upskill

A job-search learning planner that compares saved job postings with your current skills and points out what you are missing.

In plain words
What is it for?
Use it to review all tracked jobs or one job posting, see a visual summary of skill gaps, and get study resources arranged in a suggested order.
Why use it?
It turns a scattered list of possible skills into a ranked plan, so you can focus your study time on gaps that matter for the jobs you want.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/madslorentzen/ai-job-search/upskill
Any agent
npx skills add MadsLorentzen/ai-job-search --skill upskill
Clone the repo
git clone --depth 1 https://github.com/MadsLorentzen/ai-job-search

Made for: Claude Code, Codex.

Per session 48 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,569 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00048 $0.03569
Opus 5 $0.00024 $0.01784
Sonnet 5 $0.00010 $0.00714
Haiku 4.5 $0.00005 $0.00357

Measured 2d ago against content hash 62e07abe6a7d, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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 2d 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.

Origin

Copies of this mod

2 near-identical copies found in the catalogue:

  • upskill — 100% identical, 0 lines differ
  • upskill — 100% identical, 0 lines differ
.claude/skills/upskill/SKILL.md · 257 lines

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 in job_search_tracker.csv, merged with ranked postings (rank_score >= 45) from job_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 /upskill with 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

  1. 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
  2. For each row, note the role, company, and fit_rating. The fit_rating column 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.
  3. Read job_scraper/seen_jobs.json. Keep entries with "status": "ranked" and rank_score >= 45 — the Moderate Fit floor from 04-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 its title, company, rank_score, and — when present — its recorded gaps. An entry with no gaps field (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 --all re-scores them." Never back-fill a missing gaps field by guessing from the title.
  4. Read .claude/skills/job-application-assistant/01-candidate-profile.md to get the candidate's current skills and experience.
  5. 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.

Read the full file on GitHub · 257 lines

Changes

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.

  1. 2d ago First seen · 257 lines · 48 tokens per session scan A 62e07abe6a7d

Subscribe to this mod's changes

upskill is a skill published in the GitHub repository MadsLorentzen/ai-job-search (39,400 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. 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.

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