resume-parser
v1.0.1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。
⭐ 0· 638·2 current·3 all-time
by@ayalili
MIT-0
Download zip
LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
OpenClaw
Benign
high confidencePurpose & Capability
Name/description (local resume parsing, JD matching) align with included scripts and docs. Scripts implement PDF/DOCX/OCR extraction, build prompts for a local LLM to produce structured JSON, and implement matching rules — all coherent with the stated purpose. No unrelated binaries, env vars, or external services are required in the manifest.
Instruction Scope
Runtime instructions tell the agent to extract text from files, build prompts, and pass them to a (local) large model to produce JSON results. The skill does not instruct reading unrelated system files or sending data to external endpoints. NOTE: the claim of 'completely local' depends on the agent's model configuration — if the agent is configured to use a remote API, resume content could be sent externally even though the skill itself does not include network code.
Install Mechanism
No formal install spec (instruction-only), which is low-risk. README/SKILL.md recommend pip installing PyPDF2, python-docx, pillow, pytesseract and installing the Tesseract engine — standard local dependencies. No downloads from arbitrary URLs or packaged installers are present in the manifest.
Credentials
The skill declares no required environment variables, credentials, or config paths. All requested actions (file parsing, local OCR, prompt construction) are proportional to the stated purpose.
Persistence & Privilege
Flags show normal privileges (always:false, model invocation allowed). The skill does not request permanent presence or modify other skills or system-wide settings.
Assessment
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.Like a lobster shell, security has layers — review code before you run it.
latestvk97bt1y067n2aqsfwajen4kd1582mb4z
License
MIT-0
Free to use, modify, and redistribute. No attribution required.