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 audience-icp-filtergit 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/audience-icp-filter)<a href="https://agentmods.dev/skills/swan-gtm/gtm-skills/audience-icp-filter"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/audience-icp-filter/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/audience-icp-filter"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/audience-icp-filter.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 | $0.00156 | $0.01622 |
| Opus 5 | $0.00078 | $0.00811 |
| Sonnet 5 | $0.00031 | $0.00324 |
| Haiku 4.5 | $0.00016 | $0.00162 |
Grade A, and why
audience-icp-filter 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 — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Applies to a list that already exists and needs sorting before anyone contacts it. Produces four labelled buckets, a reason per lead, and reconciled counts.
Every imported list is mostly noise
Colleagues are in it. Competitors are watching. A third of the job titles are unreadable, half the companies are stale, and somewhere in there are the twelve people actually worth a message. The default response — skim it, sort by gut, start sequencing — leaks in the direction that hurts most: someone's own coworker gets a cold pitch.
This skill starts from an existing list. Importing or scraping is a different job with different prerequisites; folding it in makes both slower.
Check the data before asking about the ICP
The instinct is to ask what the ICP is first. Do the opposite: measure what the list actually contains, then ask only about criteria the data can support.
There is no point offering geography filtering on a list where the location field is empty. It doesn't filter anything — it routes everyone into review and calls that a result. The same applies to industry, and to exclusion when there's no company and no email to match on.
Run the coverage check, read which criteria are blocked, and say so before the conversation about the ICP starts. If enrichment is needed, quote what it will cost and get an explicit yes before spending anything. If the team declines, proceed — but name the criteria you dropped and say that exclusion is now best-effort. Filtering on a criterion the data can't support and presenting the result as clean is the worst available outcome, because it looks like work. The field-by-field thresholds and the ICP question set are in references/coverage-and-icp.md.
Two passes, and neither is optional
Pass 1 is deterministic. scripts/build.py pattern-matches seniority and function across the title and, when the title is silent, the bio; applies exclusions uniformly across every identity field; and refuses to emit a result whose bucket counts don't reconcile against the input. Nobody gets lost, and the same list classifies the same way twice. Never hand-sort a list — it's unauditable and it's exactly how colleagues leak through.
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.
- 9d ago First seen · 81 lines · 156 tokens per session scan A d0bb4fa65d98
audience-icp-filter is a skill published in the GitHub repository swan-gtm/gtm-skills (150 stars, last pushed 2d ago), licensed MIT. It adds 156 tokens to every session and 1,622 once invoked, about $0.0008 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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