Agent skill · marketing growth · github
gtm-ai-gtm
Go-to-market strategy for AI products. Use when positioning AI products, handling "who is responsible when it breaks" objections, pricing variable-cost AI, choosing between copilot/agent/teammate framing, or selling autonomous tools into enterprises.
Why this skill is useful
Provides specific go-to-market strategies and frameworks for selling AI products in enterprise contexts, which the AI wouldn't reliably generate on its own.
What it needs
About 12k tokens when loaded. Last updated 2026-08-07. 37,534 stars on the source repository.
What this skill does
AI Product GTM Go-to-market strategy for AI products. These aren't generic AI principles — they're patterns from selling autonomous AI agents into enterprises where "autonomous" scared buyers and "teammate" converted them. When to Use Triggers: "How do we position this AI product?" "Buyers say they're worried about AI breaking production" "Should we call it autonomous or copilot?" "How do we price AI when usage varies 10x by customer?" "Enterprise security passed but ops rejected us — why?" Context: AI agent platforms (coding, support, ops) LLM-based applications Autonomous tools that do things (not just suggest) AI infrastructure Anything where the AI makes decisions --- Core Frameworks 1. The Real Enterprise AI Objection (It's Not What You Think) What I Learned Selling Autonomous AI Agents: Three months in, enterprise security reviews were passing fast. Good sign, right? Then the pattern emerged: security approved, but operations rejected us. The objection wasn't "will the AI break production?" — they assumed it would break production eventually. The real question was: "Who's responsible when the agent does something wrong?" Not "do we trust the agent?" — "do we trust our team to handle this?" Why This Matters: Autonomous agents create a new operational burden. You're not selling AI capability, you're selling organizational readiness. When your agent halts production at 2am, who gets paged? Who fixes it? Who explains it to the VP? Framework: The Accountability Cascade Before deploying AI agents, enterprises need clear answers: 1. L1 Response: Who monitors the agent? (24/7 ops team, or dev team on-call?) 2. L2 Escalation: When agent action fails, who debugs? (Agent team, or product team?) 3. L3 Ownership: When something breaks badly, who owns customer communication? If you can't answer all three, they won't buy. Doesn't matter how good your AI is. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills github/gtm-ai-gtm