ai-job-search
ai-job-search (Mads Lorentzen, MIT) is “an AI-powered job application framework built on Claude Code. Fork it, fill in your profile, and let Claude evaluate jobs, tailor CVs, write cover letters, and prepare you for interviews.” A fork-and-customize repo that turns Claude Code into a full job-application assistant — and a clean example of this wiki’s skills + harness + orchestration thesis applied to a non-coding vertical.
Built on Claude Code’s native primitives
- Slash commands (
.claude/commands/) as the UI:/setup(ingest CV PDFs / LinkedIn / diplomas / guided interview),/scrape(rank jobs across Danish portals),/apply <url>(the orchestrator),/expand,/upskill,/reset. - Skills (
.claude/skills/) as modular logic: a corejob-application-assistant(profile, behavioral assessment, writing-style, job-evaluation framework, LaTeX templates, STAR examples), plusjob-scraperandupskill..agents/skills/holds TypeScript/Bun Danish-portal CLI tools. CLAUDE.mdas the master profile + workflow rules.- No MCP/webhooks — relies on Claude Code’s native agent dispatch + filesystem.
The /apply drafter–reviewer pipeline
Parse posting → score fit → draft CV + cover letter in LaTeX → spawn a reviewer subagent that independently researches the company and critiques the draft → drafter revises → compile + visually inspect PDFs → iterate to exact length → present with a checklist. The drafter/reviewer split is a concrete instance of agent-orchestration‘s adversarial verification and the two-agent review of agents-never-do-alone (“eliminating single-pass blind spots”).
Notable design patterns
- Output-grounded verification loop — it does not trust LaTeX to render correctly: it compiles
(lualatex/xelatex), reads the rendered pages, and applies targeted fixes (
\needspace,\enlargethispage) until visual inspection passes (CV exactly 2 pp, letter 1 p). Verifying the artifact, not the generation — a reliability discipline like the harness/durability threads. - Relevance-weighted CV cutting — on overflow it scores each line by job-relevance, uniqueness, and cover-letter dependency and cuts the lowest-value line, not the oldest.
- Token-efficient reviewer dispatch — reviewer gets drafts inline; checklist runs once.
- Latent-opportunity discovery — ranks by work actually performed, not job titles.
Significance
Most sources here are coding agents or general frameworks; ai-job-search shows the same skills+harness+subagent pattern generalizing to an everyday non-coding vertical (job hunting), language/country-agnostic. Credits Anthropic’s Claude Code and Mikkel Krogholm’s Danish job CLI skills. Stack: TypeScript 40% / Python 34% / TeX 25%; needs Claude Code CLI, Python 3.10+, Bun, TeX Live (lualatex + xelatex).
Caveat
A personal MIT repo / README — design described by its author, no independent evaluation; portal scraping is Denmark-focused.
Related
agentic-coding-harness · agent-skills · agentskills-spec · agent-orchestration · agents-never-do-alone · claude-code-best-practices · anthropic