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-lookalike-expansiongit 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-lookalike-expansion)<a href="https://agentmods.dev/skills/swan-gtm/gtm-skills/icp-lookalike-expansion"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/icp-lookalike-expansion/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-lookalike-expansion"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/icp-lookalike-expansion.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 | $0.00075 | $0.00724 |
| Opus 5 | $0.00037 | $0.00362 |
| Sonnet 5 | $0.00015 | $0.00145 |
| Haiku 4.5 | $0.00007 | $0.00072 |
Grade A, and why
icp-lookalike-expansion 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 13d 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 — 41 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Use this when a few leads converted and you need more like them. It produces a deduplicated target list built on a similarity definition you chose, not one a vendor's feed chose for you.
The play
-
Read the seed properly. Pull the seed's full record and extract two things: current title and current employer. That pair is the similarity signature. Everything downstream depends on it being right, so read the actual current role rather than the headline, which is often aspirational or stale.
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Decide what "similar" means. There are two different searches and they answer different questions:
- Peers inside the same company — title plus employer. Use when you are mapping a buying committee or expanding within a won account.
- The same role across the market — title only, optionally narrowed by geography. Use when you are building a net-new list. Drop the employer filter or you will get nothing.
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Search on short titles. Pass the core role, not the decorated headline. "Chief executive officer" works; "CEO & Founder | Investor | Speaker" returns zero. Most title matching is loose, so a shorter string casts the right net.
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Branch deliberately if you need depth. Depth 1 searches off the seed alone. Depth 2 takes each discovered profile, reads its role and employer, and searches again. Depth 2 is dozens of lookups; depth 3 is hundreds. Cap how many profiles you branch on (top 10 is usually enough) and confirm the spend before going past depth 2.
-
Deduplicate by handle and drop the seed. The same person surfaces under many searches, and most search endpoints return the seed itself. Keep a seen-set across the whole run.
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Return a clean list, not the recursion tree: handle, name, headline, location, and which seed it came from.
What good looks like
- The best operator tightens one filter at a time. Stacking exact title, all-skills-match, and a city filter returns zero rows and reads as "no market" when it is really an over-narrowed query. Start broad, then narrow.
- The mediocre version trusts a "people similar to this" feed and cannot explain why anyone is on the list. When a rep asks "why him?", there is no answer.
- Empty results are a signal, not a failure. Widen by dropping the employer filter, shortening the title, or removing geography, and note which loosening produced the hit.
- You know it is good when every row survives the question "what specifically makes this person like the seed?"
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.
- 13d ago First seen · 41 lines · 75 tokens per session scan A cddf822a699f
icp-lookalike-expansion is a skill published in the GitHub repository swan-gtm/gtm-skills (150 stars, last pushed 2d ago), licensed MIT. It adds 75 tokens to every session and 724 once invoked, about $0.0004 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 2026-08-30.
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