Borrowing it
Nothing to install: this file belongs to PARK0301/job-agent. 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/PARK0301/job-agent/main/.cursor/skills/job-hunt/SKILL.mdgit clone --depth 1 https://github.com/PARK0301/job-agentWrote 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/park0301/job-agent/job-hunt)<a href="https://agentmods.dev/skills/park0301/job-agent/job-hunt"><img src="https://agentmods.dev/badge/skills/park0301/job-agent/job-hunt/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/park0301/job-agent/job-hunt"><img src="https://agentmods.dev/badge/skills/park0301/job-agent/job-hunt.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 | $0.00038 | $0.01491 |
| Opus 5 | $0.00019 | $0.00745 |
| Sonnet 5 | $0.00008 | $0.00298 |
| Haiku 4.5 | $0.00004 | $0.00149 |
Grade A, and why
job-hunt 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 11d 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 — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Job Hunt Skill
Paths (fixed)
- Project root:
~/job-agent - Profile:
~/job-agent/profile/profile.json - Answers:
~/job-agent/profile/answers.json - Today jobs:
~/job-agent/jobs/today.json - Cover letters / application folders:
~/job-agent/applications/{company}_{role}/(configapplications_dir; override inconfig.yaml)
Desktop daily launcher (no Terminal)
Install once:
cd ~/job-agent && ./scripts/install_desktop_shortcut.sh
- Job Hunt.app → starts debug Chrome + opens http://127.0.0.1:8787 (dashboard only; start hunts with Run job search).
In the dashboard: Prepare apply environment → one-time LinkedIn + SEEK login → then approve jobs (no Terminal needed).
Prefer the web UI over Terminal prompts. Canvas sidecars stay in sync for Cursor:
daily-jobs.canvas.tsxprofile-tune.canvas.tsxapplied-history.canvas.tsx
Applied history tab: compact table (company, title, link, status, date) + expandable row detail; multi-delete / single delete → DELETE /api/applications {job_ids}.
Onboarding / continuous tune
If profile/profile.json has placeholder name Your Name or empty/placeholder email ([email protected]), run onboard or tune before hunting:
cd ~/job-agent
python -m src.cli onboard # field-by-field wizard
python -m src.cli tune # OpenAI asks about CV/profile gaps; re-run anytime
python -m src.cli tune --once # up to 3 top questions
Or ask the user in chat for: name, email, skills, years, target_roles, target_countries, salary_min, visa_status, dealbreakers, linkedin_url, prefer_remote, notice_period — then save to profile/profile.json / answers.json.
Daily hunt checklist
- [ ] Load profile.json — if placeholder, run onboard/tune first
- [ ] Prefer: python -m src.cli launch (or Desktop Job Hunt.command)
- [ ] Search jobs (LinkedIn + JobsDB — default HK sources in config.yaml)
- [ ] OpenAI match each job (gpt-4o-mini); threshold 60 in config.yaml
- [ ] Filter score below threshold and dealbreakers
- [ ] Save jobs/today.json + daily_report.md
- [ ] Sync Canvas: automatic via hunt/launch, or `python scripts/sync_canvas.py`
What ships with it
5 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.
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.
- 11d ago First seen · 120 lines · 38 tokens per session scan A a78156ac17e8
job-hunt is a skill published in the GitHub repository PARK0301/job-agent (0 stars, last pushed 1mo ago), licensed MIT. It adds 38 tokens to every session and 1,491 once invoked, about $0.0002 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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