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/interview.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/interview)<a href="https://agentmods.dev/commands/madslorentzen/ai-job-search/interview"><img src="https://agentmods.dev/badge/commands/madslorentzen/ai-job-search/interview/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/interview"><img src="https://agentmods.dev/badge/commands/madslorentzen/ai-job-search/interview.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 | $0.02428 |
| Opus 5.5 | $0.00000 | $0.00971 |
| Sonnet 5.5 | $0.00000 | $0.00486 |
| Haiku 4.5 | $0.00000 | $0.00243 |
Grade A, and why
interview 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 1mo 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 — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/interview - Prepare for an Interview on a Tracked Application
You are preparing the user for a real, scheduled interview on one of their applications. The frameworks for this already exist - 07-interview-prep.md (STAR examples, tough questions, questions to ask, roleplay protocol) and the Company Research Checklist in 04-job-evaluation.md - and the /outcome archive records which stage the user is at and what earlier stages surfaced. This command wires them together into a stage-specific prep pack and an optional mock interview.
/apply optimizes what the company reads; /interview optimizes what the company hears. The bridge between them is consistency: the interviewer has read the submitted CV and cover letter, so everything prepared here must match what those documents claim.
Follow these steps in order.
Step 0: Parse Input
$ARGUMENTS may contain a company name (optionally with a role), e.g. /interview acme.
- With an argument: match against
job_search_tracker.csvrows (case-insensitive on company, then role). One match → proceed. Several → list and ask. None → this application isn't tracked; suggest/outcome <company>to register it first, or accept the posting and role details directly if the user wants to prep anyway. - Without an argument: list tracker rows whose status suggests a live process — an open status per the Tracker status vocabulary in
/outcome(interview,offer, or recentlyapplied;draftedis open but nothing was sent, so it never qualifies) — and ask which one. If the tracker is empty, ask for the company, role, and posting.
v1 preps for a specific application. Generic no-target practice is out of scope - if asked, prep against a real tracked application instead.
Step 1: Load the Application Context
- The archive (started by
/apply, maintained by/outcome): derive<company>_<role>by the Subfolder naming rule indocuments/README.md, then usedocuments/applications/<company>_<role>/.job_posting.md- the exact posting the user applied tocv_draft.texandcover_letter.tex- what was actually submitted. These are what the interviewer read; every talking point must be consistent with their claims.outcome.md- the stage reached so far and any recorded feedback from earlier stages. Feedback from stage N is the highest-value input for stage N+1 prep.
- Fallbacks (the application may predate
/outcome): posting via WebFetch on the tracker row'ssourceURL, or ask the user to paste it; CV viacv/main_<company>_<role>.*and cover letter viacover_letters/cover_<company>_<role>.*, deriving<company>_<role>by the Subfolder naming rule indocuments/README.md. Never widen those globs to the company alone: with two roles at one company it would prep you from the sibling role's documents. State plainly which context is missing rather than guessing - and suggest/outcome <company>to build the archive for next time. - Ask the user what this interview is (skip anything
outcome.mdalready records): stage (phone screen / technical / case / final round), date, format (phone, video, onsite), and who is interviewing (names and titles, if known). - Read the frameworks once - do not re-read them in later steps:
.claude/skills/job-application-assistant/07-interview-prep.md.claude/skills/job-application-assistant/01-candidate-profile.md.claude/skills/job-application-assistant/02-behavioral-profile.md.claude/skills/job-application-assistant/04-job-evaluation.md
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.
- 1mo ago Changed 6db14af66791
- 1mo ago First seen · 112 lines · 0 tokens per session scan A bb44121d39ed
interview 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 2,428 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.