gtm-engineering
When the user wants to build GTM automation with code, design workflow architectures, use AI agents for GTM tasks, or implement the 'architecture over tools' principle. Also use when the user mentions 'GTM engineering,' 'GTM automation,' 'n8n,' 'Make,' 'Zapier,' 'workflow automation,' 'Clay API,' 'instruction stacks,' 'AI agents for GTM,' or 'revenue automation.' This skill covers technical GTM infrastructure from workflow design through agent orchestration. Do NOT use for technical implementation, code review, or software architecture.
npx skills add tech-leads-club/agent-skills --skill gtm-engineering --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
The skill positions the agent as a GTM engineering expert who designs automation architecture and orchestrates AI agents for GTM tasks, covering workflow design, API-first pipelines, event-driven patterns, and architecture over tools. It cautions against using this skill for direct technical implementation, code review, or software architecture tasks.
How it works
The agent is instructed to gather contextual GTM context before designing automation, including GTM motions, tech stack, manual processes, team depth, existing automations, data sources, budget, volume, maintenance, and compliance. It defines a hierarchical instruction stack for automation:
- Layer 1: ICP Definition + Scoring
- Layer 2: Messaging Framework
- Layer 3: Personalization Rules
- Layer 4: Sequence Logic It enforces persistent context across interactions, logging every prospect interaction to a shared context, and includes mechanisms for feedback loops such as learning from positive/negative replies, no-reply sequences, and deal outcomes. It then offers architecture vs tools considerations and compares platforms (n8n, Make, Zapier) and enterprise iPaaS (Tray.io vs Workato), including when to choose each based on depth, volume, budget, and compliance.
When to use it
Use this skill when the user wants to build GTM automation with code, design workflow architectures, use AI agents for GTM tasks, or apply the 'architecture over tools' principle. It is not intended for technical implementation, code review, or software architecture. Trigger conditions include mentions of GTM engineering, GTM automation, n8n, Make, Zapier, workflow automation, Clay API, instruction stacks, AI agents for GTM, or revenue automation.
What it can touch
The skill discusses integration patterns across automation platforms (n8n, Make, Zapier, Tray.io, Workato) and emphasizes API-first design, event-driven architecture, and persistent context. It references platform capabilities (self-hosting, pricing models, AI integration) but does not prescribe executing code or interfacing with tools directly in this section. Tools declared in the skill include claude-code, copilot, cursor.
Caveats
- Do not use for technical implementation, code review, or software architecture.
- The content reflects enterprise GTM engineering concepts and platform comparisons, not hands-on code tasks.
- All claims about platform features and pricing are as described in the skill; no external validation is provided.
# GTM Engineering: Automation, Architecture & Agent Orchestration You are an expert in GTM engineering, workflow automation architecture, and AI agent orchestration for revenue teams. You combine deep technical knowledge of automation platforms (n8n, Make, Zapier, Tray.io, Workato) with API-first design principles, event-driven architectures, and the "architecture over tools" philosophy. You understand that the advantage is never the tool itself but the instruction stack, persistent context, and feedback loops built around it. You help founders, RevOps teams, and GTM engineers design, build, and scale automation systems that turn manual GTM processes into reliable, observable, cost-efficient pipelines. You understand the 2025-2026 landscape where GTM Engineer has emerged as a dedicated role combining software engineering skills with commercial acumen, and where AI agents are shifting from simple task automation to autonomous multi-step workflow execution. ## Before Starting Gather this context before designing any GTM automation or architecture: - What GTM motions are currently running? Outbound, inbound, PLG, partner, or a mix. Which generates the most pipeline today. - What is th
- Before Starting
- 1. The GTM Engineer Role
- What GTM Engineers Build
- GTM Engineer vs Adjacent Roles
- Career Trajectory
- 2. Architecture Over Tools
- The Instruction Stack
- Persistent Context
- Feedback Loops
- Architecture vs Tools: Decision Framework
- 3. Automation Platform Comparison
- n8n vs Make vs Zapier: Detailed Comparison
- Enterprise iPaaS: Tray.io vs Workato
- Platform Selection Decision Tree
What does the gtm-engineering skill do?
When the user wants to build GTM automation with code, design workflow architectures, use AI agents for GTM tasks, or implement the 'architecture over tools' principle. Also use when the user mentions 'GTM engineering,' 'GTM automation,' 'n8n,' 'Make,' 'Zapier,' 'workflow automation,' 'Clay API,' 'instruction stacks,' 'AI agents for GTM,' or 'revenue automation.' This skill covers technical GTM infrastructure from workflow design through agent orchestration. Do NOT use for technical implementation, code review, or software architecture.
How do I install it?
Run `npx skills add tech-leads-club/agent-skills --skill gtm-engineering --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From tech-leads-club/agent-skills, a repository with 4,983 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.