👋 Hi, it’s Kyle and welcome to Growth Unhinged, my weekly newsletter exploring the hidden playbooks behind the fastest-growing startups.

I recently surveyed readers about how they use Claude Code (and Cowork) for GTM. You all said the single most critical part of your Claude setup was the context layer — everything else runs downstream of this. Skill files, like the ones I shared last week, are only truly useful if they fit into GTM system that understands your exact business.

Matteo Tittarelli, founder of Genesys and co-founder of GTM Engineer School, helps Series A-C GTM teams transform their marketing and sales processes with Claude Code agents and skills. Today Matteo unpacks how to build your AI GTM system with context.

I run a solo consultancy at the speed of a 5-person team.

I work across the full B2B SaaS and AI spectrum — from seed-stage founders to PE-backed teams — embedded as the PMM and content functions for companies like Archive.com (Series A), Common Room (Series B), Crescendo (Series C), Pivot (Series B), AdvisoryAI (seed), Octave (Seed), Seapoint (seed), Enhesa (PE-backed), and many more.

I'm usually an early adopter of AI tools. But real productivity gains didn’t show up for me until January 2026. That’s when Claude Code, skills, and MCPs matured enough that I could stop stitching tools together and start building a compounding GTM system. Before that, every "AI productivity" win was local. One good prompt, one good output, then back to manual.

The first version ran inside a Claude project for Archive.com, the Series A creator marketing platform. I fed it everything by hand: 100+ customer interviews, sales call notes, competitor URLs, my PMM templates. It produced genuinely good positioning and messaging assets.

The problem was that nothing persisted between sessions except the prompt itself and the artifacts created. So all my effort went into the prompt: long instructions, context badly managed, me scrolling through a wall of my own text to find where I'd contradicted myself. There was no structured context for the model to pull from — other than project instructions and attachments — just giant prompts I kept rewriting and copy-pasting.

That's the ceiling of Claude.ai and ChatGPT, and the exact reason I moved to Claude Code. Since I moved to Claude Code, I stopped chasing better prompts and started building systems of context, action, orchestration, and integrations — a folder architecture, a naming convention, and a refresh discipline that every skill, agent, and human on the team reads from and writes to.

Here's what that looks like in practice.

Every GTM team I talk to is feeling the same thing: output isn’t compounding no matter how much they use AI.

Every new piece of content, research doc, and sales asset starts from scratch, but none of it makes the next piece sharper. Ask the team to point at one artifact that's making every other artifact better, and the room goes quiet. Meanwhile, GTM debt keeps piling up:

AI-native GTM has moved past "find the right prompt." The new standard is humans and agents orchestrating context, skills and MCPs. The most AI-mature operators I work with are running agents on tens of skills and connecting multiple MCPs (Apollo, Clay, Granola, Slack, Gmail, Drive, Linear, and counting).

As Zach Vidibor, CEO of Octave, said in a recent podcast episode with me, “The resting heart rate of the market went from 60 to 120." Research is faster, strategy is faster, the content and launch cadence is unrecognizable from twelve months ago. But because of this, there’s more noise, as out of a hundred LinkedIn posts from agents, none of them sound like the founder. Stale messaging compounds across more channels because the same drift propagates everywhere the agents reach, at a faster pace.

The gap is whether the work you produce has somewhere to live that makes the next piece of work sharper. What I’ve landed on is an AI GTM system with four layers (you can screenshot this image and tell Claude: “Set up my workspace with these four layers.”)

This is the starting point, the foundation that makes the whole machine spin. With specific skills, you can generate always-loaded foundational .md files so Claude knows your role, industry, company, and work. It should include:

Once you have your folder structure set up, you feed specific research skills your company URL and sales calls transcripts to generate these research docs, which will be used by other skills as foundational context. That’s how Claude never forgets about you, your ICP, and industry.

Skills for every GTM lane turn context into GTM research, strategy, or execution. You can build skills for each function. Yours might include LinkedIn content, ad copy, or outbound sequences. (Here’s my starter pack for competitor research, ICP research, tone of voice, case studies, battlecards, and win-loss analysis.)

Each skill reads from specific context folders, produces structured output, and dispatches it to a target folder. When programmed correctly, each skill knows what it needs as input, what its output schema is, what an example of good output looks like, and where the output belongs in the tree.

Agents and hooks decide which skills to run, when, and on what inputs, enforcing your rules and conventions. 

A simple example: a refresh agent reads latest.md, sees the win-loss skill hasn't run in five weeks, dispatches the win-loss skill against the latest batch of Gong transcripts, then routes the output to the ICP refresh skill — without anyone typing a command.

Skills use integrations to pull context from or push content to. They feed skills with external data on the way in (Exa for research, Granola for transcripts, Gong for win-loss, Apollo for accounts, HubSpot for customer data). And they route execution outputs to live destinations on the way out (Buffer for content, Google Ads for paid, Smartlead for outbound, Klaviyo for lifecycle).

Wired together, every output feeds the next input, and the inputs keep getting better.

Here's what a mature GTM system looks like as a folder. 

A few things in this diagram that aren’t obvious:

Here’s how the system works in practice: Pivot, a procurement startup I’ve been working with, recently rebranded for their Series B. I had created three vibe coded tools for them. Instead of hand editing these apps one at a time, I updated the brand kit HTML file once via the related brand kit skill. With one Claude Code chat, I then updated all three vibecoded apps with the new fonts, colors, and gradients from the new brand kit, while the underlying design components and copy stayed untouched. The same trickle-down refresh mechanism works for messaging, positioning, and tone of voice.

The execution workstreams each start with research, flow to strategy, and then execution. There’s a fourth stage of work: the refresh stage. This is what makes the system a compounding loop. 

Let me show you how you can replicate each stage for your company.