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-company-sourcinggit 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-company-sourcing)<a href="https://agentmods.dev/skills/anysiteio/agent-skills/anysite-company-sourcing"><img src="https://agentmods.dev/badge/skills/anysiteio/agent-skills/anysite-company-sourcing/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-company-sourcing"><img src="https://agentmods.dev/badge/skills/anysiteio/agent-skills/anysite-company-sourcing.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 | $0.00155 | $0.02622 |
| Opus 5 | $0.00077 | $0.01311 |
| Sonnet 5 | $0.00031 | $0.00524 |
| Haiku 4.5 | $0.00015 | $0.00262 |
Grade A, and why
anysite-company-sourcing 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 — 161 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Company Sourcing
linkedin/search/search_sql_companies searches 70M+ companies and is the single
best bulk company tool in the catalog — and its naive use is the single most
common source of garbage lists. Measured live, same intent, same day:
{keywords: "AI startup San Francisco"}→ 1 relevant of 5: a Tel-Aviv gaming-data firm, a Sydney fintech and a Ho-Chi-Minh beauty e-commerce all matched — each had the token "startup" somewhere in its description and "San Francisco" among secondary office locations.- The same intent as structured filters (below) → 5 of 5 genuine SF AI companies in the right size band.
The difference is the whole skill. Never ship results from a naive query.
Why naive queries fail (mechanics, not opinion)
keywordsmatches whole words across ALL text fields — name, description, specialities, hashtags and the locations array — so "San Francisco" as a keyword still matches a Hanoi company that lists an SF sales office inlocations. (Whole-word matching removed the inner-substring noise, but not the wrong-FIELD problem — HQ is a separate field, use it.)sortchanges ordering, not the candidate set — a naive keyword query is still not an ICP list. With relevance (the default), "AI startup San Francisco" surfaces Startup Weekend AI, Bitcoin AI Startup Lab, 1-employee shops and a Phoenix-HQ company (all measured) — they score high because the words sit in their NAME. The fix is per-field decomposition below, not a sort flag.- Millions of company pages are stubs. Without hygiene filters they dominate.
employee_count_rangecan contradictemployee_countin the same record (measured: 305 employees with range "11-50"). Never filter or segment by the range string.
The method: decompose intent into fields
Take the user's ICP sentence apart and map each fragment to its OWN field:
| Intent fragment | Field | Notes |
|---|---|---|
| "based in X" | country_hq: ["US"] + headquarter_location: "\"san francisco\"" |
token-aware; NEVER the locations field — that matches branch offices |
| "present in X" (offices count too) | country_any |
this is the only right use of the locations array |
| "does AI / fintech / logistics" | specialities OR industry_name OR description together, not specialities alone |
industry array wants URNs; industry_name resolves labels — see the specialities caveat below |
| "in the orbit of company X" | similar_organizations: "\"fsd_company:<id>\"" |
queryable filter, not just an output field — the reverse-graph expander; see below |
| "startup / SMB / enterprise" | employee_count_min / employee_count_max |
integers; ignore employee_count_range entirely |
| "founded recently" | founded_on_min |
year |
| named company lookup | name or alias DSL + exact verification |
never trust first hit; resolve by domain via the anysite-mcp resolve recipe |
| always, every query | is_active: true, has_website: true, min_description_length: 100 |
the hygiene trio kills stubs and dead pages |
| ranking | sort = relevance (default) or last_modified |
relevance for sourcing; last_modified for "what's new since last run" (monitoring). Scoring weights a term by field: name 5× > specialities/hashtags 3× > short_description 2× > long description 1×, length-normalized; ties broken by recency. Caveat: the score is built only from keywords/name/specialities/description — a query filtered ONLY by non-text fields (e.g. just industry + employee_count_min) has nothing to score, so it falls back to recency order |
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 · 161 lines · 155 tokens per session scan A 8be5ba5b8ff9
anysite-company-sourcing is a skill published in the GitHub repository anysiteio/agent-skills (19 stars, last pushed 28d ago), licensed MIT. It adds 155 tokens to every session and 2,622 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 2026-08-30.
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