Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add beingsmit/technical-product-gtm --skill ai-gtmgit clone --depth 1 https://github.com/beingsmit/technical-product-gtmWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/beingsmit/technical-product-gtm/ai-gtm)<a href="https://agentmods.dev/skills/beingsmit/technical-product-gtm/ai-gtm"><img src="https://agentmods.dev/badge/skills/beingsmit/technical-product-gtm/ai-gtm/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/beingsmit/technical-product-gtm/ai-gtm"><img src="https://agentmods.dev/badge/skills/beingsmit/technical-product-gtm/ai-gtm.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00054 | $0.04862 |
| Opus 5 | $0.00027 | $0.02431 |
| Sonnet 5 | $0.00011 | $0.00972 |
| Haiku 4.5 | $0.00005 | $0.00486 |
Grade A, and why
ai-gtm scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
97% identical to gtm-ai-gtm — 5 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 571 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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:
- L1 Response: Who monitors the agent? (24/7 ops team, or dev team on-call?)
- L2 Escalation: When agent action fails, who debugs? (Agent team, or product team?)
- 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 This Changes Your Sales Process:
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 571 lines · 54 tokens per session scan A 012d8f22bc6b
ai-gtm is a skill published in the GitHub repository beingsmit/technical-product-gtm (42 stars, last pushed 5mo ago), licensed MIT. It adds 54 tokens to every session and 4,862 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 97% identical to gtm-ai-gtm, differing in 5 lines, and is treated as a copy.
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