Borrowing it
Nothing to install: this file belongs to galiprandi/job-seeker. 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/galiprandi/job-seeker/main/.agents/skills/apply/SKILL.mdgit clone --depth 1 https://github.com/galiprandi/job-seekerWrote 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/galiprandi/job-seeker/apply)<a href="https://agentmods.dev/skills/galiprandi/job-seeker/apply"><img src="https://agentmods.dev/badge/skills/galiprandi/job-seeker/apply/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/galiprandi/job-seeker/apply"><img src="https://agentmods.dev/badge/skills/galiprandi/job-seeker/apply.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 | $0.00028 | $0.03303 |
| Opus 5 | $0.00014 | $0.01651 |
| Sonnet 5 | $0.00006 | $0.00661 |
| Haiku 4.5 | $0.00003 | $0.00330 |
Grade A, and why
apply 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 10d 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 — 244 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Apply
Trigger
Keyword: apply
The user says apply (or variants: "apply to N jobs", "postulate", "search jobs") and the full search and application flow is triggered.
Pre-flight (applies to ALL applications: LinkedIn Easy Apply AND direct career pages)
- Verify active LinkedIn session. If session closed → open browser with wrapper (see AGENTS.md "Browser session"):
node scripts/browser.js open <url> --headed(Gold Rule 5) → notify user → wait for confirmation - Browser: always use
node scripts/browser.jsfor open/close/goto. See AGENTS.md "Browser session" and "Parallel execution" for details. Never callplaywright-cli opendirectly, never open Chrome directly - Parallel execution: if running alongside other flows (e.g:
newsortargets), attach a session withnode scripts/browser.js attach --session apply-1and pass--session apply-1tolinkedin-easy-apply.jsand all browser commands. Usedetachwhen done (neverclose— it's ref-counted) - Load active preferences (see
memoryskill):node scripts/db.js "SELECT category, key, value, confidence, source FROM preferences WHERE user_id = 1 AND status = 'active' ORDER BY category, key" - Load strategy (see AGENTS.md "Strategy levels"):
Respect:node scripts/db.js "SELECT data->'strategy' AS strategy FROM users WHERE id = 1"apply_batch_size(max jobs per session),match_threshold(must_only / must_strong / must_strong_nice),relax_must_haves(loosen Must-have filtering). Ifapply_batch_size = 0, don't auto-apply, only present matches for manual approval - Read profile and existing applications via db CLI:
node scripts/db.js "SELECT data->'profile' AS profile, data->'job_preferences' AS prefs, data->'personal_info' AS personal FROM users WHERE id = 1" node scripts/db.js "SELECT url FROM applications WHERE user_id = 1" - DB is the single source of truth for ALL form fields. Before filling ANY form (LinkedIn, Lever, Greenhouse, Workday, SuccessFactors, custom sites), the agent must have the profile data loaded in context. Never invent, guess, or fabricate any value. If a required field is not in the DB, STOP, ask the user, save the answer to DB, then continue. This is Gold Rule 5c.
- Captcha policy: NEVER attempt to solve captchas programmatically. This is Gold Rule 5b. When a captcha appears (hCaptcha, reCAPTCHA, image challenge, drag-and-drop, etc.), the agent must: (1) ensure browser is headed, (2) notify the user and wait, (3) continue only after user confirms. Never retry in a loop. Never attempt to click captcha elements, solve challenges, or bypass them.
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.
- 10d ago First seen · 244 lines · 28 tokens per session scan A 1ed1bfd0c92f
apply is a skill published in the GitHub repository galiprandi/job-seeker (26 stars, last pushed 16d ago), licensed MIT. It adds 28 tokens to every session and 3,303 once invoked, about $0.0001 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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