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/skills/upskill/SKILL.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/skills/madslorentzen/ai-job-search/upskill)<a href="https://agentmods.dev/skills/madslorentzen/ai-job-search/upskill"><img src="https://agentmods.dev/badge/skills/madslorentzen/ai-job-search/upskill/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/skills/madslorentzen/ai-job-search/upskill"><img src="https://agentmods.dev/badge/skills/madslorentzen/ai-job-search/upskill.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk warn
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Prompt Injection · line 187 Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
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 | $0.00048 | $0.03569 |
| Opus 5.5 | $0.00019 | $0.01428 |
| Sonnet 5.5 | $0.00010 | $0.00714 |
| Haiku 4.5 | $0.00005 | $0.00357 |
Grade A, and why
upskill 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 — 257 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Upskill
Overview
/upskill analyses jobs you have tracked and your current profile to identify skill gaps, then produces a heatmap of those gaps and a learning plan with concrete, web-searched study resources and a recommended study order.
Invocation
/upskill— aggregate mode: analyses all jobs injob_search_tracker.csv, merged with ranked postings (rank_score >= 45) fromjob_scraper/seen_jobs.json/upskill <URL>— targeted mode: analyses a single job posting fetched from the URL
Step 1: Detect Mode
Check whether the user provided a URL argument:
- If the invocation was
/upskillwith no argument → aggregate mode - If the invocation was
/upskill <URL>→ targeted mode, store the URL for Step 2
In targeted mode, derive a slug from the job title and company for the report filename (e.g. guardsix-senior-ai-engineer). You will fetch the posting in Step 2.
Step 2: Load Data
Aggregate mode
- Read
job_search_tracker.csv. Extract all rows. The columns are:date, company, sector, role, role_type, channel, status, contact_person, fit_rating, notes, cv_file, cover_letter_file, source, deadline - For each row, note the
role,company, andfit_rating. Thefit_ratingcolumn is a 0–100 score where 100 = perfect fit. You will use it to weight gaps — a lower fit rating means the role exposed more gaps. - Read
job_scraper/seen_jobs.json. Keep entries with"status": "ranked"andrank_score >= 45— the Moderate Fit floor from04-job-evaluation.md(below that, a job is Weak/Poor Fit and would otherwise dominate the heatmap with jobs the user shouldn't chase). For each kept entry, note itstitle,company,rank_score, and — when present — its recordedgaps. An entry with nogapsfield (ranked before gap persistence existed) is skipped, counted, and reported once in the terminal: "N ranked jobs were scored before gap persistence and contribute nothing;/rank --allre-scores them." Never back-fill a missinggapsfield by guessing from the title. - Read
.claude/skills/job-application-assistant/01-candidate-profile.mdto get the candidate's current skills and experience. - Check
upskill/for the most recent aggregate report file (report-YYYY-MM-DD.md) — if one exists, note its date and load it for the diff in Step 8.
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 First seen · 257 lines · 48 tokens per session scan A 62e07abe6a7d
upskill is a skill published in the GitHub repository MadsLorentzen/ai-job-search (47,502 stars, last pushed today), licensed MIT. It adds 48 tokens to every session and 3,569 once invoked, about $0.0002 per session on Opus 5.5. 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 skills, from other repositories
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Format CVs for academic positions with publications, grants, and teaching.
resume-tailor
Use when tailoring a resume or CV to a job, creating a general resume variant, editing resume sections, reviewing ATS readability or job fit, or preparing an application cover letter.
onboarding
Use when creating or updating a candidate master profile from resumes, career notes or supplied facts, organizing career-source provenance, or resolving profile identity, conflicts and disclosure preferences.
career-gap-planner
A planning tool that compares your current skills and experience with the requirements of a target job. It separates essential requirements from useful extras and identifies what can realistically be learned.
ai-job-search
An open-source job-application framework built for Claude Code that uses a personal profile to assess job matches, tailor CVs, write cover letters, and prepare interview responses. A CV is a document describing your work history and skills.
principal-agent
A Chinese-language guide based on the principal-agent idea: the person who owns a decision or its result may have different incentives from someone acting on another person's behalf. It applies this lens to jobs, companies, and partnerships.