job-seeker: Skill for Claude Code

.agents/skills/apply/SKILL.md

apply is a skill for Claude Code, Codex from galiprandi/job-seeker. It costs 28 tokens per session (3,303 once invoked), scanned A, original, MIT.

A job-application routine that searches LinkedIn, checks jobs against required profile criteria, and applies through LinkedIn Easy Apply when suitable.

In plain words
What is it for?
It searches for openings, filters them using your must-have requirements, submits Easy Apply applications, and records each application in the database.
Why use it?
It reduces the manual work of finding relevant jobs and remembering which applications were submitted.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: installed under .agents/ (shared by several agents); mentions AGENTS.md.

This is galiprandi/job-seeker's own configuration. It tells Claude Code and Codex how to work on job-seeker itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything job-seeker configures →

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is node scripts/db.js "SELECT category, key, value, confidence, source FROM preferences WHERE user_id = 1 AND status = 'active' ORDER BY category, key".

Reuse

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.

Copy the file
curl -O https://raw.githubusercontent.com/galiprandi/job-seeker/main/.agents/skills/apply/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/galiprandi/job-seeker

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for apply

README.md
[![agentmods](https://agentmods.dev/badge/skills/galiprandi/job-seeker/apply/github.svg)](https://agentmods.dev/skills/galiprandi/job-seeker/apply)
Your own site
<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.

agentmods 80×15 button for apply

Your own site · 80×15
<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>
Per session 28 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,303 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 10d ago against content hash 1ed1bfd0c92f, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

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.

.agents/skills/apply/SKILL.md · 244 lines

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.js for open/close/goto. See AGENTS.md "Browser session" and "Parallel execution" for details. Never call playwright-cli open directly, never open Chrome directly
  • Parallel execution: if running alongside other flows (e.g: news or targets), attach a session with node scripts/browser.js attach --session apply-1 and pass --session apply-1 to linkedin-easy-apply.js and all browser commands. Use detach when done (never close — it's ref-counted)
  • Load active preferences (see memory skill):
    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"):
    node scripts/db.js "SELECT data->'strategy' AS strategy FROM users WHERE id = 1"
    
    Respect: apply_batch_size (max jobs per session), match_threshold (must_only / must_strong / must_strong_nice), relax_must_haves (loosen Must-have filtering). If apply_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.

Read the full file on GitHub · 244 lines

Changes

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.

  1. 10d ago First seen · 244 lines · 28 tokens per session scan A 1ed1bfd0c92f

Subscribe to this mod's changes

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.