The majority of founding teams running their Go-To-Market through Claude are using maybe 10% of what it can do.

It’s not because a lack of effort. They just haven’t used Claude’s Skills properly.

Skills have been available since October 2025 and most teams are still missing them, which is why every Monday morning still feels like rebuilding the system from memory.

Once I created my first one, building a fresh prospect list took ten minutes instead of ninety.

Writing a world-class cold email took two minutes instead of fifteen.

Reviewing six months of closed deals to find what my best clients had in common happened in a single command, instead of an afternoon with a spreadsheet.

The thinking happened once, when I wrote the Skill. Every run after that was simply execution.

Quick Note On What This Covers

Skills work in both Claude and Claude Code in your terminal. The file format is identical, but what each one can do is different.

Skills in Claude run inside the chat: research, drafting, single-company briefs.

Skills in Claude Code run on your PC they read your CSVs, update your CLAUDE.md, call MCP servers like Apollo and Brave Search, and save files back to your project folder.

This article and every GTM Skill in Founder’s GTM focuses on Claude Code. If you have not set it up yet, the Account List Builder walks you through it first.

What a Skill actually is

A Skill is a saved workflow that lives in a folder inside your Claude Code project.
It tells Claude exactly how to do one specific job: which files to read, which steps to follow, what to output, what to skip.

You write it once. Every time you want Claude to run that job again, you type one command and Claude follows the instructions.

The clearest way to understand a Skill is to compare it to the two things you are probably already using if you’ve played with Claude Code:

• A prompt is a one-off. You type it, Claude does the work, the session ends, and the instructions are gone.

• A CLAUDE.md is your project’s permanent operating manual that loads automatically in every session: your ICP, your no-go list, your rules, the background context that applies to everything you do in that folder.

• A Skill sits between them. It loads only when you ask for it, or when Claude decides it is relevant to the task at hand. One specific job, one specific output, available on demand.

Without Skills, every workflow lives in your head or in a notes file you copy and paste at the start of each session. With Skills, every workflow lives in a file Claude reads automatically when you call it.

The Account List Builder uses a Skill called /list-builder. Every system in the Founder’s GTM System Library ships at least one. Each one is a folder, each one is a file, and each one runs identically every time.

The Anatomy Of A Skill

Here’s what a Skill folder actually looks like on your PC:

.claude/skills/quick-brief/
├── SKILL.md              ← the instructions
└── references/
    └── good-briefs.md    ← optional supporting material

Only SKILL.md is required. Everything else is optional. Reference files, examples, templates, anything you want Claude to read when running this specific Skill lives in subfolders and loads only when needed, which keeps your session lean.

The SKILL.md itself has two parts:

• A short YAML frontmatter block at the top (description).

• Plain markdown instructions below.

The frontmatter tells Claude what the Skill is called and what it does:

---
name: quick-brief
description: Produce a one-page outreach brief for a single company. Pulls recent news, hiring signals, and the most likely person to contact.
---

The description matters more than it looks. Claude reads it at the start of every session to decide whether to load this Skill automatically when relevant.

A weak description means the Skill never gets used. A sharp description means Claude finds it for you the moment a related task comes up.

Everything after the frontmatter is your instructions in plain English: the steps Claude follows, the inputs it needs, the outputs it produces, the edge cases it should handle.

Why This Matters For GTM

Three reasons Skills change how founders run outbound: repeatability, compounding and speed.

The first is repeatability. Every prospect list scores the same way, every email follows the same logic. The output last Monday and the output this Monday are produced by the same instructions, which means your system stops being something you “do well when you have time” and becomes something that just happens.

The second is compounding. Each Skill you write makes the next one cheaper. Reference files get reused across multiple Skills. The signals you document for the list builder feed into the hook writer. Three months in, you have a library that works for your business specifically, not a generic GTM stack lifted from someone else’s playbook.

The third is speed. A workflow that took ninety minutes the first time runs in ten once the Skill exists. The system survives a busy week, which is the only kind of week founders have.

Build Your First GTM Skill In 15 Minutes

This is a working Skill you can paste into your project folder today, called Quick Brief.

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A few months ago I cancelled my Clay subscription. When I wrote about it, the pushback came fast.

“Sure, fine. But what about the email enrichment waterfall?”

“What about running thousands of rows?”

“What about the table view?”

Fair points, and here’s what they have in common: None of them are about enrichment.

All of them are about the same thing: workflow orchestration.

The last thing Clay still does well that nothing else does cleanly.

In the past week, two people on LinkedIn made the same argument from two different angles.

• A bootstrapped GTM consultant.

• A founder running $10M+ ARR.

So let me name what is in it, and why I think it has twelve months left, maybe fewer.

Complex workflow orchestration

When you run a lead enrichment job, you are not just looking up an email, but running a chain of decisions:

• Check Apollo. If it returns a verified email, stop.

• If not, try Prospeo. Then another email finding tool.

• If you find an email, validate it through NeverBounce.

• If the company has a HubSpot …

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Two agentic coding tools launched within two months of each other last year.

Both run locally in your terminal. Both support MCP servers and maintain persistent project context. The difference:

OpenAI’s Codex CLI is open-source under Apache-2.0.

Claude Code is not. At the foundation layer, they are otherwise functionally similar.

But here is where it gets interesting for B2B founders: Your GTM motion defines which one you pick.

The short version: Codex builds your product. Claude Code sells it.

Let’s dive in.

Read more

Four founders running outreach on LinkedIn sent me their numbers. Different offers, different markets, but the same pattern.

The only thing they had in common was this: before they used a system, their DMs were getting ignored. After the system, conversations started happening.

This edition breaks down the friction that kills conversations and shows the 3-step DM system that fixes it.

You’ll see exactly how to open a real business dialogue without sounding like every other seller on the platform.

Let’s dive in.

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I cancelled my Clay subscription three months ago. My verified email hit rate barely moved.

That surprised me, because Clay had always felt essential. Then MCP came along and the thing Clay was doing for me turned out to be a single command.

Here’s what changed and how to run the same test yourself.

What MCP actually did

MCP, the Model Context Protocol, turns every data provider into a direct connection inside Claude. Lead enrichment providers like Apollo, Prospeo, Findymail, and a growing list of others can now be queried in plain English from a single terminal session, without Clay sitting in the middle.

Before MCP, the enrichment flow looked like this:

You → Clay → Data Provider → Clay → You

You paid Clay at every point in that chain. Their platform fee, their credit markup on top of the underlying data, and your time managing a visual interface that required attention at every step.

After MCP, the flow looks like this:

You → Claude Code → Data Provider → Claude Code → You

No intermediary, n…

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Last week I spoke with an agency owner who had spent two hours tweaking a prompt, trying to get Claude Code to produce a better prospect list.

Different phrasing, different structures, longer instructions. But nothing really worked.

I asked him what he had loaded in before the prompt ran. He looked at me blankly.

There is a difference between prompt engineering, which is about phrasing the instruction better, and context engineering, which is about what your LLM knows before you type a word: Your ICP, your closed-won deals, your signal criteria.

When that context is in place, the prompt becomes almost secondary. Four lines produce better output than forty, because the context sitting underneath them is doing the real work.

The three prompts I’m sharing today are starting points, not a system, yet.

Each one covers a different layer of GTM: finding the right place at the right moment, finding the right person, and writing the right message.

1. The signal check: right place, right time

Require…

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There’s a direction problem at the heart of today’s outbound prospecting.

You start by describing who you want to find. Industry, company size, job title, geography.

Then you run it through Apollo, or you use Ocean’s lookalike feature to find companies that behave similarly to your best customers.

Your list looks right on paper, but the close rate tells a different story.

The problem is that you’re working forwards from a description of who your buyer is, before you understand why your best customers actually bought.

A fintech CFO at a 120-person company looks identical to another fintech CFO at a 120-person company on any lookalike search. But one of them just hired a Head of Finance, closed a Series B two months ago, and has three G2 reviews mentioning reporting chaos.

The other has been quiet for six months. Both match your ICP. Only one of them is ready.

The tools aren’t the problem

The lookalike tools have never been better. Apollo matches on firmographics at scale. Ocean goes a layer de…

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A few days after last week’s Warm Intro Guide went out, replies started coming in.

Most were variations of the same question:

• “What about enrichment? “

• ”What about contacts who are not in my LinkedIn connections? “

• ”What happens when someone changes jobs and I miss the window?”

Fair questions. The network audit maps what you have. The cross-connector matching finds warm paths through people you already know.

But two specific signals - work overlap and job changes - sit completely outside what any LinkedIn export and prompt combination can produce.

Today we are covering those gaps.

Gap 1: Work Overlap

Former colleagues who worked at the same company during overlapping years sit in a relationship sweet spot: Close enough to recognise your name and respond. Distant enough to have their own networks and connections you cannot reach through your immediate circle.

That relationship signal is the strongest one available in second-degree prospecting.
And it is completely invisible in a standard Linke…

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Last week, I sat down with Claude and my full LinkedIn data export, just to finally see what my network actually looked like.

One hour later I had an entire map of every warm path to every ICP-fit prospect I could reach through just six people I already trusted.

The data baffled me:

• 1,382 founders and CEOs I had never spoken to, one introduction away from someone I know well.

• 25 prospects connected to two or more of those six people, meaning multiple independent warm paths into the same conversation.

• 5 past clients who had publicly endorsed me on LinkedIn and who I had not contacted in months.

The network was never small. The problem was that I could not hold it in my head.

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A few months back, a founding team asked me for feedback on their email campaigns.

Six replies from ~ 2000 emails. They had already rewritten the subject line three times, swapped the hook, tried a shorter version, tried a longer version, and run the whole thing through Claude twice to make it sound less salesy.

They thought the copy was the problem. I asked to see the list first.

The accounts were fine on paper: right industry, right company size, right job titles. But there was nothing behind any of them. No signal that any of those companies were actively dealing with the problem they solve.

They would pull filtered lists from Sales Navigator and start sending. The copy was not the problem.

Why the Copy Gets the Blame

Copy is visible and easy to change. You can open a doc and rewrite a subject line in ten minutes, or add some fluffy personalisation.

Rebuilding the logic of your targeting however, finding better signals, questioning whether you are actually reaching the right accounts at …

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How come Clay is valued at $5B... when Claude Code exists?

Tim Soulo did not ask that question by accident.

When the CMO of Ahrefs publicly wonders, he is not making a product comparison, but pointing at something structural: a company whose entire value proposition sits between you and data you could access directly.

Rather than a $5B moat, that is a $5B question mark.

And last week, about 100 people had something to say about it on LinkedIn.

The debate in the comments was fascinating: founders, GTM engineers, and operators all weighing in on Clay vs Claude Code.

But underneath all of it, one comment from Alex Bojovich (Ramp) said it all:

“Clay helped train an entire market to value enrichment data. That market is now about to buy it without Clay in the middle.”

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A B2B founder running a specialised dev agency was doing outbound the hard way. Every morning, he’d open LinkedIn Sales Navigator, run the same saved searches, scroll through profiles, and copy prospect details into a Google Sheet.

Then he’d open each company’s website in a new tab, scan their about page and recent news, and write a personalised email from scratch.

One by one. For every single lead.

He was spending 12 to 15 hours a week on this. Prospecting on Monday and Tuesday. Writing emails on Wednesday. Following up on Thursday. Updating his spreadsheet on Friday. And booking maybe 2 meetings a month. Good meetings, with founders who genuinely needed his team. But not nearly enough to replace the referral pipeline that had quietly dried up over the past year.

He’d looked at agencies. One quoted him $4,500/month with a 3-month minimum. Another wanted $6,000 plus a setup fee. Both pitched “guaranteed meetings” but couldn’t explain what happened to the data and the learnings when the contract ended.

He’d looked at hiring a junior SDR. The maths didn’t work: $60,000 base, 3 to 6 months before they’d book a single meeting, and he’d need to build the entire playbook for them anyway.

We installed this system in his business over 8 weeks. Same founder. Same offer, market, and ICP. The only difference: AI now handles the list building, research, enrichment, and first drafts. He reviews, edits where needed, and approves. Nothing goes out without his eyes on it.

After 60 days: his pipeline went from 2 meetings a month to 8 to 10. His time on outbound dropped from 15 hours a week to under 4. His total tool cost: $580/month. No complex 12-tool stack.

The system you’re about to read is the same one we built together. It’s not a theory, but a working pipeline blueprint that B2B founders at $1M to $10M ARR are using right now to generate consistent new business without a full sales team, without agencies, and without spending half their week on manual prospecting.

The system has five layers. Each layer does one job. Together, they replace the $5,000/month agency retainer and the $120,000/year sales hire with something that costs less than $1,000/month and gets smarter every time you run it.

• Layer 1: Targeting. AI builds your prospect list from public data based on your exact buyer criteria. No expensive database subscriptions required.

• Layer 2: Research and enrichment. Connected to a lead enrichment tool, AI researches each company, finds the right contact, and documents why they’re a fit before you write a single word.

• Layer 3: Messaging. AI drafts personalised outreach based on what it found, using a proven framework you’ll learn about here, your voice, your proven angles, and your rules. You review and approve.

• Layer 4: Sending infrastructure. Emails go out through properly warmed domains with deliverability handled. They land in the primary inbox, not in spam.

• Layer 5: Signal-based follow-up. AI tracks who engaged. Hot leads appear automatically. You focus on conversations instead of chasing.

The rest of this guide breaks down each layer: what tools to use, what it costs, how to set it up, and where to go deeper.

The AI Stack: What You Need and What It Costs

Before getting into the layers, here is the full stack. Three tools. That’s it.

• Claude Code ($20 to $100/month): Your AI agent. It builds lists, researches prospects, drafts emails, and manages files. It doesn’t just suggest what to do. It does it. It opens your tools, runs searches, creates files, fixes its own errors, and works through tasks from start to finish.

• Apollo, Prospeo, or LinkedIn Sales Navigator ($99 to $200/month): Your contact data source. Contact information, company data, and job change signals. Apollo/Prospeo is better for email-first outbound. Sales Navigator is better if your buyers are active on LinkedIn.

• Smartlead or Instantly ($50 to $150/month): Your email sending infrastructure. Domain warm-up, email sequencing, deliverability monitoring, and reply tracking.

Total: $170 to $450/month for the core stack.

Add Clay ($150 to $300/month) if you need workflow orchestration and multi-source enrichment at higher volume. Even at the top end, you’re under $1000/month.

Compare that to the alternatives:

• A lead generation agency: $3,000 to $5,000/month. Pipeline stops when the contract ends. You own nothing.

• An SDR hire: $80,000 to $120,000/year fully loaded. Takes 3 to 6 months to ramp. No guarantee of results.

• An AI SDR tool (Artisan, AiSDR, etc.): $300 to $1,000/month. Automates sending but has zero context about your business. Generates volume without compounding.

The system below costs less than any of these and compounds because every campaign makes the next one better.

For the complete tool-by-tool breakdown, including what to use when and what to skip, read The only GTM tools you need in 2026.

This is the free edition of Founder’s GTM 🦊.

Founders on the paid version get access to all Claude GTM guides, Outbound Playbooks, and proven Frameworks.

Upgrade to paid

The Brain: Why This System Gets Smarter Over Time

Every AI SDR tool on the market has the same problem: it knows almost nothing about your business. It sends emails based on templates and public data, but it has no real context about your buyer, your voice, your past campaigns, or what has worked before. Every campaign starts from zero.

This system is different because of one file: your CLAUDE.md.

A CLAUDE.md file is a structured document that serves as the operating memory of your entire GTM system. It is loaded automatically into every Claude Code session and contains:

• Your buyer definition. Not “B2B SaaS companies with 50 to 500 employees” but the specific title, company situation, and breaking point that makes someone a real buyer this week.

• Your voice rules. Three to five concrete rules that make every email sound like you wrote it, not like AI generated it.

• Your proven angles. Subject lines and opening lines that got replies. Messaging approaches that converted. These get added after every campaign.

• Your exclusion list. Company types, industries, and geographies that never convert. Write them down once and the system skips them automatically.

• Your campaign history. What you sent, to whom, what happened. The AI reads this before drafting anything new.

This file starts small. After 30 days of campaigns, it becomes the most valuable sales asset in your business. After 90 days, your AI agent knows your market better than any SDR you could hire.

For the full breakdown on building a CLAUDE.md and the four components of an AI-ready GTM system, read Context engineering: the GTM skillset that makes outbound compound.

For the practical setup walkthrough of Claude Code, start with Claude Code for GTM: the first steps.

Layer 1: AI-Powered Targeting

Traditional prospecting works like this: you log into a database, set some filters (title, industry, company size), export a CSV of 500 names, and start emailing. The list is generic. The targeting is surface-level, and the reply rate reflects it.

AI-powered targeting works differently. Instead of filtering a database, you describe your exact buyer to Claude Code and it searches public sources to find companies that match.

The instruction looks like this:

“Find 30 B2B SaaS companies in the UK that raised a Series A or B in the last 18 months, have 30 to 150 employees, and are currently hiring in their finance function. For each company, record the company name, domain, funding stage, employee count, evidence of the hiring signal, and source URL. Save to companies.csv.”

Claude Code searches sources like Crunchbase, LinkedIn, job boards, company websites, and news coverage. It doesn’t guess. If it can’t verify your criteria from a public source, it skips the company. The result is 30 to 50 companies where every row has a documented reason for being there.

This replaces the $200/month database subscription and produces a higher quality list because every company has verified fit evidence, not just a matching job title.

For the full targeting framework, including the WHO/WHY/WHEN model and the Claude Code prompt you can run today, read How to source high-quality leads.

For the deeper guide on defining a buyer profile that actually converts, read How to find customers that actually buy.

Layer 2: AI Research and Enrichment

You have a list of 30 to 50 target companies. Now the system needs to find the right person at each company, get their contact details, and build a brief on why they’re worth reaching out to.

In a traditional setup, this is 2 to 3 hours of manual research per batch. A junior SDR does this all day. In this system, Claude Code does it in minutes.

The instruction: “Take companies.csv. For each company, find the CEO or founder. Get their email from Apollo or Prospeo. Research their company website and recent news. Write a one-sentence, relevant personalisation note explaining why we’re reaching out. Save everything to prospects.csv.”

Claude Code connects to Apollo via API, pulls contact data, then researches each company using web search. It produces a CSV where every row has: name, title, email, company, personalisation note, and fit score.

If you need deeper enrichment across multiple data sources (combining Apollo, LinkedIn, Crunchbase, and company websites into a single view), Clay is still the best tool for that. The decision depends on volume. If you’re running fewer than 500 prospects per month, Claude Code with Apollo is enough. Above that, Clay adds value.

For a specific comparison, read Do you still need Clay in 2026?

Layer 3: AI-Drafted Messaging

This is where most AI outbound fails. Tools like Artisan and AiSDR generate emails from templates and data. They’re grammatically correct but contextually empty. They read like AI wrote them because AI wrote them without knowing anything about the sender’s business.

The CLAUDE.md file fixes this. Because Claude Code loads your buyer definition, voice rules, and proven angles before drafting anything, the output sounds like you.

The instruction: “Using prospects.csv and the cold email Skill, draft a 3-step email sequence for each prospect. Email 1: reference the specific signal from their personalisation note. Connect it to the pain point from the CLAUDE.md. End with a single ask under 10 words. Total length: under 80 words. Save all drafts to review-drafts.csv. Do not send anything.”

Claude Code writes the sequences, follows your rules, and saves everything for your review. You read through, make edits where needed, and approve. Nothing reaches a real prospect until you’ve signed off.

This is the critical difference between this system and every AI SDR tool: you stay in control of the message. The AI handles the labour. You handle the judgment.

For the detailed breakdown of the 3-step email sequence structure, read The cold email sequence that builds pipeline.

For the messaging principles behind emails that get replies, read Outreach copy that converts.

Layer 4: Sending Infrastructure

None of the above matters if your emails land in spam. Deliverability is invisible work that makes everything else possible.

The setup:

• Separate sending domains. Never send cold outreach from your primary domain. Buy 2 to 3 variants (e.g., if your domain is acme.com, buy getacme.com and acme-mail.com). Cost: $10 to $15/year each.

• Authentication. Configure SPF, DKIM, and DMARC on every sending domain. Without these, inbox providers flag your emails before a human sees them. Your sending tool (Smartlead or Instantly) walks you through this.

• Warm-up. New domains need 2 to 4 weeks of warm-up before sending cold email. Smartlead and Instantly both have built-in warm-up that handles this automatically.

• Volume limits. Stay at 30 to 50 emails per day per inbox. With 2 to 3 domains and 2 to 3 inboxes each, your total daily capacity is 150 to 300 emails. That’s more than enough for a founder-led system.

Load your approved sequences from Layer 3 into Smartlead or Instantly, set the sending schedule, and the emails go out automatically.

For the full deliverability walkthrough, including technical setup and the mistakes that land you in spam, read How to land in the primary inbox.

For what actually drives open rates (it’s not subject lines), read How to get your cold emails opened.

Layer 5: Signal-Based Follow-Up

Most prospects won’t answer to cold emails. But silence doesn’t mean no.

A prospect who opened all three emails, clicked the link in Email 2, and visited your website or landing page is a warm lead. They just didn’t reply yet. In a traditional setup, that signal is invisible. In this system, it’s tracked and surfaced automatically.

When a prospect engages but doesn’t reply, they go into a warm follow-up queue. The follow-up is different from the original sequence: a phone call referencing the resource they clicked, a LinkedIn connection request, or a short direct email acknowledging their interest.

This is where most of the meetings actually come from. Not from Email 1. It comes from intelligent follow-up based on engagement data.

For the complete signal-based follow-up process, read Turning cold emails into warm pipeline.

Adding LinkedIn as a Second Channel

If your buyers are active on LinkedIn, LinkedIn becomes a parallel pipeline channel that amplifies your email outreach.

The system is the same: targeted connection requests, personalised messages based on AI research, and signal-based follow-up. Claude Code handles the research and drafting. You handle the sending using tools like HeyReach or Prosp.ai.

Supporting deep dives:

• The first step to making LinkedIn pay off: the foundation most founders skip.

• LinkedIn account-based targeting with job alerts: using hiring signals as intent data.

• Collecting LinkedIn leads on autopilot: scaling beyond manual prospecting.

• Turning connections into sales conversations: warm outreach that converts.

The Timeline: From Zero to Pipeline

Week 1: Set up Claude Code, create your CLAUDE.md file, connect Apollo. Buy sending domains.

Week 2: Domain warm-up begins (runs in the background). Build your first target list with Claude Code. Draft your first sequences. Review and refine.

Week 3: First emails go out. Start with 20 to 30 per day. Track deliverability closely.

Week 4: Ramp to full volume (100 to 200 per day). First replies arrive. First meetings booked.

Weeks 5 to 8: Run 2 to 3 campaign cycles. Update your CLAUDE.md with what’s working. Targeting gets sharper. Messaging gets better. The system starts compounding.

Week 9 onwards: The system is operational. You spend 3 to 5 hours per week reviewing AI output, approving sequences, and taking meetings. The AI handles the rest.

When to Hire (And When Not To)

This system is designed to be run by a founder. But at some point, the question becomes: should I hand this off?

The answer is specific. Hire only after you have:

• A CLAUDE.md file with at least 3 months of campaign data.

• A tested messaging sequence with documented reply and conversion rates.

• A working stack that someone else can operate without rebuilding.

• Consistent pipeline generation of 5+ meetings per month.

Without these, you’re paying someone to figure out your outbound from scratch. That’s a $120,000 experiment. With these, you’re handing someone a machine that already works. That’s a $60,000 hire who produces results in month one.

For the detailed hiring framework, read When to hire a GTM engineer and Should you hire a GTM strategist?

Common Questions

Q: How much does this system cost per month?

The core stack (Claude Code + Apollo + Smartlead) costs $170 to $450 per month. Adding Clay for advanced enrichment brings the total to $450 to $900. Even at the top end, the system runs for under $1,000/month. This replaces agency retainers of $3,000 to $5,000/month or an SDR salary of $80,000 to $120,000/year.

Q: How many meetings per month should I expect?

A well-targeted system sending 100 to 200 emails per day to a precise ICP typically produces 5 to 15 qualified meetings per month. The conversion rate from first email to booked meeting is roughly 1% when targeting is sharp. Founders who report lower numbers almost always have a targeting or deliverability problem, not a volume problem.

Q: Do I need Clay or is Claude Code enough?

For most founders running fewer than 500 prospects per month, Claude Code with a lead enrichment tool like Apollo handles targeting, research, and enrichment without needing Clay. Clay adds value when you need to combine multiple data sources into a single workflow at higher volume, or when your enrichment logic has complex conditional steps (e.g., “check LinkedIn first, then Crunchbase, then company website, use whichever has the most recent data”).

Q: Why not just use an AI SDR tool like Artisan or AiSDR?

AI SDR tools automate sending but lack context about your business. They don’t know your buyer, your voice, or what worked in previous campaigns. Every campaign starts from zero. The system in this guide compounds because your CLAUDE.md file carries all context and learnings forward. After 90 days, your system knows your market better than any external tool. AI SDR tools are useful only after you have a proven playbook you want to scale beyond what you can manage. It’s a pure volume game.

Q: Is this GDPR-compliant for European outreach?

B2B cold email is legal under GDPR when the legal basis of “legitimate interest” applies. This means: only contact people in their professional capacity at their business email, include a clear opt-out in every message, honour unsubscribe requests immediately, and be transparent about who you are and why you’re reaching out. The system described here is designed for targeted, relevant outreach to specific buyers, which is the strongest basis for legitimate interest.

Q: Can I really set this up without technical skills?

Yes. Claude Code operates through natural language. You type instructions in plain English and the agent executes them. “Find 30 SaaS companies in the UK that raised funding in the last year” is a valid instruction. You don’t write code. The initial setup (installing Claude Code, connecting APIs) requires running a few terminal commands, but Claude walks you through each step.

Q: How much time does this take per week once it’s running?

Once the system is operational (after the first 4 to 6 weeks of setup and initial campaigns), most founders spend 3 to 5 hours per week. That time goes to: reviewing AI-drafted sequences before they send, taking meetings with qualified prospects, and updating the CLAUDE.md with new learnings. The AI handles list building, research, enrichment, and drafting.

This guide is updated regularly as tools and tactics evolve. Every section links to a detailed deep-dive you can work through at your own pace.

This is the free edition of Founder’s GTM 🦊.

Founders on the paid version get access to all Claude GTM guides, Outbound Playbooks, and proven Frameworks.

Upgrade to paid

When outbound stops working, the copy often gets the blame.

Founding teams rewrite subject lines, test new frameworks, switch tools, and eventually conclude that cold outreach is dead.

The list never comes up, even though the list is almost always where the problem started.

Before anything else works, three things have to come together:

1. Targeting: the right message to the right person. WHO is dealing with the problem you solve, right now.

2. Credibility: making it obvious you can actually fix that problem. Why should they trust YOU over anyone else.

3. Your Offer: something worth acting on. Why NOW, not next quarter.

Today we are covering number one, because without it, the other two are a waste of good copy.

Get targeting right and everything downstream gets easier.

Your emails write themselves because you know exactly what pain you are addressing.

Your reply rate starts reflecting the quality of your thinking rather than the luck of your timing.

By the end of this edition you will have a framewor…

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I have yet to meet a founder who said their AI outbound is compounding.

Most build a campaign, send it out, get inconsistent results, and start over. Six months in, the AI knows nothing more about their business than on day one.

Take your mind back to late 2022. ChatGPT launches and everyone suddenly has an AI writing assistant. A few months later, most have quietly gone back to writing emails themselves. The output was generic. It could have been written for any company selling anything to anyone.

The founders who got real results figured out something everyone else missed: They stopped treating AI like a search engine and started treating it like a new team member who needed proper onboarding before touching anything important.

That shift is called Context Engineering. The difference between outbound that plateaus and outbound that compounds.

Generative vs Agentic AI

Every AI tool that we have used so far, ChatGPT, Gemini, the standard Claude interface, works the same way. You type somet…

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I remember looking at a tool called Clay back in 2022.

At the time, many just saw it as another outbound tool. It felt technical, and frankly, a little intimidating too.

But I saw something else. I saw a tool that was going to fundamentally break the way we build lead lists.

If you listened to me then, you know what happened next. Clay became the operating system for modern GTM.

I have that exact same feeling today.

We are standing at another turning point, and this time it was launched by a single command in a terminal window: Claude Code.

This is not AI hype. It’s a structural shift in how companies are built.

For the last decade, we have been living in the No-Code era. We paid premiums for visual interfaces, drag-and-drop builders, and colourful buttons because they made us feel safe.

We treated the Terminal - that black screen with the white text - as a “Do Not Enter” zone reserved for engineers.

Claude just gave the rest of us keys to the engine room. And if you are willing to get over …

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I admitted a hard truth to Chris Hines on his Next Round Ready podcast recently.

Two years ago, if a founder asked me, “How do I fix my sales pipeline?” I would have sold them a GTM Strategy sprint.

You would pay a consultant $15,000. They would interview your team, audit your stack, and three weeks later, they would hand you a beautiful Roadmap.

And back then, it was often worth every penny.

Today, paying for such a Roadmap seems like burning cash. You no longer need to hire a human to answer that question, because you just ask your LLM.

You upload your website to Claude or o1-preview, paste in your value prop, and ask for the ideal GTM strategy.

And honestly? The answer is usually 80% correct. Plus, it does in 30 seconds what used to take us 30 days.

The market value of information has fallen to zero.

The Canary in the Coal Mine

from Moxie GTM shared an honest update last week about the collapse of his agency.

In January 2025, he was doing $30,000 MRR. By April, he was at $0.

He pivoted, t…

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For the last two years, if you asked me how to scale outbound, I had one answer. Get Clay.

Clay basically a spreadsheet with a brain. You give it a list of companies and it automatically finds the CEOs, their emails, and their open job roles. It turns a week of manual research into a 10-minute coffee break.

But if you’re a user, you know it comes with a headache.

You might stare at your credit usage and worry that one wrong setting will burn $1,000 in two minutes. That’s “credit anxiety.” Or maybe you spend your weekend fixing a workflow that broke just because a website changed its layout.

We put up with it because we had to. We paid the “No-Code Tax” because we didn’t know how to code.

That changed in 2026.

When Anthropic launched Claude Code, the barrier to entry collapsed. Now the same Clay experts who sold you on Clay are quietly cancelling their subscriptions. They are moving to the terminal.

Here is why the pioneers are trading expensive tables for affordable agents.

The Three Eras of …

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We need to discuss the ‘Magic Hire’ fantasy.

If you’re a founder grinding through the early days of founder-led sales, you have likely looked at the explosion of GTM automation tools (Clay, n8n, Apify, etc.) and thought:

“If I just hire one brilliant engineer to hook all this up, I can stop doing manual sales, and automate everything!”

This is the most seductive narrative in GTM tech right now. It promises that code can replace headcount and that you can replace a team of 10 burning-out sales development reps with one genius writing Python scripts in a dark room.

But before you spend $200,000 trying to build a Ferrari engine, you need to check if you are still driving a go-kart.

Putting a rocket engine on a sinking ship doesn’t make it fly. It just makes it sink faster.

To make the right decision, let’s understand what this role actually is, where it came from, and why you most likely do not need one yet.

What is a GTM Engineer?

The GTM Engineer differs from a rebranded developer. Clay effect…

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2025 GTM stack advice on LinkedIn has turned into engineering cosplay: complex workflows that look impressive but ignore business reality.

These 12-step diagrams cost $3,000 a month and require a full-time GTM engineer to maintain.

Complexity is profitable for the sellers. SaaS platforms get paid per seat and workflows grow longer every month, but you are the one left carrying the burden of the maintenance.

Before you buy a single tool, map your workflow to the 3 Core Categories below.

If a tool doesn’t fit cleanly into one of these buckets, cut it.

Category 1: Data Sourcing

1. The Accounts (Who fits?)

Founders often default to LinkedIn. But before you buy a new SaaS tool, ask yourself where your target accounts actually live.

• Standard B2B: If you sell to standard tech or service companies, they live on LinkedIn. → You need LinkedIn Sales Navigator. The gold standard for title, industry, and headcount filtering.

• Bootstrapped or early-stage companies: If you sell to founders who have not ra…

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I speak to founders every week who are drowning. They are building the product, managing the team, and trying to run sales all at once.

The result is usually a calendar that looks like a crime scene:

You spend 45 minutes researching a prospect before a call.
You spend hours reading competitor reviews to find an angle.
You spend your Sunday evenings trying to summarise financial reports to find a budget trigger.

This is low-leverage work. If you’re doing manual data synthesis in 2025, you’re voluntarily lowering your hourly rate to $15.

Today I’m sharing three GTM ‘Analyst’ prompts from my personal library. These aren’t prompts to replace your thinking. They exist to fuel it.

AI for reading, not just writing

Founders love asking ChatGPT to write emails, but that is actually its weakest skill. It defaults to the average.

AI’s superpower is synthesis.

An LLM can read a 100-page annual report, scan 500 reviews, and analyse a LinkedIn profile in seconds. A human takes hours.

If you want leverage, l…

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