Borrowing it
Nothing to install: this file belongs to ARPeeketi/claude-resume-kit. 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/ARPeeketi/claude-resume-kit/main/.claude/skills/setup-extract/SKILL.mdgit clone --depth 1 https://github.com/ARPeeketi/claude-resume-kitWrote 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/arpeeketi/claude-resume-kit/setup-extract)<a href="https://agentmods.dev/skills/arpeeketi/claude-resume-kit/setup-extract"><img src="https://agentmods.dev/badge/skills/arpeeketi/claude-resume-kit/setup-extract/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/arpeeketi/claude-resume-kit/setup-extract"><img src="https://agentmods.dev/badge/skills/arpeeketi/claude-resume-kit/setup-extract.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 | $0.00016 | $0.01656 |
| Opus 5 | $0.00008 | $0.00828 |
| Sonnet 5 | $0.00003 | $0.00331 |
| Haiku 4.5 | $0.00002 | $0.00166 |
Grade A, and why
setup-extract 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 9d 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 — 171 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/setup-extract
User input: $ARGUMENTS
Parse $ARGUMENTS:
- File path to a paper (e.g.,
papers/Smith2024_catalyst.pdf,papers/project_report.tex) → read that file - Multiple paths separated by spaces → batch mode (process each sequentially)
- Empty → ask the user for the paper path or paste content
Startup
- Read
CLAUDE.md— check KB Corrections Log for known issues - Read
config.md— load Personal Info (to identify user's author position), Provenance Flags - Read
knowledge_base/extractions/_INVENTORY.md— see what's already extracted, avoid duplicates
If the paper is already in the inventory:
- Show the existing extraction path
- Ask: "This paper is already extracted. Re-extract (overwrite) or skip?"
- Wait for user response before proceeding
Phase 1: Read & Understand the Paper
Read the paper using the appropriate method:
- PDF files: Use the Read tool (supports PDF reading)
- .tex source: Read directly — often has more detail than the compiled PDF
- If both exist: Prefer .tex for content extraction, use PDF for figures/tables
While reading, collect:
- Full title, all authors, year, journal/venue, DOI (if available)
- The user's position in the author list (first, co-first, second, middle, last, corresponding)
- Publication status (check
config.mdProvenance Flags first, then infer: published / under review / draft / internal) - All computational methods, experimental techniques, software, and frameworks mentioned
- Quantitative results — speedups, accuracies, efficiencies, improvements over baselines
- Novelty claims — "first-ever", "new framework", "novel approach", etc.
- Collaboration indicators — other groups, institutions, shared resources
- Funding acknowledgments
Progress: "Reading paper... [title] by [first author] et al., [year]"
Phase 2: Clarify User's Role
If the user's contribution is not obvious from the paper (common for multi-author work), ask:
Questions to ask (skip any that are already clear from the paper):
- "What was your specific contribution? (e.g., all computational work, specific analysis, code development)"
- "Did you develop any tools, methods, or code used in this paper?"
- "Were there other groups or institutions involved? What was your group's role?"
- "Any quantitative results you can personally claim? (e.g., 'I ran all the simulations')"
- "Is there anything in this paper that should NOT appear on your resume? (e.g., collaborator's experimental data)"
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.
- 9d ago First seen · 171 lines · 16 tokens per session scan A cf200f205565
setup-extract is a skill published in the GitHub repository ARPeeketi/claude-resume-kit (239 stars, last pushed 6mo ago), licensed MIT. It adds 16 tokens to every session and 1,656 once invoked, about $0.0001 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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