Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/jain777/jobclaw-skillsnpx agentmods add skills/jain777/jobclaw-skills/find-jobsWrote 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/jain777/jobclaw-skills/find-jobs)<a href="https://agentmods.dev/skills/jain777/jobclaw-skills/find-jobs"><img src="https://agentmods.dev/badge/skills/jain777/jobclaw-skills/find-jobs/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/jain777/jobclaw-skills/find-jobs"><img src="https://agentmods.dev/badge/skills/jain777/jobclaw-skills/find-jobs.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 | $0.00077 | $0.02689 |
| Opus 5 | $0.00039 | $0.01345 |
| Sonnet 5 | $0.00015 | $0.00538 |
| Haiku 4.5 | $0.00008 | $0.00269 |
Grade A, and why
find-jobs 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 — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.
find-jobs
Pull listings from several source adapters, normalize them to one schema, dedupe, rank by fit, and save. Search quality is meant to keep improving — adapters are pluggable. Full design + caveats: reference/sources.md and docs/JOB_SEARCH.md.
1. Read the target
Read profile/master-profile.md frontmatter target.* (roles, locations, remote_only, industries, seniority, min_base_salary, exclude_companies) and target.regions. If no profile exists, ask for role + location, then suggest running /build-profile. Let the user override any criterion for this run.
Then read the region pack(s) in ../../knowledge/regions/ for each target region — it lists which sources/adapters apply there (e.g., US → ATS-direct + Adzuna(us) + HN; IN → Indian ATS-direct startups + Adzuna(in), with Naukri/Instahyre/LinkedIn-India as user-session/manual). Source from the region-appropriate list, not a US-only default.
2. Build the company target set (cache-driven)
The catalog ../../knowledge/companies/companies.csv lists 1000+ employers across all industries, tagged by region + sector. Use it instead of probing the web blind:
- Filter the catalog to the user's
target.regionsandtarget.industries(map industries → thesectorvocab, e.g.fintech→technology+finance-banking; see../../knowledge/companies/README.md). Add any companies named in the profile.- AI targets: when
target.roles/target.tracksindicate AI/ML (see../../knowledge/ai-roles.md), prioritize catalog rows taggedaiorycintracks, and pull fresh YC AI startups:python3 scripts/yc_companies.py --ai-only(or--hiring-onlyfor currently-hiring). The catalog already carries 2400+ YC companies + curated frontier labs (source_index∈yc/ai).
- AI targets: when
- Resolve boards from the cache. Refresh just the slice you need, then read it:
This populatespython3 scripts/resolve_companies.py --csv ../../knowledge/companies/companies.csv \ --cache ../../knowledge/companies/resolved.json \ --region <US,IN> --sector <...> --stale-only --max 300resolved.jsonwith each company'sboards[](ats,token,board_url,fetchable) andstatus(verified/detected_unfetchable/unresolved). It reusesdiscover_ats.py+sniff_careers.pyand is cached, so most runs are near-instant.
What ships with it
19 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.
- reference/job-schema.md 2.5 KB
- reference/sources.md 4.9 KB
- scripts/_jobposting.py 6.1 KB runs code
- scripts/adzuna.py 3.0 KB runs code
- scripts/ats_boards.py 5.0 KB runs code
- scripts/cutshort.py 5.3 KB runs code
- scripts/discover_ats.py 3.1 KB runs code
- scripts/firecrawl.py 2.8 KB runs code
- scripts/hn_hiring.py 3.5 KB runs code
- scripts/jobstore.py 4.3 KB runs code
- scripts/prerank.py 8.2 KB runs code
- scripts/resolve_companies.py 7.7 KB runs code
- scripts/serpapi.py 3.3 KB runs code
- scripts/smartrecruiters.py 4.6 KB runs code
- scripts/sniff_careers.py 3.8 KB runs code
- scripts/teamtailor.py 3.1 KB runs code
- scripts/verify_postings.py 5.2 KB runs code
- scripts/workday.py 2.7 KB runs code
- scripts/yc_companies.py 5.0 KB runs code
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 · 98 lines · 77 tokens per session scan A ccf22edf0528
find-jobs is a skill published in the GitHub repository jain777/jobclaw-skills (5 stars, last pushed 3mo ago), licensed MIT. It adds 77 tokens to every session and 2,689 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 2026-08-31.
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