Cody Schneider GTM Engineering with Claude Code

// When should you use GTM Engineering with Claude Code?

Use this skill whenever you need to delegate a repeatable GTM task (keyword research, content creation, publishing, ad analysis, performance reporting) to an AI agent rather than doing the hands-on work yourself. Trigger it any time you catch yourself about to manually touch a tool that has an API.

// What do you need before starting a GTM Engineering workflow with Claude Code?

• Working Directory / Project Folderrequired
  A dedicated local folder that will house the .env file and CLAUDE.md for this GTM project.
• API Keys for Your Stackrequired
  Credentials for every tool Claude will need to touch (e.g. Keywords Everywhere, CMS/Strapi/WordPress/Webflow, Google Search Console via Graph MCP, ad platforms, etc.).
• Target Task or Campaign Briefrequired
  A plain-language description of the GTM work to be done: target keyword, content type, platform to publish to, data source to pull from, etc.
• Source Material / Context
  Raw material Claude should base its output on — e.g. scraped page-one SERPs, your tone-of-voice style guide, a transcript of an AI interview with you, or existing analytics data.
• Voice / Perspective Transcript
  Optional 30-minute AI interview transcript capturing your opinions and POV on the content category, used to inject authentic perspective into generated content.

// What are the core principles behind Cody Schneider's GTM Engineering approach?

// How do you apply GTM Engineering with Claude Code step by step?

1. 1

   Create a dedicated project folder as your working directory

   Name it something meaningful to the campaign or client (e.g. 'brand-growth-agents'). All work for this GTM project lives here. This folder is your agent's home base.

2. 2

   Initialize the Stack-in-a-Folder infrastructure inside that folder

   Open a terminal, cd into the folder, type 'claude' to launch Claude Code. Prompt Claude to: (1) create a .env file for storing all API keys, and (2) create a CLAUDE.md file with a standing instruction that says: any time the user provides an API key, add it to the .env file. Do this once per project folder — it is reusable forever after.

3. 3

   Add all API keys for your GTM stack to the .env file

   Provide keys conversationally; CLAUDE.md instructs the agent to store them automatically. Include every platform the campaign will touch: keyword tools, CMS, ad platforms, analytics connectors (e.g. Graph MCP for Google Search Console). Stack them all upfront so no mid-task interruptions occur.

4. 4

   Open multiple terminal windows and launch parallel Claude Code sessions

   Each window is an independent agent working a different sub-task simultaneously. While one agent is doing keyword research, another can be drafting copy, another analyzing performance. Jockey between windows as a conductor. Using voice transcription software (e.g. Super Whisper) to dictate prompts speeds this up significantly.

5. 5

   Assign the research or data-gathering task to the first agent

   Example: 'Use the Keywords Everywhere API and find all versus-style keywords for [Product] vs [Competitor].' The agent knows which API to call because it is already in .env. Let it run; switch to another window and start the next task in parallel.

6. 6

   Gather and assemble Source Material for the creation task

   Scrape the top-ranking pages on Google for the target keyword — these are your Google-Signal Source Material. Optionally layer in: your style guide, and a transcript of a 30-minute AI interview capturing your personal POV and opinions on the topic. The richer this input, the higher the output ceiling.

7. 7

   Prompt the agent to create the asset using the assembled source material

   Specify exact parameters: word count, keyword target, tone, structure. Feed in all source material as context. Example: 'Write a 1500-word blog post targeting [keyword]. Base it on the scraped source material below and match the style guide provided.' Do not let Claude generate from nothing — always provide source material.

8. 8

   Prompt the agent to publish the asset directly to the CMS or platform

   Example: 'Publish this article to our blog using the Strapi API.' Works equally with WordPress, Webflow, or any platform with an API key already in .env. The agent handles the publish step — you do not touch the CMS manually. You are the polish/endpoint only if review is needed.

9. 9

   Set up a performance tracking dashboard for the campaign

   Connect the relevant data source (e.g. Google Search Console) to a reporting tool (e.g. Graph.com). Build a dashboard tracking the specific campaign output — impressions, clicks, keyword rankings — filtered to the URLs or content type you just published. This closes the loop.

10. 10

    Run the Continuous Improvement Loop by feeding performance data back into Claude Code

    Use the Graph MCP (or equivalent analytics connector) inside Claude Code. Prompt: 'Pull the top five [campaign] pages from Google Search Console via the Graph MCP, find the keywords related to each URL, and give me specific recommendations to optimize those pages based on the keyword data.' Claude analyzes live data and returns actionable improvements — repeat on a cadence.

11. 11

    Scale the workflow by looping the same process across every target

    Once a single end-to-end run (research → create → publish → track → improve) is validated, instruct Claude to repeat the same process for every keyword or target in the list. Example: 'Do the same research, writing, and publishing process for every keyword in this list.' This is where the force-multiplication effect of GTM Engineering activates.

// What are real-world examples of GTM Engineering with Claude Code in action?

// What mistakes should you avoid when using GTM Engineering with Claude Code?

• Treating GTM Engineering as a skill demonstration rather than actual work getting done — the goal is completed, published, live output, not impressive prompts.
• Working out of a single terminal window sequentially instead of running multiple parallel agent sessions — you lose the force-multiplication effect entirely.
• Skipping the CLAUDE.md setup and manually re-entering API keys each session — this breaks the reusability of the Stack-in-a-Folder and creates constant interruptions.
• Providing no Source Material and expecting Claude to generate high-quality content from nothing — weak guardrails produce weak output; blaming the tool is a skill issue.
• Publishing content once and never feeding performance data back into Claude — the Continuous Improvement Loop is what separates compounding GTM assets from one-and-done AI slop.
• Failing to incorporate your own voice, POV, and opinions into source material — content without authentic perspective is generic; the 30-minute AI interview transcript is the differentiator.
• Assuming this only applies to SEO or cold email — GTM Engineering covers paid ads, customer experience, product feedback loops, reporting, and anything else in the go-to-market motion.

// What key terms do you need to know for GTM Engineering with Claude Code?

GTM Engineering (Go-To-Market Engineering)

Using AI agents to handle all execution-layer work across marketing, sales, paid ads, SEO, customer experience, and product — originally coined around cold outreach/Clay.com workflows but now encompassing the full go-to-market function.

Middle Work

All the hands-on-keyboard, mouse-touching execution tasks that sit between having an idea and having a finished output. In the GTM Engineering model, Middle Work is entirely delegated to Claude Code or other agents.

Stack-in-a-Folder

The infrastructure pattern of a single project folder containing one .env file (all API keys) and one CLAUDE.md file (standing agent instructions), giving every agent session launched from that folder instant access to the full tool stack.

CLAUDE.md

A standing-instructions file placed in the working directory that persists context and rules across agent sessions — e.g. 'whenever the user provides an API key, add it to the .env file.'

Conductor

The human role in GTM Engineering: orchestrating multiple parallel agent sessions, reviewing outputs, and directing next actions — as opposed to doing any of the Middle Work manually.

Google-Signal Source Material

The scraped content of pages currently ranking on page one of Google for a target keyword, used as the structural and topical foundation for new content because Google's ranking is itself a signal of what constitutes a good result.

Continuous Improvement Loop

The cyclical process of feeding live performance data (e.g. from Google Search Console via Graph MCP) back into Claude Code to generate specific optimization recommendations for already-published assets.

Jockeying the Agents

Cody's term for the practice of actively switching between multiple open terminal windows running parallel Claude Code sessions, directing each agent's next action while others are still executing.

Graph MCP

An MCP (Model Context Protocol) connector that links a data analytics platform (Graph.com) into Claude Code, enabling the agent to query live GTM performance data — such as Google Search Console — directly inside a workflow.

Low Performers / High Performers

Cody's classification of assets (ads, pages, keywords) by performance data. Identifying these via agent-driven analysis is the trigger for either cutting, optimizing, or scaling those assets.

// FREQUENTLY ASKED QUESTIONS