About this skill

Problem

Contract review often gets stuck in two low-value loops: manual clause-by-clause reading can miss material adverse terms, while fixed checklists struggle to adapt to purchase, service, labor, lease, and NDA structures. Engineering contexts need PDF content converted into searchable, comparable, push-ready risk fields, not just a vague summary.

How It Works

The skill processes contract PDFs through a pipeline:

Text extraction: uses PyMuPDF and pdfplumber as dual engines to reduce missing paragraphs from a single parser.
Type detection: classifies the contract into purchase, sales, service, labor, lease, or NDA, then extracts type-specific core fields.
Risk annotation: applies an industry risk library and three-tier grading with 🔴 high, 🟠 medium, and 🟡 low flags.
Report output: generates a structured report with a summary, key-clause table, and risk list; optionally pushes an interactive Feishu card.

Boundaries

It performs structured extraction and risk annotation, not legal advice. Scanned PDFs are only basically supported and usually need OCR first. Users must provide their own OpenAI-compatible API key and choose a model that supports the target language. Batch processing, Excel output, and API priority belong to paid plans.

Screenshots

Use Cases

Before signing a purchase contract, quickly extract amount, delivery, and breach fields, then flag high-risk clauses.
When reviewing an NDA, check confidentiality scope, duration, and breach liability to avoid missing adverse terms.
Organize labor contract fields such as salary, work hours, and social insurance into a structured report for internal review.
Push the completed contract risk report to Feishu and sync it with relevant approvers.

Best For

Risk review specialists who need to convert PDF contracts into key-field tables
Procurement engineers responsible for checking supplier agreements and purchase clauses
HR specialists who need to organize core labor contract information at scale
Operations leads who need to sync contract risk summaries into Feishu approval chains

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