AI Job Search is a Claude Code-based framework that runs on a user's machine to organize an AI-assisted job application process. It evaluates job postings, adapts CVs, writes cover letters, and helps prepare for interviews. The catalogue entries define commands, skills, instructions, an agent, and a setting for this workflow.
Borrowing it
Nothing to install: this file belongs to MadsLorentzen/ai-job-search. 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/MadsLorentzen/ai-job-search/master/.claude/commands/apply.mdgit clone --depth 1 https://github.com/MadsLorentzen/ai-job-searchWrote 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/commands/madslorentzen/ai-job-search/apply)<a href="https://agentmods.dev/commands/madslorentzen/ai-job-search/apply"><img src="https://agentmods.dev/badge/commands/madslorentzen/ai-job-search/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/commands/madslorentzen/ai-job-search/apply"><img src="https://agentmods.dev/badge/commands/madslorentzen/ai-job-search/apply.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 | $0.00000 | $0.09332 |
| Opus 5.5 | $0.00000 | $0.03733 |
| Sonnet 5.5 | $0.00000 | $0.01866 |
| Haiku 4.5 | $0.00000 | $0.00933 |
Grade C, and why
apply scanned grade C with 2 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 today.
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.
Recursive force deletehighDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
- **Custom template whose compile command redirects to `build/`:** also `rm -rf cv/build cover_letters/build` (`/add-template` Step 4 already says `/apply` must delete it). Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- **If the fetch returns HTTP 403, or the content is a login wall or an unrelated listing page, do not give up and do not draft from the title.** Follow the remaining escalation order in `09-web-research.md`: retry with How it starts
The opening of the file, as written. The whole thing — 419 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/apply - Drafter-Reviewer Job Application Workflow
You are orchestrating a two-agent job application workflow. The job posting is provided below as $ARGUMENTS (either a URL or pasted text).
Follow these steps exactly in order. Do not skip steps.
Standing rule — write new facts back to the profile. If the user confirms, corrects or supplies a fact that is not already in 01-candidate-profile.md — a metric, a project detail, a skill, a scope correction — update that file in the same turn. Do not leave it living only in the conversation or in a draft.
This is not bookkeeping. A fact that exists only in chat will be treated as unsupported by a later session and stripped from drafts as a fabrication. Anything absent from the sources does not exist as far as future drafting is concerned, and the loss is silent — a real achievement quietly disappears from every subsequent CV.
This rule is the input side of the Step 3 Factual Grounding Audit, not a competitor to it. The audit is deliberately strict: an ungrounded claim is removed, and it cannot tell a fabrication from a real fact the user stated out loud last week. That strictness is correct, and it is exactly why confirmed facts have to reach the sources in the same turn they surface. Write to 01-candidate-profile.md specifically — it is one of the audit's three sources, so a fact recorded there is grounded on the next run. Adding a fact to 01 that CLAUDE.md and the master CV simply do not mention is an absence, not a contradiction, and does not trip the audit's profile-consistency warning; if the new fact corrects something either of those states, fix it there too rather than leaving the two sources disagreeing.
Token-efficiency rules for this workflow:
- Never re-Read a file whose contents are already in your context from an earlier step. If you read it in Step 1, it is still available in Step 2.
- When dispatching the reviewer agent, pass draft content inline in the agent prompt rather than asking the agent to Read files you already have in memory.
- Run the full verification checklist exactly once, at the end (Step 6). The reviewer focuses on content critique, not verification.
- Step 5 (compile and inspect PDFs) is mandatory and non-skippable — page-break decisions are unpredictable, and source files that look fine often produce broken PDFs (orphaned entry titles, cover letters spilling to page 2, bullet fonts mismatching).
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.
- today Changed · +13 lines scan A → C 74be9883ca93
- 17d ago Changed · +2 lines 5912dc36cd19
- 29d ago Changed · +20 lines bd999baabb17
- 1mo ago Changed · +15 lines de2ef9a39e81
- 1mo ago First seen · 369 lines · 0 tokens per session scan A d8b31b3e6709
apply is a command published in the GitHub repository MadsLorentzen/ai-job-search (47,502 stars, last pushed today), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 9,332 tokens. A static security scan graded it C with 2 findings (recursive force delete, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other commands, from other repositories
resume
Generate a tailored resume and cover letter from a job description, score both, create DOCX files, and update the tracker.
resume-team
Run the role-separated, fail-closed Resume Team workflow against a job description.
writing-coach
Human-voice writing coach — rewrite resumes and cover letters with brevity, burstiness, plain language, and authentic impact. Blocks AI-sounding prose.
cover-letter
Create a one-page cover letter for a job description and generate the final DOCX.
find-jobs
Search live job boards for roles that match the master resume, then rank them by fit.
job-fit
Run the deterministic, digest-bound candidate-fit gate before any resume tailoring.