Extract resume text locally and rank candidates by fit on a 0-100 scale.
npx skills add https://github.com/Moisesjr20/gueclaw --skill rag-curriculos
Please help me install this Agent Skill. Skill: rag-curriculos Source: https://github.com/Moisesjr20/gueclaw/tree/main/.agents/skills/rag-curriculos Command: npx skills add https://github.com/Moisesjr20/gueclaw --skill rag-curriculos
This skill automates reading a folder of resumes (PDFs and DOCX), extracts text locally, and compares it against a job description to produce a structured Markdown ranking of candidates by fit.
Provide a directory of resumes and a job description, then run the evaluation to generate a ranked Markdown list.
High-intent search queries and answers about installing and using this skill.
Ranking multiple resumes against a job description is automated by extracting text locally from PDF and DOCX files, scoring candidate fit on a 0-100 scale, and generating a structured Markdown list for quick shortlisting.
Local text extraction from PDF and DOCX resumes works by using native Python libraries to parse file contents directly in a self-contained environment, reading candidate data offline without external API calls.
Yes, resume matching runs entirely in a local, self-contained environment without API calls, relying on Python libraries for PDF and DOCX parsing to extract and evaluate text against job descriptions.
Shortlisting candidates from a folder of resumes is best handled by batch extracting text, scoring each profile against the job description on a 0-100 scale, and outputting a ranked Markdown list with names and contact info.
Yes, the resume ranking workflow requires Python dependencies such as pypdf2 and python-docx to parse and extract text from PDF and DOCX files locally before scoring them against the job description.
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