Borrowing it
Nothing to install: this file belongs to FarzamHejaziK/claude-linkedin-assistant. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/FarzamHejaziK/claude-linkedin-assistant/main/.claude/commands/jobs/find.mdgit clone --depth 1 https://github.com/FarzamHejaziK/claude-linkedin-assistantWrote 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/commands/farzamhejazik/claude-linkedin-assistant/find)<a href="https://agentmods.dev/commands/farzamhejazik/claude-linkedin-assistant/find"><img src="https://agentmods.dev/badge/commands/farzamhejazik/claude-linkedin-assistant/find/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/commands/farzamhejazik/claude-linkedin-assistant/find"><img src="https://agentmods.dev/badge/commands/farzamhejazik/claude-linkedin-assistant/find.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 | $0.00000 | $0.02356 |
| Opus 5.5 | $0.00000 | $0.00942 |
| Sonnet 5.5 | $0.00000 | $0.00471 |
| Haiku 4.5 | $0.00000 | $0.00236 |
Grade A, and why
find 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 1mo 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.
FIND (live job search)
Step 1 — Read resume(s) AND search profile, then build the search plan
1A. Resume
Read every resume file in resumes/ (skip the README and search_profile.md). Extract:
- Target roles — the user's most recent role title + adjacent titles (e.g. if they're a "Senior Data Scientist", also search "Staff Data Scientist", "Senior Product Data Scientist", "Lead Data Scientist"). Pull 3-6 titles total.
- Top keywords / skills — 10-15 from the skills section + repeated terms across bullets (technologies, methodologies, domain terms).
- Default location — the city/state from the resume's contact line.
If resumes/ has no resume (only the README and/or search_profile.md), stop and tell the user to drop their resume in.
1B. Search profile (optional, but read every time)
Look for resumes/search_profile.md. If present, read the whole file as free-form prose. Apply it as overlays on the resume-inferred plan:
- Locations: if the search profile names locations, those replace the resume's default. (Example: profile says "Remote only" → drop the resume's city, search Remote only.)
- Titles: if the profile mentions specific titles or role types, narrow the title list to match. If it explicitly excludes some (e.g. "no people management"), drop manager-track titles.
- Keywords: add any domain interests from the profile (e.g. "climate tech", "LLM infra") to the keyword pool used for searches and scoring.
- Salary floor: if the profile gives a number, use it for scoring (jobs at or above floor get +1).
- Deal-breakers: if the profile lists exclusions (e.g. "no crypto"), drop matching jobs entirely from results, before scoring.
- Company-size or stage preferences: keep in mind for the score; small-company-only profile + a Fortune-500 result → drop or score low.
If the search profile contradicts the resume (e.g. resume is full of ML, profile says "I want to switch to PM"), the profile wins. The resume describes what the user can do; the profile describes what they want to do.
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.
- 1mo ago First seen · 161 lines · 0 tokens per session scan A 05ad795fa327
find is a command published in the GitHub repository FarzamHejaziK/claude-linkedin-assistant (234 stars, last pushed 4mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,356 tokens. 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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