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/setup.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/setup)<a href="https://agentmods.dev/commands/madslorentzen/ai-job-search/setup"><img src="https://agentmods.dev/badge/commands/madslorentzen/ai-job-search/setup/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/setup"><img src="https://agentmods.dev/badge/commands/madslorentzen/ai-job-search/setup.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 | $0.06275 |
| Opus 5.5 | $0.00000 | $0.02510 |
| Sonnet 5.5 | $0.00000 | $0.01255 |
| Haiku 4.5 | $0.00000 | $0.00628 |
Grade A, and why
setup 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 7d 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 — 442 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/setup - Profile Onboarding
You are running the onboarding setup for the AI Job Search framework. Your goal is to collect the user's professional information and populate all profile files so the /apply workflow works out of the box.
There are three paths into setup. Step 0 picks the right one; all three converge on Step 3 (file generation) and Step 4 (confirmation).
Step 0: Welcome & Choose Path
If $ARGUMENTS contains --section <name>, skip directly to that section in Path C for an update-only flow. Do not run the path-selection prompt below.
Otherwise, first check where this working copy would publish to — before anything is
written, not after (the Step 4 privacy note fires only once every file is already on
disk, which is too late to inform the decision). Run git remote get-url origin; if the
command fails (no remote, or not a git checkout), skip this check silently. If there is
a GitHub origin, check it with gh repo view <owner/repo> --json visibility,isFork
when gh is available. If the origin is a public fork of the template — or its
visibility cannot be determined — warn now and wait:
Heads-up before we start: your
originpoints at<owner/repo>, which is a public GitHub fork. This setup writes your personal data (name, contact details, employment history, salary expectations) into tracked files, and anything you commit and push to that fork is visible to anyone. Two safe options: keep your profile commits local and never push them, or push to a private repository instead — SETUP.md section 8 has the two-minute private-remote recipe. Want to continue with the setup?
Wait for the user's confirmation before showing the path prompt. A private origin, no origin, or a non-fork remote needs no warning — continue silently.
Then, before greeting the user, scan the documents/ folder. Use Glob with documents/**/* and count files per subfolder (cv/, linkedin/, diplomas/, references/, projects/, applications/).
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.
- 7d ago Changed 9f0a18014ece
- 29d ago Changed · +5 lines 4d648949f79c
- 1mo ago Changed · +4 lines ee6ddee2adbc
- 1mo ago First seen · 433 lines · 0 tokens per session scan A 70e1136a6fcc
setup 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 6,275 tokens. 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.
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.