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Job search automation with LLM API pipeline mode and AI agent mode.

Project description

Job Hunter

Job hunting is repetitive work. Job Hunter automates the parts that don't need you: scraping listings across major job boards, scoring each one against your profile, tailoring your resume per application, and drafting cover letters. You handle the conversations.

Works interactively inside Claude Code or Codex (VS Code extensions), or runs fully autonomous via LLM API for unattended pipelines and GitHub Actions.

What It Does

  • Discovers jobs across supported job boards, aggregators, and company career pages — filtered by your titles, regions, and exclusions
  • Scores each listing against your career context so you know what to prioritize
  • Tailors your resume per job and generates a cover letter, ready for PDF export
  • Tracks applications with a dashboard and analytics so nothing slips through

Install

Requires Python 3.12 or 3.13. See the complete beginner-friendly SETUP.md for installation, PATH troubleshooting, API-key links, and agent permissions.

uv tool install job-hunter-kit

Standard install supports both agent and llm-api modes.

Quick Start

job-hunter init my-workspace
cd my-workspace
job-hunter doctor

Open the workspace in VS Code with Claude Code or Codex, then run /setup onboard, /setup context, /setup stories, and /setup resume. job-hunter doctor validates config and reports exact fixes.

Modes

Mode What runs When to use
agent Python prepares context; Claude Code or Codex (VS Code) skills handle scoring, tailoring, and writing Interactive daily review
llm-api Full autonomous pipeline; LLM APIs called inside Python Unattended runs and GitHub Actions

Set mode: in config/job_hunter.yml. Default is agent.

Daily Workflow

job-hunter hunt --region primary
job-hunter dash                      # web dashboard
job-hunter dashboard --no-interactive  # terminal

In agent mode, open the workspace in VS Code with Claude Code or Codex and use:

/job-hunter batch
/job-hunter one <url>
/job-hunter finalize

In llm-api mode, job-hunter hunt runs scrape → score → tailor → cover letter → PDF → tracker in one pipeline.

Optional Company Hunt

For company career pages that need a real browser, add targets to config/career_pages.yml, then run the Company Career Hunt workflow from GitHub Actions. Results are committed to outputs/browser_hunt/jobs.json.

CLI Reference

  • job-hunter init <workspace> — create a workspace
  • job-hunter doctor — check setup health
  • job-hunter hunt — discover and enrich jobs
  • job-hunter tailor — tailor resume for one or more job postings
  • job-hunter dash — open web dashboard in a native window (Applications, Insights, Analytics)
  • job-hunter dashboard, applications — terminal dashboard and application list
  • job-hunter update — update workspace assets, skills, and workflows after a package upgrade
  • job-hunter update --skills-only or --workflows-only — targeted refresh
  • job-hunter version — version and upgrade guidance

Bundled skills use hidden job-hunter internal ... commands. They are not part of normal user workflow.

Data Contract

Your data stays yours. Product updates must not overwrite config/, profile/, outputs/, or .env. Deterministic choices live in config/job_hunter.yml; career and writing guidance lives in profile/career_context.md; all job and application state lives in outputs/state/jobs.db.

See DATA_CONTRACT.md for the full contract.

Safety Boundaries

Job Hunter never submits applications, posts on LinkedIn, or contacts anyone automatically. It writes files under outputs/ for you to review. /job-hunter finalize and job-hunter update only touch system-owned paths — see the data contract above.

Development

uv sync --extra dev
uv run pytest tests/ -q --tb=short
uv run ruff format --check job_hunter tests scripts
uv run ruff check job_hunter tests scripts
uv run ty check job_hunter tests
uv build

MIT licensed. See CONTRIBUTING.md.

Documentation

Lineage

Job Hunter is the evolution of a single experiment: job-hunter-core and job-hunter-template worked as one system to prove the pipeline, the workspace model, and the agent skill layer — everything this package ships today.

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