Borrowing it
Nothing to install: this file belongs to treymorgan/jobsearch-apply-mcp. 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/treymorgan/jobsearch-apply-mcp/main/.claude/skills/upskill/SKILL.mdgit clone --depth 1 https://github.com/treymorgan/jobsearch-apply-mcpWrote 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/treymorgan/jobsearch-apply-mcp/upskill)<a href="https://agentmods.dev/skills/treymorgan/jobsearch-apply-mcp/upskill"><img src="https://agentmods.dev/badge/skills/treymorgan/jobsearch-apply-mcp/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/treymorgan/jobsearch-apply-mcp/upskill"><img src="https://agentmods.dev/badge/skills/treymorgan/jobsearch-apply-mcp/upskill.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 | $0.00048 | $0.03569 |
| Opus 5 | $0.00024 | $0.01784 |
| Sonnet 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 10d 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.
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.
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.
- 10d ago First seen · 257 lines · 48 tokens per session scan A 62e07abe6a7d
upskill is a skill published in the GitHub repository treymorgan/jobsearch-apply-mcp (0 stars, last pushed 12d 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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