resume-parser

智能简历解析系统，支持PDF/Word/图片格式简历的结构化信息提取、岗位匹配度分析、优化建议生成。完全本地运行，无需外部API。使用场景：(1) 解析上传的简历文件提取核心信息，(2) 输入岗位JD计算简历匹配度，(3) 生成简历优化建议，(4) 导出结构化简历数据。

This skill appears to do what it says: local resume text extraction, prompt construction, and structured matching. Before installing/using: 1) Ensure you run the LLM locally or with an on-prem model — the skill builds prompts but relies on whatever model your agent uses; if that model is a remote API, resumes (PII) could be sent externally. 2) Install Tesseract OCR separately and the listed Python packages; run in a controlled environment. 3) Review and test with non-sensitive example resumes to confirm outputs and that the agent does not make network calls. 4) Add a JSON-output validator wrapper in deployment to catch model hallucinations (the scripts instruct the model to 'only return JSON' but that is not enforceable). 5) If handling real candidate data, ensure compliance with privacy rules and consider isolating processing (air-gapped or restricted network) to prevent accidental exfiltration.

Type: OpenClaw Skill Name: resume-parser Version: 1.0.1 The resume-parser skill bundle is a legitimate tool designed for extracting and analyzing resume data from PDF, Word, and image files. The Python scripts (extract_pdf.py, extract_docx.py, extract_image.py) use standard libraries like PyPDF2 and pytesseract for local file processing without any network activity or suspicious system calls. The instructions in SKILL.md and the prompt templates in match_jd.py and parse_resume.py are strictly focused on the stated task of structured data extraction and objective job matching, with no evidence of malicious prompt injection, data exfiltration, or unauthorized execution.