upskill

upskill is a skill for Claude Code from ishandutta2007/Awesome-AI-Job-Hunting. It costs 48 tokens per session (3,569 once invoked), scanned A, a copy of upskill, MIT.

A job-search analysis tool that compares tracked job postings with a candidate profile. It identifies missing skills and creates a ranked learning plan with study resources.

In plain words
What is it for?
Use it to analyse all tracked jobs or one job-posting URL, produce a skill-gap heatmap, and generate an ordered study plan.
Why use it?
It turns a list of target jobs into a clear view of which skills matter most and what to learn first.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter. Also seen: reads .claude/ paths.

Good fit Use it to analyse all tracked jobs or one job-posting URL, produce a skill-gap heatmap, and generate an ordered study plan.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/ishandutta2007/awesome-ai-job-hunting/upskill
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.

Any agent
npx skills add ishandutta2007/Awesome-AI-Job-Hunting --skill upskill
Clone the repo
git clone --depth 1 https://github.com/ishandutta2007/Awesome-AI-Job-Hunting

Made for: Claude Code.

Wrote 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.

agentmods badge for upskill

README.md
[![agentmods](https://agentmods.dev/badge/skills/ishandutta2007/awesome-ai-job-hunting/upskill/github.svg)](https://agentmods.dev/skills/ishandutta2007/awesome-ai-job-hunting/upskill)
Your own site
<a href="https://agentmods.dev/skills/ishandutta2007/awesome-ai-job-hunting/upskill"><img src="https://agentmods.dev/badge/skills/ishandutta2007/awesome-ai-job-hunting/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.

agentmods 80×15 button for upskill

Your own site · 80×15
<a href="https://agentmods.dev/skills/ishandutta2007/awesome-ai-job-hunting/upskill"><img src="https://agentmods.dev/badge/skills/ishandutta2007/awesome-ai-job-hunting/upskill.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
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. A grade says what 26 rules found in the file — not that it is safe.
Origin 100% copy Near-identical to another mod 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 5.1 $0.00048 $0.03569
Opus 5 $0.00024 $0.01784
Sonnet 5 $0.00010 $0.00714
Haiku 4.5 $0.00005 $0.00357

Measured 12d ago against content hash 62e07abe6a7d, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, 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 12d 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

This is a copy

100% identical to upskill — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

.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. 12d 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 ishandutta2007/Awesome-AI-Job-Hunting (3 stars, last pushed 17d ago), 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. It is 100% identical to upskill, differing in 0 lines, and is treated as a copy.

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