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 swan-gtm/gtm-skills --skill icpgit clone --depth 1 https://github.com/swan-gtm/gtm-skillsWrote 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/swan-gtm/gtm-skills/icp)<a href="https://agentmods.dev/skills/swan-gtm/gtm-skills/icp"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/icp/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/swan-gtm/gtm-skills/icp"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/icp.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00038 | $0.01606 |
| Opus 5 | $0.00019 | $0.00803 |
| Sonnet 5 | $0.00008 | $0.00321 |
| Haiku 4.5 | $0.00004 | $0.00161 |
Grade A, and why
icp 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 9d 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.
How it starts
The opening of the file, as written. The whole thing — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Instructions
Setup state. Configured Jun 8, 2026 via website/case-study evidence (Path B — no CRM connected). 3 segments: Cloud-Native B2B SaaS Engineering Teams (100–1,000 emp, B2B SaaS, AWS-first, post Series B), Fintech & Payments Infrastructure (50–500 emp, fintech/payments, cloud-native, compliance-driven), Digital Media, Streaming & Gaming (200–2,000 emp, high-traffic consumer platforms). 4 personas: SRE/Platform Engineer (Champion), VP/Director of Engineering (Economic Buyer), Head of DevOps/Infrastructure Lead (Champion/Evaluator), Engineering COO/CTO (Economic Buyer, Fintech segment). Next refinement: connect CRM to validate against real win/loss data.
What this skill does
Two day-to-day jobs:
- Refine — pull what's actually winning, find patterns the current ICP misses or overstates, propose tightenings the user approves before saving.
- Add a segment — define an additional ICP segment alongside the current ones when a distinct motion has emerged.
First-time discovery lives in Setup — load it if the state check tells you to.
Refine flow
Step 1 — Load and show what's defined
Pull the current segments, personas, target markets, value prop. Print them back to the user in one or two lines each so they remember what they're refining. Ask: "Anything specific that feels off, or should I scan recent deals broadly?"
Step 2 — Pull recent closed deals
Start narrow: closed-won and closed-lost deals from the last 6 months. Check the CRM if one is connected — pull the deal stage history, the associated company, the close date, the win/loss reason if populated. Page size 20.
If there are more than 60 closed deals in the period, switch to swan-execute-code: dump the deals query result to a file and aggregate there. Don't load 200 deal records into context.
If no CRM is connected, ask the user to name their last 5–10 closed-won and closed-lost deals — work from the named list. If they can't, refinement isn't possible yet; recommend they connect the CRM or come back when more deals have closed.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 9d ago First seen · 112 lines · 38 tokens per session scan A be5a4da2060f
icp is a skill published in the GitHub repository swan-gtm/gtm-skills (153 stars, last pushed 2d ago), licensed MIT. It adds 38 tokens to every session and 1,606 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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