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 anysiteio/agent-skills --skill anysite-crm-lookalikesgit clone --depth 1 https://github.com/anysiteio/agent-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/anysiteio/agent-skills/anysite-crm-lookalikes)<a href="https://agentmods.dev/skills/anysiteio/agent-skills/anysite-crm-lookalikes"><img src="https://agentmods.dev/badge/skills/anysiteio/agent-skills/anysite-crm-lookalikes/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/anysiteio/agent-skills/anysite-crm-lookalikes"><img src="https://agentmods.dev/badge/skills/anysiteio/agent-skills/anysite-crm-lookalikes.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 | $0.00096 | $0.01098 |
| Opus 5 | $0.00048 | $0.00549 |
| Sonnet 5 | $0.00019 | $0.00220 |
| Haiku 4.5 | $0.00010 | $0.00110 |
Grade A, and why
anysite-crm-lookalikes 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 12d 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 — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CRM Lookalikes
Your real ICP is written in your closed-won list, not in your pitch deck. Extract the pattern, then search 70M+ companies for more of it.
Works for PEOPLE too, not only companies: search_sql_users has a lookalike graph —
similar_to: [<best customer contact aliases>] (tight) / also_viewed (loose) plus
normal filters. Same discipline: the user confirms the seed set, candidates get scored.
Flow
1. Collect the seed set
crm_query_records(object_type="companies", list_id=<customers list> | search=...,
properties=[record_id, name, domain, industry, <size/stage if mapped>])
Need the user's help to identify "best": a customers list, a lifecycle/status field, or an explicit pick of 10–30 names. Fewer than ~8 seeds → warn that the pattern will be weak.
2. Profile the seeds
Resolve each seed to structured firmographics — exact verification is mandatory on every
resolve (the website search is substring match and can return only look-alike domains;
a wrong seed poisons the whole ICP pattern downstream):
execute linkedin/search/search_sql_companies {website: "seed1.com", count: 5} # per seed
# batched variant allowed, but: any seed without an exact match must be re-queried
# individually. query_cache filters the WHOLE cached set; `limit` (default 10) caps only
# how many rows come back — pass one when a batch should return more than 10 matches.
query_cache {conditions: [{"field": "website", "op": "=", "value": "seed1.com"}], limit: 50}
A seed with no exact website match is NOT dropped yet — resolve it via the site itself
(webparser/parse {url, extract_minimal: true} → top-level title + own linkedin.com/company
URL in links[] → linkedin/company), or via crunchbase → contacts.linkedin_url. Only a
seed that survives neither is excluded from profiling, and say which ones.
Plus crunchbase/company for stage/funding on a subset (venture-relevant seeds only).
Derive the pattern in-session and SHOW it:
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.
- 12d ago First seen · 84 lines · 96 tokens per session scan A a58444720743
anysite-crm-lookalikes is a skill published in the GitHub repository anysiteio/agent-skills (19 stars, last pushed 28d ago), licensed MIT. It adds 96 tokens to every session and 1,098 once invoked, about $0.0005 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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