ARPeeketi

ARPeeketi / claude-resume-kit

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Extract your papers once, generate tailored LaTeX resumes for every JD. Anti-fabrication controls, multi-perspective critique, AI fingerprint avoidance.

academic claude-code cover-letter cv job-search
18
4
100% credibility
Found Mar 17, 2026 at 18 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
TeX
AI Summary

A system for researchers and engineers to extract verified achievements from papers and generate tailored LaTeX resumes, cover letters, and critiques for specific job descriptions using structured AI interactions.

How It Works

1
🔍 Find the resume helper

You discover a handy tool designed for researchers and engineers to create accurate, tailored resumes from your papers and projects.

2
🧪 Try the example first

Jump in by using the ready-made sample data to quickly generate a resume and see how it pulls together achievements for a job.

3
📄 Add your own papers

Place your research papers or reports into the tool and answer simple questions about your exact role and status in each one.

4
📚 Build your achievement library

The tool gathers all your verified experiences into a personal collection you can reuse for every job application.

5
💼 Pick a job description

Add the details of a job you're applying for, and the tool tailors everything to match.

6
✨ Get your custom documents

Receive a perfectly fitted resume, cover letter, and helpful review with suggestions to make it even better.

🚀 Apply with confidence

Turn your files into polished PDFs and submit a standout application that truly reflects your work without exaggeration.

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Star Growth

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AI-Generated Review

What is claude-resume-kit?

This tool lets researchers extract data from papers and reports once via Claude Code CLI, building a knowledge base with structured details on contributions and status. For each job description, it generates tailored LaTeX resumes and cover letters, pulling relevant achievements while enforcing accuracy. Output compiles locally to PDF, keeping data private—no pasted resumes or generic rewrites.

Why is it gaining traction?

Unlike basic AI rewriters, it uses anti-fabrication controls like provenance flags and verb rules to prevent overclaiming, plus multi-perspective critique from ATS bots to reviewers across eight dimensions. AI fingerprint avoidance via banned words and scans makes output read human-written. The one-time extraction of research papers from PDFs and quick per-JD CLI commands (/make-resume, /critique) hook users tired of manual tailoring.

Who should use this?

Computational biologists, ML engineers, or PhD applicants with 5+ papers applying to academia, industry labs, or startups. Ideal for those juggling tenure-track JDs, FAANG roles, and consultancies, needing to extract papers from PDFs accurately without fabrication risks.

Verdict

Try the included examples first—solid docs and zero-setup demo show promise despite 18 stars and 1.0% credibility score signaling early maturity. Worth it for heavy applicants if you're okay scripting Claude sessions; skip if you lack LaTeX or real papers.

(178 words)

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