. With skills installed, it can:

• Research and profile potential partners
• Draft content in your voice
• Prep for meetings by scanning attendees' LinkedIn profiles
• Generate reports from your data
• Run multi-step workflows

How to find them: Use the plugin search inside Cowork. Search for keywords related to your work (content creation, sales, research, project management).

Pick 3-5 to start. Don't overload. Install the ones that match your most repetitive tasks. You can always add more later.

No skill for your workflow? You can create custom ones. That's a topic for another article, but just know the option exists.

Step 5: Set Up Scheduled Tasks

This is where Claude goes from assistant to employee.

Scheduled tasks run automatically on a schedule you set. No prompting required. Claude just does the work.

Common examples:

• Every morning at 8 AM: Check your inbox, flag urgent items, summarize what needs attention
• Every Monday: Generate a weekly summary of completed tasks
• Every day at 6 PM: Scan your calendar for tomorrow, prep briefs for any new meetings

How to set them up:

Ask yourself three questions:

1. What do I do every day/week that's repetitive?
2. What do I always forget to do?
3. What would save me the most time if it happened automatically?

Create the task in Cowork with a plain-language schedule ("Every weekday at 8 AM") and a detailed prompt describing exactly what you want done. The more specific the prompt, the better the output.

Start with one. Get it right. Then add more.

The 30-Minute Setup (Or 10 Minutes With Our Plugin)

Here's the full sequence:

1. Connect tools (5 minutes) - Google Workspace + whatever else you use
2. Create context files (10 minutes) - Let Claude draft them from your existing docs
3. Set global instructions (5 minutes) - Condense context files into the always-on brief
4. Install skills (5 minutes) - Pick 3-5 that match your workflow
5. Create first scheduled task (5 minutes) - Start with a morning briefing

That's it. 30 minutes and Claude goes from "generic chatbot" to "knows your business, your voice, your tools, and runs tasks while you sleep."

So here's the full breakdown: 7 custom plugins you could build and sell for recurring revenue, plus 3 ways to monetize the plugins Anthropic already built for you.

No code required for most of these. Just Claude, a clear use case, and a target customer.

Part 1: 7 Plugins You Could Build and Sell

1. Client Onboarding Plugin

What it does: Walks a new client through a structured intake process. Asks the right questions, generates a project brief, creates deliverable timelines, and saves everything to a shared folder.

Who buys it: Agencies, consultants, freelancers who onboard 5+ clients per month.

How to price it: $500-1,500 one-time setup + $200/month maintenance.

Why it works: Every agency has an onboarding process. Most of them are messy. This standardizes it and saves 2-3 hours per client.

2. Weekly Report Generator Plugin

What it does: Pulls data from connected tools (Google Sheets, project management platforms, email), summarizes key metrics, and generates a formatted weekly report ready to send to clients or stakeholders.

Who buys it: Agency owners, consultants who send regular client updates, operations managers.

How to price it: $1,000-2,500 setup + $300/month.

Why it works: Nobody likes writing weekly reports. Everyone needs them. This plugin turns a 2-hour task into a 2-minute review.

3. Content Repurposing Plugin

What it does: Takes one piece of long-form content (podcast transcript, blog post, YouTube video) and generates 10+ pieces of short-form content: tweets, LinkedIn posts, email newsletters, Instagram captions, all in the user's voice.

Who buys it: Creators, coaches, solopreneurs who publish content regularly.

How to price it: $1,500-3,000 setup + $500/month.

Why it works: Content repurposing is the #1 bottleneck for creators. They have the content, they just don't have time to chop it up. This does it in minutes.

4. Client Renewal & Upsell Intelligence Plugin

What it does: Monitors your existing client base (contracts, invoices, communication history, project timelines) and flags when a client is approaching renewal, hasn't been contacted in X days, or has signals they're ready for an upsell. Generates a personalized re-engagement message or upsell pitch based on their history with you.

Who buys it: Agencies, consultants, coaches, anyone with recurring clients.

How to price it: $2,000-3,000 setup + $400/month.

Why it works: Most service businesses lose revenue from forgetting to follow up. This plugin analyzes your specific client relationships, your services, and your pricing tiers and tells you what to do next.

5. Proposal/SOW Generator Plugin

What it does: Based on a short conversation or intake form, generates a professionally formatted proposal or statement of work. Includes scope, timeline, pricing, terms, and deliverables.

Who buys it: Freelancers, agencies, consultants who write 3+ proposals per month.

How to price it: $1,500-2,500 setup + $300/month.

Why it works: Proposals take hours to write. Most of the content is repetitive across clients. This cuts proposal creation from hours to minutes and keeps quality consistent.

6. Customer Support Knowledge Base Plugin

What it does: Ingests a company's FAQ, documentation, and past support tickets. When a support question comes in, it drafts a response using the company's actual knowledge base and tone.

Who buys it: E-commerce brands, SaaS companies, any business with a support inbox.

How to price it: $2,000-5,000 setup + $500/month.

Why it works: Support is expensive. This doesn't replace the team, but it drafts 80% of responses so they just review and send. Cuts response time in half.

7. Lead Research and Outreach Plugin

What it does: Takes a target company or contact, researches them across LinkedIn, their website, recent news, and social media. Generates a personalized outreach message or email based on specific angles relevant to their business.

Who buys it: Sales teams, BDRs, agency owners doing outbound.

How to price it: $2,000-3,000 setup + $400/month.

Why it works: Personalized outreach gets 3-5x the response rate of templates. But it takes 15-20 minutes per prospect to do manually. This does it in 30 seconds.

Part 2: How to Monetize Plugins That Already Exist

You don't have to build everything from scratch. Anthropic has a growing library of official plugins that anyone can use.

The opportunity: Most business owners don't know they exist, let alone how to configure them.

Option 1: Done-For-You Plugin Setup Service

The play: Charge $2,000-5,000 to audit a client's business, recommend the right combination of official plugins, install and configure them, create their context files, and set up scheduled tasks.

This is the highest-margin option because the plugins are free. You're selling the expertise and configuration, not the software.

Package it as: "AI Employee Setup" or "Claude Business Configuration." Run it as a productized service. Same process every time, different clients.

Option 2: Plugin Training and Workshops

The play: Run a paid workshop ($200-500 per person) teaching business owners how to use specific plugins. Focus on one niche: "Claude for Real Estate Agents," "Claude for E-commerce Operators," "Claude for Consultants."

Niche down hard. The more specific the workshop, the higher the perceived value.

Scale it: Record the workshop once. Sell access as a $99-199 course. Run live Q&A monthly as an upsell.

Option 3: Plugin + Skill Bundles

The play: Take an official plugin, pair it with custom skills you build, and sell the combination as a complete solution.

Example: Take the Google Workspace plugin (free), add a custom meeting prep skill + a weekly report skill + a client onboarding skill. Sell the bundle as a "Consulting Operations Kit" for $3,000.

The official plugin does the heavy lifting. Your custom skills make it specific to the client's industry. Together they're worth far more than either piece alone.

The Math

If you build one custom plugin and sell it to 10 clients at $2,000 setup + $300/month:

• Upfront: $20,000
• Monthly recurring: $3,000/month ($36,000/year)
• Year one total: $56,000 from one plugin

Build two or three plugins for different niches and you have a real business.

The window is wide open right now. Most business owners know AI exists but have no idea how to implement it. That gap is your opportunity.

Most OpenClaw users install it, connect a channel, and never open the workspace folder.

This is a mistake.

Your workspace is the control center for how your AI assistant behaves. It holds your identity, custom skills, channel connections, and memory. Once you understand what lives where, you can configure OpenClaw exactly the way you need it.

This guide walks through the anatomy of a perfect setup.

Two config layers, not one

Before diving in, one thing worth knowing upfront: OpenClaw uses two configuration layers.

The first is your workspace folder (typically ~/clawd/ or wherever you initialized). This holds your personal configuration, skills, memory, and secrets.

The second is the global installation at opt/homebrew/lib/node_modules/clawdbot/ (or equivalent on your system). This contains the core docs, built-in skills, and default behaviors.

Your workspace is where you customize. The global install is what you update.

AGENTS.md: Your operating instructions

This is the most important file in your entire setup. When OpenClaw starts a session, the first thing it reads is AGENTS.md. It loads it straight into the system prompt and keeps it in mind for the entire conversation.

Simply put: whatever you write in AGENTS.md, your agent will follow.

If you tell it to always check your calendar before suggesting meeting times, it will. If you say "never send emails without my explicit approval," it will respect that every time.

What actually belongs in AGENTS.md:

Write:

• Your core operating directive (what's the agent's job?)
• Key tools and how to use them (email CLI, task management workflows)
• Hard rules that should never be broken
• Links to important resources (API docs, skill locations)
• Workflow triggers (what should happen automatically)

Don't write:

• Full documentation you can link to instead
• Long paragraphs explaining theory
• Information that changes frequently

Keep AGENTS.md under 300 lines. Files longer than that start eating too much context, and instruction adherence actually drops.

Here's a minimal but effective example:

# AGENTS.md - Clawdbot Workspace

## Core Operating Directive
My assistant operates as a proactive helper for my business.

**The Mandate:**
- Take as much off my plate as possible
- Be proactive - don't wait to be asked

## Key Tools
- **Email:** Check inbox daily, flag urgent items
- **Calendar:** Verify no conflicts before scheduling
- **Task Manager:** Sync completed work to dashboard

## Hard Rules
- NEVER restart services without explicit permission
- Always verify dates with `date` command before scheduling
- Log all completed work to tracking system

That's ~20 lines of core directives. It gives your agent everything it needs to work productively without constant clarification.

SOUL.md: Persona and boundaries

SOUL.md defines who your agent is and how it should communicate. This is where personality lives.

# SOUL.md - Persona & Boundaries

- Keep replies concise and direct.
- Ask clarifying questions when needed.
- Never send streaming/partial replies to external messaging surfaces.

If AGENTS.md is the instruction manual, SOUL.md is the personality profile. Some users write extensive personas here. Others keep it minimal. Both work, it depends on how much personality customization you want.

IDENTITY.md and USER.md: Who's who

These two files establish the relationship between your agent and you.

IDENTITY.md defines your agent:

# IDENTITY.md - Agent Identity

- Name: [Your Agent Name]
- Creature: [Optional: fun descriptor]
- Vibe: Clever, helpful, dependable, tenacious problem-solver
- Emoji: [Your chosen emoji]

USER.md defines you:

# USER.md - User Profile

- Name: [Your Name]
- Preferred address: [Nickname]
- Timezone: [Your timezone]
- Phone: [Your number]
- Calendar: [Calendar platform and email]

Why separate files? Because they serve different purposes. IDENTITY.md rarely changes once set. USER.md updates as your preferences evolve. Keeping them separate makes maintenance easier.

HEARTBEAT.md: Scheduled check-ins

This file controls what your agent does during heartbeat polls—scheduled moments when the system checks if anything needs attention.

# HEARTBEAT.md

**If the message contains "⚠️ EXECUTION CRON":**
- This is NOT a heartbeat check - it's work to do
- Read the complete instructions in the message
- Execute all required steps
- Never return HEARTBEAT_OK

**Otherwise:**
- If nothing needs attention, return HEARTBEAT_OK
- Keep it minimal

Heartbeats are powerful for creating proactive agents. Your agent can check your calendar, scan your inbox, or run any automated workflow on a schedule, all triggered by heartbeat polls.

TOOLS.md: External tool documentation

This file holds your notes about external tools and conventions. It doesn't define which tools exist (OpenClaw handles that internally), but it's where you document how YOU use them.

# TOOLS.md - User Tool Notes

## Email CLI
- Account: [email protected]
- Common: `cli email search`, `cli calendar create`

## Task Management API
- Token location: ~/.secrets/task-api-token.txt
- Always use `limit=100` to avoid pagination issues
- Status conversion: [document your specific workflow]

## Web scraping tool (when sites block normal requests)
- Location: ~/clawd/.venv/scraper
- Use stealth mode for protected sites

This becomes your personal reference guide. When your agent needs to use a tool, it checks TOOLS.md for your specific conventions.

The skills/ folder: Reusable workflows

Skills are where OpenClaw gets powerful. Each skill is a self-contained workflow that your agent can invoke when the task matches the skill's description.

Every skill lives in its own subdirectory with a SKILL.md file:

skills/
├── meeting-prep/
│   └── SKILL.md
├── social-post-writer/
│   ├── SKILL.md
│   └── references/
│       └── templates.md
└── podcast-show-notes/
    └── SKILL.md

A SKILL.md uses YAML frontmatter to describe when to use it:

---
name: meeting-prep
description: Automated pre-call research and briefing. Use when user asks to check for upcoming meetings or run meeting prep.
---

# Meeting Prep Skill

## When to trigger:
- User asks to check for upcoming meetings
- Scheduled to run every 10 minutes via cron
- Only briefs for FIRST-TIME CALLS

## Process:
1. Check calendar for meetings in next 15-45 minutes
2. Research attendees (LinkedIn, company website)
3. Create briefing document
4. Attach link to calendar event

The key difference from static instructions: skills can bundle supporting files alongside them. The references/templates.md file can hold example outputs, proven formats, or additional context that only loads when the skill is active.

The memory/ folder: Persistent context

This is your agent's long-term memory. Session transcripts, learned preferences, and daily logs all live here.

memory/
├── 2026-03-22.md
├── 2026-03-21.md
├── email-style.md
├── business-context-2026-02.md
└── workflow-playbook.md

Good practices:

• Keep daily logs at memory/YYYY-MM-DD.md
• On session start, your agent reads today + yesterday if present
• Capture durable facts, preferences, and decisions
• Avoid storing secrets

Memory files are what give your agent context across sessions. Without them, every conversation starts from zero.

The .secrets/ folder: Credentials

API tokens, passwords, and sensitive configuration live here. This folder should be gitignored and never committed.

.secrets/
├── task-api-token.txt
├── openai-api-key.txt
├── anthropic-api-key.txt
└── service-credentials.txt

Your AGENTS.md or TOOLS.md can reference these paths without exposing the actual values.

Channel configuration: Where you talk to your agent

OpenClaw supports multiple messaging channels: Discord, Telegram, Signal, WhatsApp, and more. Channel config lives in your gateway configuration (managed via clawdbot gateway commands).

Key concepts:

• Each channel has its own session behavior
• You can have multiple channels active simultaneously
• Channel-specific rules (like "don't respond unless @mentioned in Discord") go in AGENTS.md

The full picture

Here's how everything comes together:

~/clawd/
├── AGENTS.md           # Operating instructions (daily use)
├── SOUL.md             # Persona and boundaries
├── IDENTITY.md         # Agent identity
├── USER.md             # Your profile
├── HEARTBEAT.md        # Scheduled check-in behavior
├── TOOLS.md            # External tool documentation
├── BOOTSTRAP.md        # First-run ritual (delete after)
│
├── skills/             # Custom workflows
│   ├── meeting-prep/
│   ├── podcast-show-notes/
│   └── social-post-writer/
│
├── memory/             # Persistent context
│   ├── 2026-03-22.md
│   └── key-context.md
│
├── .secrets/           # API keys and credentials (gitignored)
│   ├── task-api-token.txt
│   └── anthropic-api-key.txt
│
└── output/             # Generated files and exports

A practical setup to get started

If you're starting from scratch, here's a progression that works well.

Step 1. Run openclaw onboard and complete the initial setup. Choose your model provider and connect at least one channel.

Step 2. Create your workspace folder and add AGENTS.md with your core directives. Start with 10-20 lines covering your main use cases.

Step 3. Add IDENTITY.md, USER.md, and SOUL.md. These establish the relationship and personality.

Step 4. Create your first skill for a workflow you do repeatedly. Meeting prep, email drafts, or content creation are good starting points.

Step 5. Set up memory/ with a daily log template. Your agent will start building context over time.

Step 6. Add TOOLS.md as you discover tool-specific patterns worth documenting.

That's genuinely all you need for 95% of use cases. Advanced features like cron jobs, multi-agent setups, and custom channel plugins come in when you have specific workflows worth automating.

The key insight

Your OpenClaw workspace is really a protocol for telling your AI assistant who you are, what you need done, and what rules it should follow. The more clearly you define that, the less time you spend correcting your agent and the more time it spends doing useful work.

AGENTS.md is your highest-leverage file. Get that right first. Everything else is optimization.

Start small, refine as you go, and treat it like any other piece of infrastructure in your workflow: something that pays dividends every day once it's set up properly.

Almost no one is using them correctly.

Plugins are what turn Claude from a chatbot into a specialist for your specific role.

They include commands that trigger entire workflows (research, formatting, delivery), and hands you back the finished work.

This article breaks down exactly how plugins work, how they differ from Skills, and gives you 5 copy-paste prompts to build your own.

What Plugins Actually Are

A plugin is a bundle. It packages four things together:

• Skills — instructions that teach Claude how to do specific tasks
• Connectors — links to external tools (Google Drive, Slack, Notion, etc.)
• Slash commands — quick actions you trigger with /command
• Sub-agents — parallel workers that handle pieces of a task

Instead of setting up each piece individually, you install one plugin and get a ready-to-go setup from the first conversation.

Think of it this way: skills are the individual capabilities. Plugins are the job description that bundles the right skills together.

How Plugins Differ from Skills

This is where most people get confused.

Skills are single-purpose instructions. One skill might teach Claude how to write in your voice. Another might teach it how to format a proposal. You can use skills without a plugin.

Plugins bundle multiple skills together with the connectors and commands needed to run a complete workflow.

Example:

• Skill: "Write status update in my tone"
• Plugin: Pulls active projects from Notion, writes status updates for each client using your communication skill, and drops the drafts into Gmail. One command, five clients handled.

Skills are Lego blocks. Plugins are the assembled set.

The 11 Official Anthropic Plugins

Anthropic shipped these on January 30, 2026. All free, all open source:

1. Sales — call prep, competitive research, account plans
2. Marketing — SEO audits, email sequences, content briefs
3. Legal — contract review, compliance checklists, risk analysis
4. Finance — financial modeling, budget analysis, forecasting
5. Customer Support — ticket response, escalation workflows
6. Product Management — PRDs, roadmap planning, user research
7. Data Analysis — dashboards, data cleaning, visualization
8. Enterprise Search — cross-platform search across company tools
9. Biology Research — literature review, experiment design
10. Productivity — task management, meeting prep, scheduling
11. Plugin Create — build your own plugins from scratch

Browse them all at github.com/anthropics/knowledge-work-plugins

How to Install a Plugin

Open Cowork → Plugins (sidebar) → Click "+" → Browse available plugins → Click "Add plugin"

That's it. Plugins you add are saved locally to your machine.

To use a skill from your plugin, type "/" in the chat to see available commands, or click the "+" button and select from your installed plugins.

The Most Useful Slash Commands

Here are commands worth bookmarking from the official plugins:

Marketing Plugin:

• /marketing:seo-audit — Analyzes any URL for SEO issues
• /marketing:email-sequence — Generates a multi-email sequence
• /marketing:competitive-brief — Researches competitors and outputs a brief

Sales Plugin:

• /sales:call-prep — Researches a prospect and creates a call brief
• /sales:account-plan — Builds a strategic account plan
• /sales:objection-handling — Generates responses to common objections

Legal Plugin:

• /legal:contract-review — Analyzes a contract for risks
• /legal:compliance-check — Checks against regulatory requirements

Data Plugin:

• /data:clean-dataset — Standardizes messy data
• /data:visualize — Creates charts from data

How Connectors Work

Connectors link Claude to external tools. Without them, Claude can only work with what you paste in. With them, Claude can pull live data.

Available connectors include:

• Google Drive, Gmail, Google Calendar
• Slack, Notion, Asana
• DocuSign, Salesforce, FactSet
• Linear, Monday.com, and more

To connect: Settings → Connectors → Browse connectors → Search for your tool → Connect

Once connected, Claude can search your Drive, read your emails, check your calendar, and act on that data within a workflow.

How to Customize a Plugin

The default plugins are starting points. Click "Customize" on any installed plugin and Claude walks you through adjusting it:

• Swap connectors to match your tool stack
• Add your company's terminology
• Adjust workflows to how your team operates
• Add custom skills specific to your process

Prompt to use:

Customize this plugin to match how my team actually works. We use 
[tools]. Our process for [workflow] is [description]. Update the skills 
and commands accordingly.

How to Build Your Own Plugin

Use the Plugin Create plugin (meta, I know). Or build from scratch with this structure:

my-plugin/
├── plugin.json          # Manifest file
├── commands/            # Slash commands
│   └── my-command.md
├── skills/              # Individual skills
│   └── my-skill/
│       └── SKILL.md
└── .mcp.json           # Connector config

Prompt to start:

I want to build a plugin for [your workflow]. Walk me through creating 
the plugin structure, skills, and commands. My workflow involves 
[describe the steps].

Copy-Paste Prompts for Common Use Cases

Create a client management plugin:

Build me a client management plugin that pulls active projects from 
Notion, writes weekly status updates using my communication style, 
and drafts them in Gmail. Include a /send-updates command.

Create a content repurposing plugin:

Build a content repurposing plugin with skills for: turning podcast 
transcripts into LinkedIn posts, creating Twitter threads from articles, 
and extracting clips suggestions from video scripts.

Create a research plugin:

Build a competitive research plugin that searches the web, organizes 
findings into a structured brief, and saves it to my Google Drive. 
Include /research-company and /market-analysis commands.

What Plugins Actually Unlock

Here's the main benefit:

Before plugins, you had to manage each piece manually. Connect your tools. Load your skills. Remember which prompt to use. Repeat for every task.

With plugins, you set it up once and run it forever. One command triggers an entire agentic workflow and hands you back the finished work.

The people who figure this out first will operate like they have a team of 10.

The people who don't will wonder why everyone else is moving so fast.

Join 8,004 others on the waitlist:

But almost no one is using it to its full potential.

I've spent the last few weeks building workflows, testing model routing, and figuring out what actually produces ROI.

I even made a video testing it head to head with Claude Cowork.

Here's the complete starter pack. Everything you need to master this incredible technology (no coding required).

Understand What You're Working With

The 19-Model Orchestra (Know Your Players)

Computer doesn't use one AI model. It uses 19+, automatically routing tasks to the right one.

The lineup:

• Claude Opus 4.6 — Core reasoning engine. Complex problem-solving.
• Gemini — Deep research, creates sub-agents for parallel work
• ChatGPT 5.2 — Long-context recall, broad search
• Grok — Speed tasks, lightweight queries
• Nano Banana — Image generation
• Veo 3.1 — Video production

You don't pick the model. Computer picks for you based on the task. This is the magic: no more jumping between Claude and ChatGPT and Gemini trying to find who does what best.

The Sub-Agent Architecture (Why It's Different)

When you give Computer a task, it doesn't process linearly. It breaks your outcome into subtasks and spins up specialized sub-agents to handle each part.

Example: "Research my competitors and create a presentation"

• Agent 1: Web research on competitors
• Agent 2: Drafts slides
• Agent 3: Formats output
• Agent 4: Delivers to your team

All running in parallel. While one waits for data, others keep working.

This is why Computer can tackle projects that would take you hours in minutes.

The Credit System (Don't Ignore This)

The economics:

• $200/month for Max subscription
• 10,000 credits included monthly
• 20,000 bonus credits for early adopters (expires in 30 days)
• Standard searches don't consume credits
• Complex tasks consume more credits

Pro tip: Check your balance at perplexity.ai/account/credits. Enable auto-refill to avoid paused tasks.

The Workflows That Actually Produce ROI

Deep Research Workflow

This is Computer's killer feature. Seven search types running in parallel.

Prompt:

Research [TOPIC]. Run parallel searches across web, academic, 
people, image, video, shopping, and social. Read full source pages, not
 just snippets. Cross-reference findings and flag where sources 
disagree. Present as a structured brief with citations.

What makes it special:

• Hits scholarly databases directly
• Reads full pages, not snippets
• Cross-references automatically
• Identifies source disagreements

I use this for market research, competitor analysis, and due diligence. Collapses hours into one prompt.

Build-and-Deploy Workflow

Computer can build micro-apps and push them to your repo in one conversation.

Prompt:

Memorize this brand guidelines file for future use [paste file]. Build me
 an app that [FUNCTION] in my exact brand style, with live preview and
 PNG export. Push to my GitHub repo when complete.

Real example from testing: Went from brand guidelines markdown to two working, deployed tools in under 30 minutes. No designer. No developer handoff.

Data Visualization Workflow

Turn complex data into shareable visuals.

Prompt:

Create an interactive [CHART TYPE] for [DATA SOURCE]. X-axis:
[METRIC 1]. Y-axis: [METRIC 2]. Size/color coded by [METRIC 3]. 
Highlight at least 10 interesting findings. Package as a shareable 
website.

Example: "Create an interactive bubble chart for every company in the S&P 500. X-axis: trailing 12-month revenue. Y-axis: net profit. Bubble size: market cap. Color: % change in share price (green-to-red gradient)."

Computer handles the data gathering, analysis, visualization, and deployment.

Animated Analysis Workflow

Create dynamic content that tells a story.

Prompt:

Create an animated GIF of [DATA] over [TIME PERIOD]. Annotate each 
major inflection point with the event that caused it. Make annotations 
fade in and out as the timeline progresses.

This produces the kind of content that gets saved and shared.

Research-to-Insight Workflow

This gets synthesis as opposed to just gathering data.

Prompt:

Research [TOPIC]. Then answer: What do these sources disagree on? 
Which source is most credible and why? What questions should I be 
asking that I haven't asked yet?

Turns Computer into a research collaborator, not just a search engine.

Context Files That 10x Your Output

Create a brand-guidelines.md File

Computer can memorize your brand and apply it to everything it builds.

Include:

• Logo usage, colors (hex codes), typography
• Voice and tone guidelines
• Example outputs that match your style
• What to avoid

Once uploaded, say "Memorize this brand guidelines file for future use" and every future output will be on-brand.

Create a project-brief.md File

For multi-step projects, give Computer the full context upfront.

Include:

• Project goals and success metrics
• Target audience
• Constraints (budget, timeline, technical limits)
• Reference materials and examples
• Delivery requirements

Computer will reference this throughout the project without you re-explaining.

Connect your tools

Computer connects to 400+ apps. Set these up immediately:

Priority integrations:

• Gmail/Outlook — Email research and sending
• GitHub — Code deployment
• Slack — Team notifications
• Notion — Documentation
• Google Drive — File storage
• Snowflake/Databricks — Data analysis

The more connected Computer is, the more end-to-end your workflows become.

Power User Tips

Run tasks in parallel

Don't wait for one project to finish. Computer can run dozens of tasks simultaneously.

Strategy:

• Start your research task
• While it's running, start your build task
• While those run, set up your deployment

Computer works asynchronously. Use that.

Let long tasks run in the background

Tasks can run for hours, days, or weeks. You don't need to babysit.

Best practice: Frame big projects as outcomes, not steps. Let Computer break it down and execute while you do other work.

Control model selection (when needed)

Computer auto-routes, but you can override.

When to override:

• Specific coding tasks → Request Claude specifically
• Speed-critical tasks → Request Grok
• Visual generation → Request Nano Banana

Most of the time, let the orchestrator decide. Override only when you know better.

Request checkpoints for complex projects

For high-stakes work, ask Computer to pause for review at key moments.

Prompt modifier:

[YOUR TASK]. Pause for my approval before [CRITICAL STEP]. Show 
me your plan before executing.

Keeps you in control without micromanaging every step.

When to Use What

Computer vs Claude Cowork vs OpenClaw

Use Perplexity Computer when:

• You need multi-model orchestration
• Tasks involve parallel research from multiple sources
• You want to build and deploy without a dev environment
• Projects can run asynchronously while you sleep

Use Claude Cowork when:

• Deep document work requiring extensive context
• You need local file access on your machine
• Privacy is critical (runs locally, not cloud)
• You prefer a single-model, consistent experience

Use OpenClaw when:

• You're technical and want maximum flexibility
• You need WhatsApp/iMessage/Slack integrations
• You're comfortable managing security risks
• You want open-source control

I use all three. Cowork for deep work. Computer for research and build. OpenClaw for messaging integrations.

The Bottom Line

Perplexity Computer is not a chatbot. It's a cloud-based team of 19 AI specialists waiting for your instructions.

The difference between "this costs too much" and "this is the best $200 I spend" is setup. Connect your integrations. Create your context files. Frame tasks as outcomes, not steps.

Start with 3 workflows from this list. Master those. Then add more.

Use it like an employee. Give it real work. Let it run. Check the output.

The prompting game is over. The orchestration game is everything.

That's not a thought experiment anymore. People are building AI-only companies right now.

No humans on the org chart. Just AI agents doing research, writing, coding, marketing, and customer support.

The best part is that you don't need to be technical to build one.

This article shows you exactly how it works.

The Concept: An Org Chart Made of AI

Think about how a normal company works. You have a CEO who sets direction. Managers who break that direction into projects. Employees who execute tasks.

Everyone reports to someone.

Now imagine the same structure, but every role is filled by an AI agent.

Your "CEO" agent decides priorities.

Your "marketing" agent writes content.

Your "developer" agent builds features.

Your "analyst" agent reviews what's working.

They all coordinate through one system.

This isn't science fiction anymore. A free tool called Paperclip makes this possible today.

What Paperclip Actually Does

Paperclip turns your AI tools into a coordinated team. If you've ever used ChatGPT, Claude, or any AI assistant, you've worked with individual agents. Paperclip organizes them into a company.

Here's what that means in practice:

1. You create an org chart

Just like a real business, you define roles. CEO, CTO, Content Lead, whatever your company needs. Each role gets a job description and a boss to report to. The structure determines how work flows between agents.

2. You set one company goal

"Build a content marketing agency" or "Launch a SaaS product" or "Create a YouTube channel with 100 videos." That goal breaks into projects. Projects break into tasks. Every agent sees how their work connects to the mission.

3. Agents work on a schedule

They don't run constantly (that would burn money). They wake up on a schedule, check if there's work, do it, and go back to sleep. You control how often each one checks in. Costs stay predictable.

4. Budgets keep you safe

Every agent has a spending limit. When they hit it, they stop. No surprise bills because an AI got stuck in a loop. You see exactly what you're spending in real-time.

5. Everything gets logged

Every task, every decision, every conversation. If something goes wrong, you can trace back exactly what happened. Full accountability.

How to Set This Up (No Coding Required)

The technical version involves terminals and command lines. But here's the non-technical path:

Step 1: Get the right AI subscription

You need an AI that can take actions, not just chat. Claude Pro ($20/month) or ChatGPT Plus ($20/month) with the right tools enabled. This is your workforce.

Step 2: Install Paperclip

If you have a Mac, open Terminal (search for it in Spotlight). If you have Windows, open Command Prompt. Type this and hit enter:

npx paperclipai onboard --yes

That's it. Paperclip installs and opens a dashboard in your browser.

Step 3: Create your first company

The dashboard walks you through it. Name your company. Set your mission. ("Build a content business" or whatever you're building.)

Step 4: Hire your first agent

Start with a CEO. Paperclip will recommend who else to hire based on your goal. You approve each hire and set their budget.

Step 5: Let them work

Assign the first project. Watch the dashboard as tasks get created, assigned, and completed. Check in from your phone if you want.

What You Can Actually Build With This

Here are real use cases people are running:

Content Production Line

• Research agent finds topics
• Writing agent creates drafts
• Editing agent polishes content
• Publishing agent schedules posts
• Analytics agent reports what's working

Automated Agency

• Sales agent qualifies leads
• Project manager assigns work
• Specialist agents execute deliverables
• QA agent reviews output
• Billing agent sends invoices

Product Development

• Product agent specs features
• Developer agent writes code
• Testing agent finds bugs
• Documentation agent writes guides

You define the workflow. The agents execute it.

The Honest Limitations

As with any AI solution there are real limitations to be aware of. Here's what you need to know:

You're still the board of directors. Agents need oversight. You approve major decisions. You step in when things go sideways.

Think of yourself as a manager of managers, not someone who walks away completely.

Quality varies. AI agents work fast but they're not always right. You need review processes.

The ticket system helps, but human judgment still matters for important outputs.

Setup takes real time. That "5-minute setup" gets you running. Building a well-functioning AI company takes iteration. Plan for a learning curve.

Best for repetitive, definable work. Creative strategy, relationship building, novel problem-solving? Still human territory.

But everything systematic and repeatable? That's where AI companies shine.

The Bottom Line

A year ago, "AI employee" was a metaphor. Today it's a job title on an org chart.

Paperclip is free and open source. No subscription. No account required. Your data stays on your machine.

The real question isn't whether this works. It's whether you'll be the one building an AI company, or competing against people who already did.

The setup takes 5 minutes. The first project might take a weekend to dial in. After that, you have a team that never sleeps.

The problem is the setup.

I've spent the last few months building my Cowork environment from scratch. Tested every plugin. Built custom skills. Refined workflows until they produced real ROI.

Here's the complete starter pack. Everything you need to go from "this is interesting" to "this is how I work now."

Install These Plugins ASAP

1. Productivity Plugin (Install First)

This is the foundation. It gives you slash commands for task management, scheduling, and workflow automation.

Key commands:

• /task - Create and track tasks without leaving your conversation
• /schedule - Block time on your calendar directly
• /workflow - Run multi-step automations you've saved

Install it before anything else. Everything else builds on top of this.

2. Marketing Plugin

If you create content, this is non-negotiable. It handles drafting, repurposing, and campaign planning.

What I use it for:

• /draft-content - LinkedIn posts, tweets, newsletter sections
• /repurpose - Turn one piece of content into 5 platform-specific versions
• /campaign - Plan a full content calendar with themes and hooks

The output quality jumps significantly when you pair this with a voice profile file (more on that below).

3. Data Plugin

For anyone working with spreadsheets, reports, or analytics. This connects Cowork to your actual data.

Useful commands:

• /analyze - Upload a CSV and get insights without writing formulas
• /build-dashboard - Create interactive visualizations
• /sql - Generate queries from plain English descriptions

I use this weekly for revenue tracking and client reporting.

4. Sales Plugin (if you do outreach)

Account research, call prep, and outreach drafting. Turns 30 minutes of prep into 3 minutes.

• /research-account - Pull company info, recent news, key contacts
• /prep-call - Get a briefing doc before any meeting
• /draft-outreach - Personalized emails based on prospect data

The Files That Make Cowork Actually Useful

Plugins are just the start. The real power comes from context files. These are markdown files you create that tell Cowork who you are and how you work.

5. Create an about-me.md file

This is your professional identity in a text file.

Include:

• Your name, role, company
• How you prefer to communicate (direct? detailed?)
• Your timezone and working hours
• 2-3 examples of your writing or work style

Cowork reads this before every response. No more explaining who you are.

6. Create a brand-voice.md file

If you create any content, this file is mandatory.

Include:

• Words you use vs. words you avoid
• Tone (casual, professional, bold, supportive)
• Example sentences that sound like you
• Topics you cover and topics you don't

I have a 2,000-word voice profile. Every draft sounds like me without editing.

7. Create a current-projects.md file

A living document of what you're working on right now.

Include:

• Active projects with status
• Key deadlines this week
• Blockers or open questions
• Links to relevant docs or resources

Update this weekly. Cowork becomes an assistant who actually knows what's on your plate.

The Workflows That Save Real Time

8. Morning Dashboard Workflow

Start every day with context, not chaos.

Prompt:

"Open Chrome and set up my morning dashboard. Open Gmail, Google
Calendar (week view), Notion, and Twitter notifications side by side. 
Summarize what's on my calendar today."

Takes 30 seconds instead of 10 minutes of clicking.

9. Meeting Prep Workflow

Never walk into a call cold again.

Prompt:

"I have a meeting with [NAME] from [COMPANY] in 30 minutes. 
Research them: LinkedIn profile, company website, recent news, any 
email history we have. Create a one-page brief with 3 talking points."

This is the workflow that made me stop using ChatGPT for work. The integration with your actual tools changes everything.

10. Content Repurposing Workflow

One piece of content becomes five.

Prompt:

"Read [this YouTube video/podcast/article]. Create: 5 Twitter posts, 1 
LinkedIn post, 1 newsletter draft, and 10 short-form video clip ideas 
with timestamps. Save everything to Google Drive."

I run this every time I publish a podcast episode. Hour of work compressed into 15 minutes.

11. End-of-Day Shutdown Workflow

Close loops and prep for tomorrow.

Prompt:

"Help me shut down for the day. Check my task manager for anything 
urgent I missed. Look at tomorrow's calendar and flag any prep I need. 
Create a sticky note on my desktop with my top 3 priorities for 
tomorrow."

Clean shutdown means you start tomorrow fresh instead of anxious.

The Setup That Ties It All Together

12. Folder Structure

Organize your Cowork context like this:

claude-context/
├── about-me.md
├── brand-voice.md
├── current-projects.md
├── skills/
│ ├── content-repurpose/
│ ├── meeting-prep/
│ └── research-compile/
└── references/
├── writing-samples/
└── templates/

Point Cowork to this folder. Now every conversation has full context.

13. The Meta-Prompt That Makes Everything Better

Before running any workflow, prime Cowork with this:

"You are my executive assistant. You have access to my computer and
 can control apps, browser, and files. When I give you tasks: ask 
clarifying questions if needed, narrate what you're doing, pause before 
any destructive actions (deleting, sending), save important outputs to
Google Docs for my review."

This single prompt prevents 90% of the mistakes people complain about.

The Bottom Line

Cowork is so much more than a chatbot. It's an entire agentic system.

The difference between "this is cool" and "this changed how I work" is setup. Install the right plugins. Create the context files. Build workflows for your actual recurring tasks.

Start with 3 things from this list. Master those. Then add more.

The prompting game is over. The context game is everything.

While everyone's debating which AI is "best," a small group of operators are using Cowork to automate entire workflows that used to take hours.

What separates the true operators from everyone else is they simply know exactly what to ask for.

Here are 10 prompts that do the work for you. Copy them. Paste them. Watch your output multiply.

1. The Morning Dashboard Builder

Prompt:

Open Chrome and create a morning dashboard for me. Open these tabs in order:
1. Gmail inbox
2. Google Calendar (week view)
3. Notion (my workspace)
4. Twitter/X notifications
Arrange them so I can see Calendar and Gmail side by side. Then summarize what's on my calendar for today.

Why it works: Instead of manually opening 6 tabs every morning, Cowork sets up your entire workspace in seconds. The summary at the end means you start with context, not confusion.

2. The Research Compiler

Prompt:

I need to research [TOPIC].
Open 5 browser tabs with the top articles on this topic from the last 30 days. Read each one and create a Google Doc summarizing:
- Key points from each source
- Common themes across all articles
- Contrarian takes or unique angles
- Links to all sources
Save it as "[TOPIC] Research - [Today's Date]"

Why it works: This turns 2 hours of research into 10 minutes of review. Cowork does the hunting. You do the thinking.

3. The Email Batch Processor

Prompt:

Open Gmail. Find all unread emails from the last 24 hours.
For each email:
1. Categorize it (Urgent / Needs Response / FYI / Archive)
2. Draft a response for anything in "Needs Response"
3. Archive anything that's FYI only
Show me the drafts before sending. List the urgent items at the top.

Why it works: Email is a time black hole. This prompt turns inbox zero from a 45-minute task into a 5-minute review.

4. The Meeting Prep Assistant

Prompt:

I have a meeting with [PERSON NAME] from [COMPANY] in 30 minutes.
Research them:
1. Open their LinkedIn profile
2. Open their company website
3. Find any recent news or posts they've shared
4. Check if we have any email history in Gmail
Create a one-page brief with:
- Their role and background
- Company overview
- 3 talking points based on their recent activity
- Any previous context from our emails

Why it works: Walking into a meeting cold is amateur hour. This gives you unfair advantage in 5 minutes.

5. The Content Repurposer

Prompt:

Open [URL to my YouTube video/podcast/article].
Watch/read the content and create:
1. 5 Twitter/X posts (each highlighting a different insight)
2. 1 LinkedIn post (more detailed, professional tone)
3. 1 email newsletter draft
4. A list of 10 short-form video clip ideas with timestamps
Save everything to a Google Doc called "Content Repurpose - [Title]"

Why it works: One piece of content should become 10. Most people don't repurpose because it's tedious. Cowork makes it automatic.

6. The Competitor Spy

Prompt:

Research [COMPETITOR NAME].
Open their:
1. Website (homepage + pricing page)
2. Twitter/X profile (last 20 posts)
3. LinkedIn company page
4. Any recent press or Product Hunt launches
Create a competitive analysis doc with:
- Their positioning and key messaging
- Pricing structure
- What they're talking about on social
- Gaps or weaknesses I can exploit
- 3 ways we're different/better

Why it works: Know thy enemy. This gives you a competitive brief in 10 minutes that would take an analyst a full day.

7. The Weekly Review Automator

Prompt:

Help me do my weekly review.
1. Open Google Calendar and list everything I did this week
2. Open my task manager and show completed vs incomplete tasks
3. Check my email sent folder for key conversations
4. Open my notes app for any meeting notes from this week
Create a weekly review doc with:
- Top 3 wins this week
- What didn't get done (and why)
- Key decisions made
- Focus areas for next week

Why it works: Weekly reviews are powerful but most people skip them because they're manual. Cowork does the data gathering. You just reflect.

8. The Invoice/Expense Processor

Prompt:

I need to process my business expenses.
1. Open my email and find all receipts from the last 30 days
2. Open my accounting software [Quickbooks/Xero/etc]
3. For each receipt:
- Extract the vendor, amount, and date
- Categorize it (Software, Travel, Meals, etc.)
- Log it in the accounting software
Create a summary of total spend by category.

Why it works: Expense tracking is death by a thousand paper cuts. This batches it into one session that Cowork handles.

9. The Social Media Scheduler

Prompt:

I want to schedule my social media for the week.
Open my content calendar [Notion/Sheets/etc] and review what's planned.
For each piece of content:
1. Open the scheduling tool [Buffer/Hypefury/etc]
2. Create the post with the content from my calendar
3. Set the scheduled time based on the calendar
4. Add any images or links specified
Confirm each post is scheduled and show me a summary of what's going live this week.

Why it works: Scheduling posts is repetitive clicking. Cowork handles the tedium while you focus on creating.

10. The End-of-Day Shutdown

Prompt:

Help me shut down for the day.
1. Check my task manager for anything urgent I missed
2. Look at tomorrow's calendar and flag any prep I need
3. Close all browser tabs except [essential ones]
4. Create a sticky note on my desktop with my top 3 priorities for tomorrow
5. Open Spotify and play my wind-down playlist
Tell me if there's anything critical I need to handle before logging off.

Why it works: A clean shutdown means you start tomorrow fresh. This prompt creates a ritual that actually happens.

The Meta-Prompt (Use This First)

Before running any of these, prime Cowork with context:

You are my executive assistant. You have access to my computer and can control apps, browser, and files.
When I give you tasks:
- Ask clarifying questions if needed
- Narrate what you're doing so I can follow along
- Pause before any destructive actions (deleting, sending, etc.)
- Save important outputs to Google Docs for my review
My key apps are: [list your main tools]
My browser is: [Chrome/Arc/Safari]

This context makes every other prompt work better.

The Productivity Shift

What I love about Cowork is that it isn't just a chatbot. It's a digital employee that can actually do things on your computer.

Most people use AI to write. Smart operators use AI to execute.

Start with one of these prompts. Run it tomorrow morning. See what happens.

Then build your own.

But most people couldn't explain what makes an agent different from ChatGPT if you asked them.

Here's the short version: A chatbot waits for you to ask a question. An agent goes and does the work.

That distinction changes everything about how you use AI in your business.

Here's a full breakdown of AI agents, what they are, when to use them, and how to get started.

What Is an AI Agent, Really?

An AI agent is an AI tool that can:

1. Take a goal or instruction
2. Break it down into steps
3. Execute those steps autonomously
4. Adapt when things don't go as planned

The key word is autonomously.

When you use ChatGPT, you're having a conversation. You ask, it answers. You ask again, it answers again. You're driving.

When you use an agent, you give it a destination. It figures out the route, drives itself, and handles the detours.

Example:

Chatbot: "Write me an email to follow up with John about our meeting." → Gives you the email. You copy, paste, send.

Agent: "Follow up with everyone I met at last week's conference." → Pulls your calendar, identifies the meetings, drafts personalized emails for each person, schedules them to send at optimal times.

Same underlying AI. Completely different capability.

The Four Capabilities That Define Agents

1. Tool Use

Agents can use external tools. Not just generate text, but actually do things.

Send emails. Create calendar events. Update spreadsheets. Post to social media. Query databases. Make API calls.

This is the difference between AI that writes about doing work and AI that actually does work.

2. Memory

Agents remember context across sessions.

A chatbot forgets everything when you close the window. An agent remembers that you prefer morning meetings, that your biggest client is Sarah, that you're working on a product launch in Q2.

This persistent memory means the agent gets better at helping you over time.

3. Reasoning

Agents can break complex goals into subtasks.

You say: "Prepare for my podcast interview with Mike tomorrow."

The agent reasons through: research Mike's background, pull his recent content, identify talking points, draft intro questions, create a one-page brief, add it to tomorrow's calendar.

It doesn't just do one thing. It figures out what needs to be done.

4. Autonomy

Agents can work without constant supervision.

They can run on schedules. They can monitor for triggers. They can execute multi-step workflows while you're asleep.

This is where the real leverage comes from.

Chatbot vs. Agent: When to Use Which

This is where most people get confused. Let me make it simple:

Use a Chatbot When:

• You need a quick answer to a specific question
• You're brainstorming or exploring ideas
• You want real-time conversation and iteration
• The task is simple and self-contained
• You need to stay in control of every step

Use an Agent When:

• The task has multiple steps
• You want the work done, not just described
• The workflow repeats regularly
• You need integration with other tools
• You want to free up your time, not just get information

Real examples:

The rule of thumb: If you want thinking, use a chatbot. If you want doing, use an agent.

The Best Business Use Cases for Agents

After working with dozens of business owners on AI implementation, these are the use cases where agents deliver the biggest ROI:

1. Inbox Management

An agent that monitors your email, categorizes messages, drafts responses, and flags what needs your attention.

Time saved: 5-10 hours/week for most business owners.

2. Meeting Prep

An agent that researches attendees before calls, pulls relevant context from past conversations, and creates a one-page brief.

Never walk into a meeting unprepared again.

3. Content Repurposing

An agent that takes one piece of content (a podcast, a video, a long article) and turns it into multiple formats: social posts, email newsletters, quote graphics.

One input, ten outputs.

4. Lead Research

An agent that takes a list of companies or people, researches each one, and delivers a formatted brief with relevant details.

What used to take a VA 5 hours takes an agent 5 minutes.

5. Follow-up Automation

An agent that tracks conversations, identifies who needs a follow-up, drafts personalized messages, and either sends them or queues them for your approval.

Nothing falls through the cracks.

6. Report Generation

An agent that pulls data from multiple sources, analyzes trends, and produces a formatted report on a schedule.

Weekly metrics, monthly summaries, quarterly reviews. All automated.

Best Practices for Implementing Agents

1. Start with One Workflow

Don't try to automate everything at once.

Pick one repetitive task that takes you 2+ hours per week. Build an agent for that. Get it working reliably. Then expand.

2. Human-in-the-Loop First

Start with agents that draft, not agents that send.

Have the agent prepare the email, but you click send. Have the agent create the report, but you review before it goes out.

Build trust gradually. Increase autonomy as you gain confidence.

3. Define Clear Boundaries

Agents need guardrails.

What can it access? What can it modify? What requires approval? What should it never do?

The more specific your boundaries, the more reliably your agent will operate.

4. Monitor and Iterate

Check what your agents are doing. Review their outputs regularly.

They'll make mistakes. They'll misunderstand context. That's normal. The goal is to catch errors early and refine the instructions.

5. Document Your Workflows First

Before building an agent, write down exactly how you do the task manually.

What inputs do you need? What steps do you take? What tools do you use? What does "done" look like?

This documentation becomes your agent's instruction set.

Common Mistakes to Avoid

1. Going full autonomous too fast

The agent that sends emails without approval will eventually send an embarrassing one.

Build in checkpoints. Start supervised. Earn autonomy over time.

2. Vague instructions

"Handle my social media" is not an instruction.

"Every Monday at 9am, pull my top 3 tweets from last week, repurpose them into LinkedIn posts, and save drafts in Notion for my review" is an instruction.

Specificity matters.

3. Ignoring edge cases

What happens when the data is missing? When the API fails? When the format is unexpected?

Good agents handle errors gracefully. Build in fallbacks.

4. No feedback loop

If you never review your agent's output, you'll never know when it's drifting off course.

Schedule regular audits. Check the work. Provide corrections.

5. Automating the wrong things

Not everything should be automated.

High-stakes communication, creative decisions, relationship building. These benefit from human judgment.

Automate the repetitive work so you have more time for the work that matters.

How to Build Your First Agent

You don't need to code.

Option 1: Claude Projects + MCP

Create a Claude Project with instructions and connected tools. Claude can read files, access the web, and use MCP integrations to connect to external services.

Good for: Personal productivity, research, content creation.

Option 2: Claude Cowork

Claude's built-in agent mode that can browse the web, write and execute code, and complete multi-step tasks autonomously. Available on any Claude paid plan.

Good for: Research, analysis, building deliverables, tasks that require web access.

Option 3: Manus

A cloud-based agent platform that handles complex tasks with a polished interface. Assigns the right AI model for each subtask automatically.

Good for: Users who want agent capabilities without technical setup. Works even when your computer is off.

Option 4: OpenClaw

An open-source agent framework that runs locally on your machine with full access to your files, tools, and custom skills. More powerful and flexible than the other options.

Good for: Power users who want maximum control and customization.

Note: OpenClaw is a more advanced tool. It requires additional setup to secure properly and unlock its full potential. Start with Options 1-3 if you're new to agents, then graduate to OpenClaw when you're ready for more.

The Agent Mindset Shift

Here's what most people miss:

Using agents requires you to think differently about work.

Instead of asking "How do I do this task?" you ask "How do I describe this task so an agent can do it?"

Instead of doing the work yourself, you design the workflow and let the agent execute.

This is a skill. It takes practice. But once you develop it, your leverage multiplies.

The business owners who figure this out first will have a massive advantage for the next few years.

Key Takeaways

• Chatbots answer. Agents do. The core difference is autonomy and tool use.
• Four capabilities define agents: Tool use, memory, reasoning, autonomy.
• Use chatbots for thinking, agents for doing. Match the tool to the task.
• Best ROI use cases: Inbox management, meeting prep, content repurposing, lead research, follow-ups, reports.
• Start small: One workflow, human-in-the-loop, clear boundaries, regular monitoring.
• Avoid common mistakes: Don't go full autonomous too fast. Be specific. Handle edge cases. Review outputs.
• You don't need to code: Claude Projects, Claude Cowork, and agent platforms make it accessible.

But they're stuck.

They're drowning in tools and have no idea which ones solve their specific problems.

They don't need a developer. They don't need a course. They don't need another AI newsletter.

They need someone who can look at their calendar, their inbox, their workflows, and say: "This tool. This process. This is where you start."

That's the service I've been selling. And it's working.

Here's my entire model you can copy to start making money this week.

The Model

I charge $999 for what I call an AI Tools Assessment. The client gets a 45-minute interview, a custom report with 3-7 tool recommendations, and a 30-minute walkthrough call.

The guarantee: 5+ hours returned per week, or full refund.

That sounds aggressive. It's not.

Most business owners are losing hours every week to tasks that AI solved years ago. They just don't know which tool to use.

The average client sees 6+ hours per week freed up for an average tool cost of about $40/month.

That's a 25x return on a $999 investment. Every single month.

The Four Phases

Phase 1: The Discovery Call

This is a 45-minute Zoom call. But I'm not pitching solutions. I'm pulling problems.

The questions I ask:

• Walk me through yesterday. Where did your time actually go?
• What tasks do you dread but can't hand off?
• Where does work pile up waiting on you?
• What have you tried to automate but couldn't figure out?

The goal is to hear their pain in their words. Not what they think they should automate. What's actually eating their time.

I record everything with Fathom. The transcript is critical for what comes next.

Phase 2: The AI Analysis

After the call, I feed the full transcript into Claude with a specific prompt:

"Analyze this business owner interview. Identify 5-7 areas where existing AI tools could save significant time. For each opportunity, recommend specific tools and estimate weekly hours saved."

Claude catches patterns I miss. It connects dots across different parts of the conversation.

I review the output, add recommendations from my own toolkit, and cut anything that feels like a stretch.

Phase 3: The Report

I build the final deliverable in Gamma. Clean. Professional. Looks like something a $10K consulting firm would produce.

The structure:

• Executive summary with top 3 opportunities
• Priority matrix (high impact + low effort = start here)
• Tool recommendations with implementation details
• 4-day quick start plan
• Financial impact breakdown
• Next steps

Takes about 30 minutes to assemble from a template. Clients think it took days.

Phase 4: The Review Call

This is where trust gets built and bigger deals happen.

I screen-share the report. Walk through each recommendation. Answer questions. Let them absorb it.

Then I ask:

• Which of these feels most urgent to you?
• Do you want to implement these yourself, or would you like help?
• What's your realistic timeline?

About 60% want help with implementation. That's where the real revenue lives.

The Expansion Menu

The $999 assessment is the door. Here's what's behind it:

Process Redesign ($3-5K) Almost every assessment uncovers at least one broken workflow. Map the current state, design the future state, remove the waste.

Automation Build ($1-3K) Sometimes they just need Zapier or Make.com workflows connecting their existing tools. Build it, train them, hand it off.

Knowledge Systems ($3K+) One client had 1,000+ hours of training videos nobody watched. We built a custom GPT his team can query instead of interrupting him. Saves 3 hours weekly.

Custom Workflows ($3-5K) For clients already using AI tools, I build custom prompts and templates that turn manual processes into one-click operations.

Full Implementation ($5-10K + ongoing) Custom agents running entire workflows. Only works for tech-savvy clients or those with someone to maintain it.

Why This Model Works

You're clarity + quantifiable outcomes.

Business owners are drowning in options. Every week there's a new tool, a new framework, a new "game-changing" automation.

They need someone to cut through the noise and say: "For your situation, use this."

That's worth $999. Often more.

The On-Ramp

You don't need to be an AI engineer. You need to:

1. Know the landscape Spend a month getting comfortable with Claude, ChatGPT, Fathom, Gamma, Zapier, Make.com. Use them daily. Understand what they're good at.

2. Practice the process Run 3-5 free assessments. Friends. Family. Local business owners. Build your interview questions. Refine your report template. Get testimonials.

3. Start charging $499 for your first paid client. Then $749. Then $999. Raise your price every few clients until you start hearing "no."

4. Build the backend Create your upsell menu. Process templates. Automation packages. Implementation tiers. The assessment is the entry point. Implementation is where you scale.

The Window

This opportunity exists because there's a translation gap.

On one side: powerful AI tools that keep getting better.

On the other side: business owners who know they should be using AI but don't know where to start.

The people who learn to bridge that gap will stay busy for the next 2-3 years.

Eventually the tools will get simpler. Eventually everyone will figure it out. But right now? The gap is wide open.

Key Takeaways

• The AI consulting opportunity isn't building. It's translating.
• A $999 assessment typically returns 6+ hours/week for clients
• The 4-phase framework: discovery call → AI analysis → polished report → review call
• 60% of assessment clients want implementation help at $3-10K+
• You don't need technical skills. You need to know the tools and ask good questions.
• The translation gap is wide open. It won't last forever.

What if it could actually do work for you, not just give you answers?

That's exactly what Perplexity Computer and Claude Cowork promise to deliver.

One costs $200 a month. The other costs $20.

I put them head-to-head with three real tasks. Here's a breakdown of the full test + my final verdict.

The Tools

Claude Cowork runs on your local machine. It can access files on your computer, modify them, and execute tasks directly. You need a Claude paid plan ($20/month minimum).

Perplexity Computer is cloud-based. It runs in the browser, can work even when your computer is off, and has access to multiple AI models (not just Anthropic's). It requires Perplexity Max ($200/month).

Both promise the same thing: AI that does work, not just answers questions.

Let's see which one delivers.

Test 1: Competitive Research to Client Deliverable

The task: Research four scheduling tools (Cal.com, Calendly, Acuity, HubSpot Meetings). Create a concise one-page client-ready comparison with pricing, key features, and recommendations.

How I scored it:

• Did it follow the one-page constraint?
• Is the formatting client-ready?
• Are the pricing claims accurate when verified against actual websites?

Results:

Cowork finished 60 seconds faster. But speed isn't what matters here.

Perplexity delivered a clean, one-page table format. Aesthetically pleasing. Included a "Best For" recommendation at the bottom. Client-ready out of the box.

Cowork delivered about a page and a half. Broke the one-page rule immediately. Still accurate, but needed editing before sending to a client.

When I verified pricing against Cal.com:

• Perplexity got it right ($12/user/month on annual, $28/user/month enterprise)
• Cowork had a small error (said $12-15 instead of $12-16)

Winner: Perplexity Computer

Better formatting. Followed instructions exactly. More accurate data.

Test 2: Messy Files to Structured Spreadsheet

The task: I created 10 text files with fake client intake notes. Typos everywhere. Inconsistent formatting. Varied structures.

Each tool had to read all 10 files, extract structured data, and output a clean spreadsheet with: client name, business type, primary bottleneck, wasted hours per week, and an AI tool recommendation.

Key difference in workflow:

Perplexity is cloud-based. I had to upload each file manually.

Cowork runs locally. It went directly into the folder on my machine and pulled the files itself.

Not a dealbreaker either way. Just different.

Results:

Both spreadsheets looked similar. Perplexity's was slightly more aesthetically pleasing.

But here's where it got interesting.

I checked the output for a fake client named Sophia Martinez (bakery owner, outdated website, no online ordering, 200 Instagram followers).

Both tools correctly identified the bottleneck: outdated website with no online ordering.

Both estimated wasted hours accurately (Perplexity said 5-8 hours, Cowork said 5 hours).

The difference came in the AI tool recommendation:

Perplexity recommended: ChatGPT for social media captions.

Cowork recommended: An AI website builder with integrated online ordering.

Think about that.

Both tools identified the problem as "bad website, no online ordering." But Perplexity recommended a social media content tool. Cowork recommended a website builder.

Cowork's recommendation actually solves the problem.

Winner: Claude Cowork

This is the test that matters. AI that identifies problems is useful. AI that recommends the right solution is valuable.

Test 3: Research and PDF Report

The task: Find three free or low-cost AI tools that help solopreneurs automate invoice follow-ups. Include name, pricing, one key feature, and one real user review. Compile into a one-page PDF with a professional header, comparison table, and "Best For" recommendations.

Results:

Both tools delivered strong outputs.

Perplexity gave me: Zoho Invoice, Invoice Ninja, Invoice Sherpa.

Cowork gave me: Zoho Invoice, FreshBooks, Invoice Sherpa.

Two of three recommendations overlapped.

Both had professional headers. Both had comparison tables. Both had "Best For" sections.

The difference: Perplexity included clickable source links at the bottom (including links to the actual reviews). Cowork named the sources (G2, Capterra, etc.) but didn't link to them.

We didn't ask for source links. But Perplexity went above and beyond.

Winner: Tie

Both deliverables were strong. Perplexity gets bonus points for citations, but not enough to declare a clear winner.

The Scorecard

The Verdict

Perplexity Computer is maybe 5-10% better than Claude Cowork in overall polish.

The outputs look slightly cleaner. It has access to multiple models. It runs in the cloud so your computer doesn't need to be on.

If you have $20/month: Go with Claude Cowork. You get 90%+ of the capability at 10% of the cost.

If you have $200/month and need cloud-based execution: Perplexity Computer is slightly more polished, but you're paying a premium for marginal improvements.

My recommendation: Start with Cowork.

The $180/month you save can go toward other tools, other experiments, other ways to grow your business.

Both tools are impressive. Both do real work, not just chat.

But value matters. And Cowork wins on value.

Key Takeaways

• Claude Cowork ($20/mo) runs locally and can access your files directly
• Perplexity Computer ($200/mo) is cloud-based with multi-model access
• In head-to-head tests, results were comparable (Perplexity won 1, Cowork won 1, 1 tie)
• Perplexity outputs are ~5-10% more polished
• Cowork made better contextual recommendations (matched solutions to problems)
• The $180/month premium isn't justified by the marginal improvement

I read the whole thing so you don't have to.

Here's everything that matters, stripped down to a non-technical beginner's guide that you can use to build your first Skill in 30 minutes or less.

What's a Skill, Really?

A skill is a folder. That's it.

Inside that folder, you put instructions that teach Claude how to handle specific tasks. You teach it once. It works every time after that.

No more re-explaining your preferences. No more starting from scratch every conversation. No more "remember when I told you to format it this way?"

You write it down once. Claude remembers forever.

The Folder Structure

Every skill needs exactly one thing: a file called SKILL.md.

That's the minimum. Here's what a full skill folder looks like:

your-skill-name/
├── SKILL.md (required)
├── scripts/ (optional - Python, Bash, etc.)
├── references/ (optional - docs Claude can read)
└── assets/ (optional - templates, icons)

The naming matters. SKILL.md must be exactly that. Case-sensitive. No variations.

Your folder name must be kebab-case. Like podcast-prep or client-onboarding. No spaces. No capitals. No underscores.

The Three-Level System

This is where Anthropic got clever.

Skills use progressive disclosure. Instead of loading everything into Claude's context (burning tokens), it loads information in three levels:

Level 1: YAML Frontmatter Always loaded. Tells Claude when to use this skill. Minimal tokens.

Level 2: SKILL.md Body Loaded when Claude thinks the skill is relevant. Contains the full instructions.

Level 3: Linked Files Loaded only when needed. Reference docs, examples, detailed specs.

This means your skill can be as comprehensive as you want without slowing Claude down.

The Most Important Part: Your Description

The YAML frontmatter at the top of SKILL.md is everything.

Here's the minimum:

---
name: your-skill-name
description: What it does. Use when user asks to [specific phrases].
---

That description field determines whether Claude loads your skill or ignores it.

Bad descriptions:

• "Helps with projects" (too vague)
• "Creates documentation" (no trigger phrases)

Good descriptions:

• "Manages Linear project workflows including sprint planning and task creation. Use when user mentions 'sprint', 'Linear tasks', or 'create tickets'."
• "Analyzes Figma design files and generates developer handoff docs. Use when user uploads .fig files or asks for 'design specs'."

See the difference? Good descriptions tell Claude both WHAT it does and WHEN to use it.

The Three Use Cases

Anthropic identified three categories where skills shine:

1. Document & Asset Creation

Creating consistent output. Reports, designs, code, presentations.

The key technique: embed your style guides and templates directly in the skill. Quality checklists before finalizing. No external tools required.

2. Workflow Automation

Multi-step processes that need to run the same way every time.

The key technique: step-by-step workflows with validation gates. Built-in review loops. Templates for common structures.

3. MCP Enhancement

If you have an MCP server connected (Notion, Linear, Slack, etc.), skills add the intelligence layer.

Anthropic uses a kitchen analogy I love:

MCP gives Claude the kitchen. Access to tools, ingredients, equipment.

Skills give Claude the recipes. Step-by-step instructions on how to create something valuable.

Without skills, users connect your MCP and ask "now what?" With skills, workflows activate automatically.

Writing Instructions That Work

After your frontmatter, write the actual instructions in markdown.

Here's what Anthropic recommends:

Be specific and actionable:

Bad: "Validate the data before proceeding."

Good: "Run python scripts/validate.py --input {filename} to check data format. If validation fails, common issues include missing required fields and invalid date formats (use YYYY-MM-DD)."

Use progressive disclosure:

Keep SKILL.md focused on core instructions. Move detailed documentation to the references/ folder and link to it.

Include error handling:

## Common Issues
### MCP Connection Failed
If you see "Connection refused":
1. Verify MCP server is running
2. Confirm API key is valid
3. Try reconnecting in Settings

Reference your bundled resources:

Before writing queries, consult `references/api-patterns.md` for:
- Rate limiting guidance
- Pagination patterns
- Error codes

Testing Your Skill

Three things to test:

1. Triggering

Does your skill load when it should? Does it stay quiet when it shouldn't?

Test with obvious queries ("help me plan this sprint") and paraphrased ones ("I need to create project tasks"). Make sure it doesn't trigger on unrelated topics.

2. Functionality

Does the skill actually work? Do API calls succeed? Does it handle edge cases?

Run the same request 3-5 times. Compare outputs for consistency.

3. Performance

Is it better than not having the skill?

Anthropic's benchmark: fewer clarifying questions, fewer failed API calls, fewer tokens consumed.

The Fastest Way to Start

Anthropic built a skill-creator skill. It's available in Claude.ai and Claude Code.

Just say: "Help me build a skill for [your use case]"

It will:

• Generate the SKILL.md with proper frontmatter
• Suggest trigger phrases
• Flag common issues
• Recommend test cases

You can build and test your first working skill in 15-30 minutes.

Common Mistakes

Skill won't upload? Your file isn't named exactly SKILL.md (case-sensitive).

Skill doesn't trigger? Your description is too vague. Add specific phrases users would actually say.

Skill triggers too often? Add negative triggers: "Do NOT use for simple data exploration."

Instructions not followed? They're too verbose or buried. Put critical instructions at the top. Use headers like "## CRITICAL" or "## Important."

The Most Important Point

Here's what most people miss:

Skills work across Claude.ai, Claude Code, Claude Cowork, AND the API.

Build once. Deploy everywhere.

Skills allow you to build execution infrastructure on top of LLMs.

The era of "just write a better prompt" is ending. Skills are how prompts become systems.

Key Takeaways

• A skill is just a folder with a SKILL.md file
• The description field determines when Claude loads your skill
• Use progressive disclosure: frontmatter → instructions → reference files
• Three use cases: document creation, workflow automation, MCP enhancement
• Test for triggering, functionality, and performance
• Use skill-creator to build your first one in 30 minutes

Start Here

Pick one workflow you repeat every week.

Ask yourself: "Could I teach Claude to do the first 80% of this?"

If yes, that's your first skill.

Build it today. You'll never re-explain that workflow again.

Open a browser, ask a question, copy-paste the answer somewhere else.

But what if your AI could actually do things?

Send emails, update your calendar, research someone before a meeting, draft content, manage your tasks.

All from a text message on your phone.

That's what OpenClaw does. It turns AI into a personal assistant you can message from anywhere, with real tool access, not just chat.

I've been running it for months. Here's everything you need to know.

What Is OpenClaw (And Why Should You Care)?

OpenClaw is a free, open-source tool that turns Claude into a personal AI assistant you can message from anywhere.

Here's the simple version: You install OpenClaw on your computer (or a server). It connects to your messaging apps like WhatsApp, Telegram, Discord, iMessage, or Signal.

Then you can text your AI assistant just like you'd text a friend, and it responds with real capabilities, not just chat.

Why this matters for non-technical users:

Most AI tools live in a browser tab. You have to go to them. OpenClaw flips that. Your AI comes to you, wherever you already are.

Waiting in line at the grocery store? Text your assistant to check your calendar and reschedule a meeting.

Driving home? Send a voice note asking it to draft an email to a client.

Lying in bed before sleep? Ask it to research tomorrow's meeting attendees and send you a briefing.

The AI becomes ambient. It's just there, ready to help, in the apps you already use every day.

Key things that make OpenClaw different:

1. Self-hosted means you own your data. Unlike cloud AI services, OpenClaw runs on your own computer. Your conversations, files, and business data never leave your control. For anyone handling sensitive client information or proprietary business data, this matters.
2. Multi-channel means one assistant, many apps. Connect WhatsApp AND Telegram AND Discord AND iMessage all at once. Same assistant, same memory, accessible from whatever app you prefer at that moment.
3. Tool access means it can actually do things. Regular AI can only talk. OpenClaw can browse the web, run commands, manage files, control apps, send messages, and interact with dozens of other tools. It's the difference between an advisor and an employee.
4. Open source means no vendor lock-in. It's free to use, the code is public, and there's an active community building new features and integrations. You're not dependent on one company's roadmap or pricing changes.

How to Set It Up (The Non-Technical Version)

I'm going to be honest: setting up OpenClaw requires some comfort with your computer's terminal (that black window where you type commands). But it's not as scary as it sounds, and I'll walk you through exactly what to do.

What you need before starting:

• A Mac or Windows computer (or Linux if you're technical)
• Node.js version 22 or newer (free software, 2-minute install from nodejs.org)
• An API key from Anthropic (the company that makes Claude)

Getting your Anthropic API key:

1. Go to console.anthropic.com and create an account
2. Add a payment method (you'll pay per use, typically $5-20/month for normal usage)
3. Create an API key and copy it somewhere safe

Installing OpenClaw:

Open your terminal (on Mac, search for "Terminal" in Spotlight) and type:

npm install -g @openclaw/openclaw

Then run the setup wizard:

openclaw setup

The wizard walks you through everything: entering your API key, choosing which messaging apps to connect, and setting basic preferences.

Finally, start the Gateway:

openclaw gateway

That's it. OpenClaw is now running on your computer.

Connecting your first messaging app:

For most people, I recommend starting with Telegram. Here's why:

• Setup takes 2 minutes (just create a bot and paste the token)
• No phone number linking or QR code scanning
• Works immediately
• Great for testing before connecting your main apps

WhatsApp is the most popular choice long-term, but it requires scanning a QR code to link your account, similar to WhatsApp Web. The setup wizard guides you through this.

Security: Keeping Your AI Assistant Safe

This is important. You're giving an AI assistant real access to your accounts and data. Here's how to do it safely.

Rule 1: Always use authentication.

OpenClaw requires a password or token by default. Never disable this. It prevents random people from sending commands to your assistant.

Rule 2: Restrict who can message your bot.

Out of the box, your assistant will respond to anyone who messages it. That's dangerous. Lock it down to only accept messages from your own phone number or specific trusted contacts.

The setup wizard helps you configure this, but the concept is simple: create an "allowlist" of phone numbers or usernames that are permitted to interact with your assistant.

Rule 3: Keep it local by default.

OpenClaw only listens on your local computer by default. It's not exposed to the internet. If you want to access it remotely (like from your phone when you're away from home), use a secure method like Tailscale (a free, easy VPN service) rather than opening ports directly.

Rule 4: Run security audits regularly.

OpenClaw has a built-in command that checks for common security issues:

openclaw security audit

Run this after any configuration changes. It flags problems and can automatically fix many of them.

Rule 5: Treat it as personal, not shared.

OpenClaw is designed for one person (or one trusted household/team). Don't share access with people you don't fully trust. Each person who can message your assistant has the same level of access you do.

What Can OpenClaw Actually Do?

Once running, your AI assistant gains serious capabilities. Here's a breakdown:

1. Respond across all your messaging apps

WhatsApp, Telegram, Discord, iMessage, Signal, Slack, Google Chat, and more. You message from any app, and the assistant responds in that same app. No app switching required.

This alone is valuable. Instead of opening a browser and going to Claude, you just text. The friction disappears.

2. Browse the web and do research

Your assistant can open websites, read content, search Google, and compile information. Need to know what a company does before a sales call? Ask your assistant. Want a summary of the latest news on a topic? Text it.

3. Manage files and documents

Create documents, organize folders, convert file formats, compress images. Your assistant has access to your filesystem (within boundaries you set) and can manipulate files on your behalf.

4. Control your browser

Need to fill out a form? Navigate a complex website? Take screenshots of a page? Your assistant can control a web browser, seeing what's on screen and interacting with it just like you would.

5. Connect to your apps through "Skills"

Skills are pre-built instruction sets that teach your assistant how to use specific tools. There's a whole marketplace of community-created skills for things like:

• Google Workspace (Gmail, Calendar, Drive, Docs, Sheets)
• Notion
• Todoist
• Slack
• Image generation
• Voice calls
• And dozens more

Installing a skill is one command. Once installed, your assistant knows how to use that tool.

6. Run on a schedule

Set up automated tasks that run at specific times. Every morning at 6am, have your assistant check your calendar, summarize today's meetings, and send you a briefing. Every Friday at 5pm, have it compile a weekly summary of completed tasks.

7. Connect to your phone

This one is powerful. You can install a companion app on your iPhone or Android that links to your OpenClaw setup. Your assistant can then:

• Take photos using your phone's camera
• Capture screenshots of your phone
• Send push notifications
• Get your current location
• Run commands on your mobile device

This turns your phone into an extension of your AI assistant.

Real Use Cases From My Workflow

Theory is nice, but here's what I actually use OpenClaw for every single day:

1. Automatic meeting prep

I have a lot of calls with people I've never spoken to before. Before OpenClaw, I'd scramble to research them 5 minutes before the call, or just wing it.

Now, my assistant automatically monitors my calendar. When it sees a call with someone new (someone I haven't met before), it:

• Looks up their LinkedIn profile
• Researches their company
• Finds any recent news or content they've published
• Checks if we have any mutual connections
• Compiles everything into a one-page briefing document
• Saves it to my Google Drive
• Sends me a notification with the link

All of this happens automatically, 30 minutes before the call. I walk into every meeting prepared.

2. Content creation from my phone

I publish daily content, which means I'm always writing. With OpenClaw, I can text something like:

"Write an X article about how to set up Claude Cowork for beginners"

My assistant then:

• Researches the topic (checks official docs, recent articles, common questions)
• Writes a full 800-1000 word article in my voice
• Creates multiple headline options
• Generates thumbnail images
• Uploads everything to Google Drive
• Sends me links to review

I do a quick edit, and it's ready to publish. What used to take 2-3 hours now takes 20 minutes of review and polish.

3. Email and task triage

Every morning, my assistant pulls my tasks that are due today and categorizes them:

• Tasks it can complete for me (like drafting content)
• Tasks it can help with (like research or prep work)
• Tasks that require my direct involvement (like calls or decisions)

It then drafts what it can and sends me a summary. I review, approve, and move on. My mornings are now about reviewing AI-completed work rather than starting everything from scratch.

4. Podcast guest outreach

I host a podcast and constantly need to find and pitch potential guests. I give my assistant a list of YouTube channels or podcasts in my niche. It:

• Researches each creator
• Finds their contact information (email, LinkedIn, etc.)
• Watches/reads their recent content to understand their angle
• Writes personalized pitch emails using my proven template
• Saves each pitch as a Gmail draft ready to send

I review the drafts, make any tweaks, and hit send. Ten personalized pitches that would take me hours now take 15 minutes of review time.

5. Voice calls and follow-ups

Through Twilio integration, my assistant can actually make phone calls. I've used this for:

• Automated appointment reminders to clients
• Follow-up calls after someone downloads a lead magnet
• Quick voice check-ins that would otherwise fall through the cracks

The assistant uses text-to-speech for outbound messages and can transcribe responses.

6. Quick research on the go

This happens multiple times a day. I'm in a conversation and need to reference something. Instead of stopping to look it up, I text my assistant:

"What's the pricing for Notion's team plan?" "Find me the episode number where we discussed X topic" "What's [person's name]'s company and role?"

I get answers in seconds without breaking my flow.

Common Questions

Do I need to keep my computer running?

Yes, if you're running OpenClaw on your laptop or desktop. The Gateway needs to be running to receive and respond to messages. Most people either:

• Run it on a Mac Mini or old laptop that stays on
• Run it on a small cloud server (DigitalOcean, etc.) for $5-10/month
• Use a Raspberry Pi

If your computer sleeps, you won't get responses until it wakes up.

How much does it cost?

OpenClaw itself is free. You pay for:

• Anthropic API usage (typically $5-20/month for normal use)
• Optionally, a server if you don't want to run it on your own computer

Is it hard to maintain?

Honestly, once set up, it mostly just works. Updates are one command. The community is active and helpful if you hit issues.

Can I use it with ChatGPT instead of Claude?

Yes. OpenClaw supports multiple AI providers including OpenAI, Google's Gemini, and others. Most people use Claude because it's best for tool use and long-context tasks, but you have options.

What if I break something?

OpenClaw has good defaults and safety guardrails. The security audit command catches most issues. And since it's self-hosted, you can always reset to a clean state if needed.

The Bottom Line

OpenClaw turns AI from a chat interface into a true AI operating system.

Instead of going to AI, AI comes to you. Instead of copy-pasting between apps, your assistant works across all of them. Instead of just getting answers, you get actions completed.

The setup requires some technical comfort. It's not a one-click install. But for anyone who spends hours a day on repetitive knowledge work, the investment pays off quickly.

I've saved 10+ hours per week since implementing my OpenClaw workflows. Not because the AI is magic, but because it removes all the friction between "I need something done" and "it's done."

Resources:

• Official Docs: https://docs.openclaw.ai
• GitHub: https://github.com/openclaw/openclaw
• Community Skills: https://clawhub.com
• Community Discord: https://discord.gg/openclaw

It's not. Most people don't realize Claude is three distinct products. If you're using the wrong one for your workflow, you're leaving massive productivity on the table.

And the Claude that most people have never heard of (Claude Cowork) is the biggest unlock for 99% of people.

Here's the full breakdown, including what each one does and when to use them.

The Three Claudes

Claude (claude.ai) - The chat interface. What most people think of when they hear "Claude."

Claude Code - A terminal-based coding agent that reads your codebase and executes tasks.

Claude Cowork - An agentic tool that connects to your apps and takes actions on your computer.

Same underlying AI. Completely different use cases.

Claude (The Chat Interface)

This is the original. The one you probably started with.

What it does:

• General conversation and Q&A
• Writing, editing, and brainstorming
• Document analysis (upload PDFs, images, etc.)
• Research and summarization
• Claude Projects for organized, reusable context

Best for:

• Quick questions and one-off tasks
• Content creation and editing
• Research and analysis
• Anyone who needs a smart assistant for varied tasks

Strengths:

• No setup required. Just start chatting.
• Works on web, desktop, and mobile
• Voice mode for hands-free conversations
• Projects let you save context and files for ongoing work
• Most accessible entry point to Claude

Weaknesses:

• Can't take action in your other tools
• Limited to what you paste or upload
• No direct integration with your business systems
• You're the middleman for everything

Pricing: Free tier available. Pro is $20/month for 5x usage.

The bottom line: Claude chat is your Swiss Army knife. Great for thinking, writing, and research. But it can't DO things in your other apps.

Claude Code

This is Claude with hands.

What it does:

• Reads your entire codebase
• Edits files directly
• Runs terminal commands
• Handles git workflows (commits, PRs, branches)
• Spawns "agent teams" that coordinate on complex tasks

Best for:

• Developers who want AI that actually writes and ships code
• Debugging and refactoring
• Automating repetitive development tasks
• Teams that want AI-assisted code review and PRs

Strengths:

• Lives in your terminal, IDE, or browser
• Understands your full project context
• Can run tests and verify its own work
• Agent Teams let multiple AI agents coordinate on a task
• Integrates with GitHub, GitLab, Slack, and more via MCP
• Works with VS Code, JetBrains, and standalone

Weaknesses:

• Learning curve if you're not comfortable in a terminal
• Requires setup and configuration
• Primarily for technical users
• Can burn through API credits fast on complex tasks

Pricing: Same tiers as Claude chat. Pro at $20/month, up to Ultra at $200/month for heavy usage.

The bottom line: If you write code and you're not using Claude Code, you're working too hard. It's a junior developer that never sleeps.

Claude Cowork

This is Claude with hands (for everyone else).

Cowork lets Claude take actions directly on your computer and inside your apps. It's doing the work instead of just answering questions.

What it does:

• Connects to Google Drive, Gmail, Excel, PowerPoint, Slack, and more
• Organizes files, renames documents, deletes duplicates
• Generates reports, spreadsheets, and presentations
• Runs batch operations (convert formats, compress images, standardize naming)
• Automates browser tasks when paired with Claude in Chrome
• Uses customizable plugins for specific workflows

Best for:

• Anyone drowning in repetitive computer tasks
• Knowledge workers who live in Google Workspace or Microsoft 365
• People who want AI that actually takes action, not just gives advice
• Solo operators and teams who need automation without code

Strengths:

• Works directly in your existing tools (no switching apps)
• Can pull from one app and push to another (analyze in Sheets, present in PowerPoint)
• Runs terminal commands and manages files autonomously
• Desktop extensions let Claude see and control your screen
• No coding required to automate complex workflows

Weaknesses:

• Consumes more tokens than regular chat (plan accordingly)
• Requires Claude Desktop app to stay open during tasks
• Learning curve to understand what it can and can't do

Pricing: Available on any paid Claude plan (starting at $20/month).

The bottom line: Cowork is Claude that lives inside your business tools. It's doing the work across your entire stack instead of just answering questions.

How to Pick the Right One

Use Claude (chat) if:

• You need a general-purpose AI assistant
• Your work is mostly thinking, writing, or research
• You don't need it to take action in other tools
• You're just getting started with AI

Use Claude Code if:

• You write code (any language, any framework)
• You want AI that can actually ship changes
• You're comfortable in a terminal or IDE
• You need to automate development workflows

Use Claude Cowork if:

• You need it to take action within your connected tools
• Your work spans multiple apps (email, docs, spreadsheets, etc.)
• You need domain-specific AI capabilities
• You want AI embedded in your existing workflow, not separate from it

The best part

These tools are complementary.

I use Claude chat for thinking through problems and drafting content.

I use Claude Code for building automations and writing actual code.

I use Cowork for repetitive tasks across my apps.

The question isn't "which Claude is best?"

It's "which Claude fits THIS task?"

Key Takeaways

• Claude (chat): General assistant. Great for thinking and writing. Can't take action in your tools.
• Claude Code: Coding agent. Reads your codebase, writes code, runs commands. For developers.
• Claude Cowork: Agentic automation. Connects to your apps and takes actions on your computer. For everyone.
• Pick based on the task, not loyalty to one interface.
• Most power users will end up using multiple Claudes for different workflows.

Not OpenClaw . Not Claude Cowork workflows. Not Manus builds.

The agent I'm going to show you solves a real problem that every business has, and they're willing to pay good money for it.

It's called a Speed to Lead agent, and I built one in a few hours then sold it for $1,500.

I didn't need to learn coding or any complex integrations.

Here's everything you need to know to build them, price them, and sell them.

What Speed-to-Lead Agents Actually Are

A speed-to-lead agent is an AI system that responds to new leads instantly, 24/7, without human intervention.

When someone submits a form, the agent:

1. Responds within seconds via text, email, or call
2. Asks qualifying questions
3. Captures key information
4. Books a meeting or sends a quote
5. Notifies the business owner with a summary

The lead never waits. The business never misses an opportunity. The owner wakes up to qualified appointments on their calendar instead of a list of cold leads to chase.

This isn't theoretical. I've seen contractors, real estate agents, and service businesses increase close rates by 30-50% just by responding faster.

Why Speed-to-Lead Matters (The Data)

The numbers are brutal:

• 78% of customers buy from the company that responds first (Lead Connect)
• Responding in 5 minutes makes you 21x more likely to qualify a lead vs. 30 minutes (InsideSales)
• 50% of leads go to the vendor that responds first (Drift)
• Average B2B response time is 42 hours. Most leads are dead by then.

Speed-to-lead isn't just about being fast. It's about being fast AND intelligent.

A dumb autoresponder that says "Thanks for your inquiry, we'll be in touch soon" doesn't cut it. That's what everyone does.

A speed-to-lead agent that says: "Hey [Name], thanks for reaching out about [specific service]. Quick question: is this for a residential or commercial project?" turns a form submission into a conversation.

That's the difference between a $50/month autoresponder and a $500/month agent.

How to Build a Speed-to-Lead Agent

You have three main options: Zapier, Make, or n8n. Here's when to use each.

Option 1: Zapier (Fastest to Build)

Best for: Simple workflows, non-technical clients, quick wins

Basic flow:

1. Trigger: New form submission (Typeform, Jotform, website form)
2. Action: Send SMS via Twilio
3. Action: Send email via Gmail/Sendgrid
4. Action: Create CRM record (HubSpot, Pipedrive)
5. Action: Send Slack notification to owner

Limitations: Gets expensive at scale ($50-150/month for business plans), limited logic branching, no AI without add-ons

Build time: 1-2 hours

Option 2: Make (Best Balance)

Best for: More complex logic, AI integration, budget-conscious clients

Basic flow:

1. Webhook receives form data
2. Router splits by lead type (residential vs. commercial)
3. OpenAI module generates personalized response
4. Twilio sends SMS
5. Google Sheets logs the lead
6. Calendly link included for booking

Why Make wins: Native AI modules, visual builder, much cheaper than Zapier at scale ($9-16/month covers most use cases)

Build time: 2-4 hours

Option 3: n8n (Most Powerful)

Best for: Complex multi-step agents, self-hosted clients, maximum flexibility

Basic flow:

1. Webhook trigger
2. AI agent node with conversation memory
3. Conditional branching based on responses
4. Multi-channel outreach (SMS + email + voicemail drop)
5. CRM integration
6. Automated follow-up sequences

Why n8n wins: Self-hosted option (no per-execution fees), most powerful AI agent capabilities, can build actual conversational agents

Build time: 4-8 hours

The Tech Stack I Recommend

For most speed-to-lead agents, here's my go-to stack:

• Automation: Make.com (balance of power and simplicity)
• SMS: Twilio ($0.0079/message)
• Voice: Twilio or Vapi for AI voice calls
• AI: OpenAI API (GPT-4o for speed, GPT-4 for complexity)
• Scheduling: Calendly or Cal.com
• CRM: Whatever the client already uses

Total monthly cost to run: $20-50 for most small business volumes.

How to Price Speed-to-Lead Agents

This is where most people leave money on the table.

The Wrong Way: Hourly

Don't charge hourly. A speed-to-lead agent takes 4-8 hours to build but delivers value for years. Hourly pricing punishes you for being fast and efficient.

The Right Way: Value-Based + Retainer

Setup fee: $1,500 - $5,000

• Covers initial build, testing, integration
• Higher end for complex multi-channel agents
• Lower end for simple SMS responders

Monthly retainer: $300 - $1,000/month

• Covers monitoring, updates, optimizations
• Includes X hours of support/tweaks
• Can include AI/API costs or bill separately

How to justify the price:

If a contractor closes 2 extra jobs per month at $5,000 each because they responded faster, that's $10,000 in new revenue.

Your $500/month retainer is a 20x ROI. That's an easy sell.

Pricing by Client Type

How to Find Clients

Speed-to-lead agents sell themselves once you explain the problem. Here's how to find people with that problem.

1. Local Service Businesses

Who: Contractors, roofers, plumbers, HVAC, landscapers, cleaning services

Why they need it: They're busy on job sites, can't answer the phone, losing leads to competitors who respond faster

How to find them:

• Google "[service] + [city]" and look for businesses with contact forms
• Check their response time yourself (submit a test inquiry)
• If they take more than 1 hour to respond, they need you

Pitch: "I noticed you have a contact form on your site. How quickly do leads usually get a response? I build systems that respond in under 60 seconds, 24/7."

2. Real Estate Agents

Who: Individual agents, small teams, brokerages

Why they need it: Buyer leads go cold fast, Zillow/Realtor leads are expensive, first response wins

How to find them:

• LinkedIn search "real estate agent" + your city
• Local real estate Facebook groups
• Zillow agent profiles

Pitch: "The agent who responds first wins the client. I build AI systems that respond to your leads in seconds, qualify them, and book showings while you sleep."

3. Agencies That Resell

Who: Marketing agencies, web design agencies, business consultants

Why they need it: They can white-label your service and sell to their existing clients

How to find them:

• Agency owner communities (Facebook groups, Slack channels)
• LinkedIn outreach to agency owners
• Partnerships with web designers (they build sites, you add the AI)

Pitch: "I build speed-to-lead AI agents. Want to add this to your service offering? I'll build them, you sell them, we split the revenue."

The Fastest Path to Your First Client

1. Pick one niche (contractors are easiest)
2. Find 10 businesses with contact forms
3. Submit test inquiries, track response times
4. Email the slowest responders: "I submitted an inquiry 3 days ago and haven't heard back. How many leads do you think you're losing? I can fix this."
5. Offer to build one for free or at cost in exchange for a testimonial
6. Use that case study to sell the next 10

The 80/20 of Speed-to-Lead Agents

If you remember nothing else:

1. Speed wins. Responding in 5 minutes vs. 30 minutes is a 21x difference in qualification rates.
2. AI makes it personal. Generic autoresponders don't work. Personalized, intelligent responses do.
3. Price on value, not time. A contractor who closes 2 extra jobs per month doesn't care that it took you 4 hours to build.
4. Find slow responders. Your best prospects are the businesses already losing leads. Test their response times. Show them the gap.
5. Start simple. An SMS that goes out in 60 seconds beats a complex AI agent that takes 3 weeks to build.

Build one this weekend. Sell it next week. Repeat.

Most people install one, play with it for an afternoon, and forget about it.

That's a mistake.

The right plugins, properly configured, turn Cowork from "kind of cool" into "I can't imagine working without this."

Here are the 5 that deliver real, measurable value.

1. Data Analysis

This is the single most impressive plugin in the entire library.

What it does:

Drop a CSV into your Cowork folder. Type /data:explore.

Claude reads the full dataset, summarizes every column, flags anomalies, detects data quality issues, and suggests three analyses worth running, all before you've told it what you're looking for.

Then type /data:write-query and describe what you want in plain English. It writes the SQL, validates the query, and explains what it's doing and why.

Connectors: Snowflake, Databricks, BigQuery, Hex, Amplitude, Jira

Real example:

One user ran a client's quarterly revenue data through it, 45,000 rows across three product lines. Within 8 minutes, Claude identified a pricing anomaly in their mid-tier plan that was costing roughly $14,000 per month in undercharged renewals.

The client's own data team had missed it for two quarters.

Who needs it:

Anyone who works with data but isn't a data engineer. If you've ever spent an hour trying to figure out why numbers don't add up, or wished you could just ask "what's wrong with this spreadsheet?", this plugin pays for your entire Claude subscription.

Key commands:

• /data:explore - Automatic dataset analysis
• /data:write-query - Natural language to SQL
• /data:visualize - Generate charts and graphs

2. Productivity

This is the plugin everyone should install first, regardless of role.

What it does:

Manages tasks, calendars, daily workflows, and personal context.

Type /productivity:start and Claude reviews your day, organizes priorities, and sets up your task list based on what's actually on your calendar and in your project management tools.

Connectors: Slack, Notion, Asana, Linear, Jira, Monday, ClickUp, Microsoft 365

Why it's different:

Most productivity tools tell you what to do. This one actually does the organizing for you.

What makes it genuinely useful isn't any single feature. It's that it compounds. After a week of use, the Productivity plugin paired with good context files makes Claude feel like a chief of staff who actually knows your schedule, your priorities, and your working style.

Real example:

Start every morning with /productivity:start. Before you check email, before you open Slack, Claude has already pulled your calendar, reviewed your task list, identified what's urgent vs. important, and drafted your daily plan.

The context from yesterday carries forward.

Who needs it:

Everyone. This is the foundation plugin. Install it first, use it daily, then add role-specific plugins on top.

Key commands:

• /productivity:start - Morning planning session
• /productivity:review - End-of-day wrap-up
• /productivity:plan-week - Weekly planning

3. Sales

If you do any prospecting, account management, or outreach, this plugin is absurdly good.

What it does:

/sales:call-prep pulls context from your CRM (via HubSpot, Close, or Clay connectors), researches the prospect's company, identifies recent news and triggers, and produces a structured briefing doc.

The output includes talking points, potential objections, competitive positioning, and suggested next steps.

Connectors: Slack, HubSpot, Close, Clay, ZoomInfo, Notion, Jira, Fireflies, Microsoft 365

The battlecard feature:

/sales:battlecard is particularly strong. Feed it your product info and a competitor, and it builds a side-by-side comparison with specific rebuttal language.

Not generic "we're better because" talking points, actual responses mapped to specific competitor claims.

Real example:

One sales manager shared that it replaced what used to take his team 90 minutes of pre-call prep. He now does it in under 10 minutes.

The research quality is higher because Claude pulls from multiple sources and synthesizes them, instead of someone skimming a LinkedIn profile 5 minutes before the call.

Who needs it:

Sales reps, account executives, founders doing their own sales, anyone who needs to show up prepared for calls.

The ROI is immediate: better prep = higher close rates.

Key commands:

• /sales:call-prep - Pre-call research and briefing
• /sales:battlecard - Competitive comparison docs
• /sales:pipeline-review - Deal analysis and next steps

4. Product Management

This is the plugin I didn't expect to love.

What it does:

/product-management:write-spec takes a vague product idea and turns it into a structured specification with user stories, acceptance criteria, and technical requirements.

It asks clarifying questions first (using the AskUserQuestion tool), which means the output is grounded in your actual constraints, not generic templates.

Connectors: Slack, Linear, Asana, Monday, ClickUp, Jira, Notion, Figma, Amplitude, Pendo, Intercom, Fireflies

The roadmap feature:

The roadmap command synthesizes your backlog, stakeholder input, and competitive context into a prioritized roadmap.

The competitive analysis feature pulls from connected tools and produces a structured brief that would take a PM 2-3 hours to assemble manually.

Real example:

A product manager described using it to turn a Slack thread with 47 messages of stakeholder feedback into a coherent PRD in 20 minutes.

The spec included acceptance criteria, edge cases, and technical considerations that would normally require multiple rounds of review.

Who needs it:

Product managers, founders building products, anyone who needs to turn vague requirements into concrete specs.

The connector list is the most extensive in the library, which means it can pull context from design tools, analytics, customer feedback, and project management simultaneously.

Key commands:

• /product-management:write-spec - Requirements to PRD
• /product-management:roadmap - Backlog to prioritized plan
• /product-management:competitive-analysis - Market research brief

5. Marketing

Content drafting, campaign planning, brand voice enforcement, competitor briefings, and performance reporting. The Marketing plugin covers the full content lifecycle.

What it does:

/marketing:draft-content is the standout command.

Point it at your brand-voice.md file (from your context folder), specify audience and goal, and it produces content that actually sounds like your brand. Not "AI voice." Your voice.

Connectors: Slack, Canva, Figma, HubSpot, Amplitude, Notion, Ahrefs, SimilarWeb, Klaviyo

Campaign planning:

The campaign planning workflow helps you build multi-channel plans with specific messaging, timing, and metrics for each channel.

The competitive briefing command pulls insights from connected tools and structures them for quick decision-making.

Real example:

A marketing director used it to draft a month's worth of LinkedIn content in one session. She loaded her brand voice file, her content pillars document, and her top-performing posts from the last quarter.

Claude produced 20 drafts that needed minimal editing because they matched her established style.

Who needs it:

Content marketers, social media managers, marketing directors, founders doing their own marketing. The brand voice enforcement alone is worth it, you stop explaining your style every time you need content.

Key commands:

• /marketing:draft-content - On-brand content creation
• /marketing:campaign-plan - Multi-channel campaign structure
• /marketing:competitive-brief - Market intelligence summary

How to Get Started

1. Install Productivity first. Use it for a week. Get comfortable with the daily rhythm.
2. Add one role-specific plugin. Pick the one that matches 80% of your work: Data, Sales, Product Management, or Marketing.
3. Connect your tools. Go to Settings → Connectors. Start with Slack and whatever you use most (Notion, HubSpot, Google Drive).
4. Customize the skills. Click "Customize" on any installed plugin. Add your company's terminology, your processes, your quality standards. This is where generic becomes powerful.
5. Build the compound effect. Every time Claude's output misses the mark, update your context files or plugin skills. Permanent fix, not a one-time prompt adjustment.

The Pattern

The plugins that matter share one trait: they encode domain expertise, not just prompts.

When you install the Sales plugin, you're not just getting a "write me a sales email" template.

You're loading an entire sales methodology into Claude's working memory.

Start with these five. Customize them for your workflow. Then watch what happens to your output quality.

That's leaving 90% of the value on the table.

Claude Cowork turns Claude into an autonomous coworker who can read your files, execute multi-step tasks, and deliver finished work to your folder while you do something else.

Here's everything you need to know to get started.

What Cowork Actually Is

Cowork is an agentic desktop tool built into the Claude Desktop app.

It gives Claude direct read/write access to folders on your computer, the ability to execute multi-step tasks autonomously, and the capacity to spin up parallel sub-agents for complex work.

No terminal. No command line. No code.

You describe what "done" looks like. Claude makes a plan, breaks it into subtasks, executes in a sandboxed VM on your machine, and delivers finished files to your folder.

Keep in mind: Cowork isn't a conversation. It's task delegation. You can step away and come back to completed work.

How It's Different From Chat and Claude Code

Claude Chat is prompt-response. You ask something, Claude answers. You're always in the loop. Good for brainstorming, writing drafts, answering questions.

Claude Code is for developers. It runs in your terminal, writes code, executes commands, manages git repos. Incredibly powerful if you code. Not accessible if you don't.

Claude Cowork is the middle ground for non-technical operators. It has the autonomous execution power of Claude Code, but works through a visual interface with folders and files instead of a terminal. You don't need to know bash commands or git.

Think of it this way:

• Chat = Assistant who answers questions
• Code = Developer who builds software
• Cowork = Employee who completes tasks

For most business owners, Cowork is the game-changer.

--------------------------------------

How to Set It Up (15 Minutes)

Step 1: Get Access

• Download Claude Desktop from claude.com/download
• Sign in with a paid plan (Pro at $20/month is enough)
• Open the app, click the "Cowork" tab at the top

Step 2: Create Your Workspace Create a dedicated folder for Cowork. Don't point it at your entire Documents folder.

~/Claude-Workspace/
├── context/      # Your standing context files
├── projects/     # Active project folders
└── outputs/      # Where Claude delivers work

This limits blast radius if something goes wrong. Cowork has real read/write access to whatever folder you share.

Step 3: Set Up Your Context Files

This is the highest-leverage step. Create three files in your context/ folder:

about-me.md — Who you are, what you do, your current priorities. Not your resume. What you actually work on day-to-day.

brand-voice.md — How you communicate. Tone, phrases you use, phrases you hate, 2-3 samples of your actual writing.

working-preferences.md — How you want Claude to work. "Ask questions before starting. Show your plan. Never delete without confirmation."

These three files eliminate the "generic AI output" problem. Without them, every session starts cold. With them, Claude already knows your voice and standards.

Step 4: Set Global Instructions

Go to Settings → Cowork → Edit Global Instructions.

Paste something like:

I'm [Name], [Role]. Before starting any task, read my context files first. Always ask clarifying questions before executing. Show a brief plan before taking action. Default output: .docx. Never delete files without my explicit approval.

This applies to every session automatically. Your prompt just handles the specific task.

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What Plugins to Install

Plugins bundle skills, slash commands, and connectors into role-specific packages. They're how Cowork goes from "kind of cool" to "replaced half my software stack."

Install these first:

1. Productivity (everyone needs this)

• Manages tasks, calendars, daily workflows
• Type /productivity:start and Claude reviews your day
• Connects to Slack, Notion, Asana, Linear, and more

2. Data Analysis (if you work with spreadsheets)

• Drop a CSV, type /data:explore
• Claude summarizes columns, flags anomalies, suggests analyses
• Writes SQL queries in plain English

3. One role-specific plugin:

• Sales → /sales:call-prep, /sales:battlecard
• Marketing → /marketing:draft-content
• Finance → financial statements, variance analysis
• Legal → contract review, NDA triage

How to install: Click Customize in the left sidebar → Browse plugins → Install.

Start with two. Add more once you've got the basics dialed in.

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3 Example Use Cases You Can Run Today

Use Case 1: Organize a Messy Folder

The task: You have 6 months of files dumped into one folder. Receipts, contracts, notes, random screenshots.

The prompt:

Organize all files in this folder into subfolders by type (receipts, contracts, notes, images). Use the format YYYY-MM-DD-descriptive-name for all filenames. Create a summary log documenting every change. Don't delete anything. If a file could belong to multiple categories, put it in /needs-review.

What happens: Claude reads every file, categorizes them, renames them with dates, creates the folder structure, and gives you a log of what it did. Takes 10 minutes instead of 2 hours.

Use Case 2: Weekly Research Brief

The task: Every Monday, you want a summary of what competitors shipped, industry news, and anything relevant to your business.