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 icp-fit-and-buying-readinessgit 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-fit-and-buying-readiness)<a href="https://agentmods.dev/skills/swan-gtm/gtm-skills/icp-fit-and-buying-readiness"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/icp-fit-and-buying-readiness/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-fit-and-buying-readiness"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/icp-fit-and-buying-readiness.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 | $0.00116 | $0.03270 |
| Opus 5.5 | $0.00046 | $0.01308 |
| Sonnet 5.5 | $0.00023 | $0.00654 |
| Haiku 4.5 | $0.00012 | $0.00327 |
Grade A, and why
icp-fit-and-buying-readiness 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 20d 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 — 350 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Build an evidence-based qualification framework that separates company fit from buying readiness, and both from what can only be confirmed in conversation. Produces a verdict for each account with cited evidence, explicit unknowns, and a flip condition. The method is transferable; the specific criteria are yours to define from your own win/loss/no-decision data.
The core conviction: diagnosis before prescription. Fit describes a market — it does not explain buying behavior. The companies that actually buy share a pattern: something disrupted the status quo enough to make change feel necessary. This skill builds the system for separating "looks like a fit" from "has a reason to change right now."
(See references/diagnostic-philosophy.md for the full reasoning chain and the central question this framework answers.)
Skill contract
Inputs
- ICP or customer fit criteria (operator-defined or to be built in Step 1)
- Closed-won, closed-lost, and no-decision opportunity data
- CRM and account history
- Public company information (filings, press, job postings, leadership pages)
- Observable trigger events
- Discovery notes or call transcripts when available
Outputs (required for every account evaluated)
- Fit assessment: each criterion rated Yes / No / Unknown with cited evidence
- Readiness assessment: each signal evaluated as Observed Fact → Hypothesis → Unknown, with evidence strength (Strong / Mixed / Weak)
- Investigation questions: what to research or ask next, derived from Unknowns
- Verdict: Investigate / Watch / Pass (pre-discovery) or Pursue / Watch / Pass (post-discovery)
- Flip condition: what would need to change for the verdict to move
- Next best action: the single most valuable thing to do next for this account
Evidence the skill may use
- CRM history, deal records, activity logs
- Direct buyer statements from calls, emails, or transcripts
- Documented leadership announcements, press releases, SEC filings
- Confirmed technology or organizational changes
- Job postings with specific, relevant content
- Network intelligence from known, named sources
What ships with it
6 files 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.
- 20d ago First seen · 350 lines · 116 tokens per session scan A c2c47ba77d27
icp-fit-and-buying-readiness is a skill published in the GitHub repository swan-gtm/gtm-skills (181 stars, last pushed 3d ago), licensed MIT. It adds 116 tokens to every session and 3,270 once invoked, about $0.0005 per session on Opus 5.5. 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 2026-09-28.
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