保险条款分析
v1.0.0解析医疗险条款文档并提取32个结构化字段，含14个核心字段和18个增强字段，支持PDF/DOCX/TXT格式，输出标准JSON
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by马龙@long1973m
MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
OpenClaw
Benign
high confidencePurpose & Capability
Name/description describe extracting 32 fields from medical-insurance documents; the repository contains a small Python script that extracts text from PDF/DOCX/TXT and a detailed output-format spec. Declared dependencies (PyPDF2, python-docx) match the parser's needs. No unrelated credentials, binaries, or install steps are requested.
Instruction Scope
SKILL.md limits runtime behavior to: (1) run scripts/parse_document.py on user-supplied files or parse provided text, (2) map extracted text to the JSON schema in references/output-format.md. This stays within scope. Note: the README asserts the AI can '100% extract' Level 1 fields — that is an overconfident claim (extraction quality depends on document layout, OCR needs for scanned PDFs, ambiguous language, etc.). The skill does not handle OCR or images; it only extracts text content.
Install Mechanism
No install spec provided (instruction-only), so nothing is downloaded or executed beyond the included parse_document.py. Dependencies are declared in SKILL.md but not automatically installed by the skill; missing libraries will cause the script to raise ImportError. No external URLs, archives, or unusual install locations are used.
Credentials
The skill requires no environment variables, credentials, or config paths. The requested resources are proportional to its function (text parsing of user-provided documents).
Persistence & Privilege
The skill does not request always:true or other elevated persistence. It contains no code that modifies other skills or system-wide configs; it simply reads input files and prints extracted text.
Assessment
This skill appears coherent and focused on extracting structured fields from user-supplied insurance documents. Before installing/using it: (1) recognize it will read the full text of any uploaded document — do not supply documents containing personal/medical data unless you trust where the agent runs and how outputs are stored/transmitted; (2) ensure your environment has the required Python libraries (PyPDF2 and python-docx) or expect ImportError; (3) note it does not perform OCR on scanned images—provide selectable text PDFs or run OCR beforehand; (4) test with benign documents to confirm output matches the JSON schema and to validate edge cases; (5) be skeptical of the claimed '100% extraction' accuracy — manually review outputs for correctness.Like a lobster shell, security has layers — review code before you run it.
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License
MIT-0
Free to use, modify, and redistribute. No attribution required.