Contract Risk Analyzer

Contract Risk Analyzer

Upload contract PDF → AI auto-extracts key clauses → risk annotation → structured risk report.

No legal judgment — only structured extraction and risk annotation.

Core Features

• Contract type detection: Auto-identifies 6 types (purchase/sales/service/labor/lease/NDA)
• Text extraction: PyMuPDF + pdfplumber dual-engine for complete coverage
• AI structured extraction: OpenAI-compatible API, user provides their own key
• Risk annotation: Industry risk library + three-tier classification (🔴 High 🟠 Medium 🟡 Low)
• Feishu push: Risk report auto-sent as Feishu interactive card

AI Agent Workflow

# 1. Upload contract PDF and analyze
result = analyze_contract(
    pdf_path="/path/to/contract.pdf",
    api_key="sk-...",                  # OpenAI-compatible API Key (user-provided)
    base_url="https://api.openai.com/v1",
    model="gpt-4o-mini",
    user_focus="payment cycle and breach liability",
)

# result returns:
# {
#   "contract_type": "Purchase Contract",
#   "summary": "This contract is a purchase agreement between XXX and YYY for...",
#   "key_terms": [
#     {"category": "Parties", "content": "...", "risk": ""},
#     ...
#   ],
#   "risks": [
#     {"level": "🔴", "item": "Amount clause left blank", "suggestion": "..."},
#     ...
#   ],
#   "report_markdown": "..."  # Full report in Markdown
# }

# 2. Push Feishu card (optional)
send_feishu_notification(
    open_id="ou_xxx",
    report_markdown=result["report_markdown"],
    contract_type=result["contract_type"],
)

Usage Examples

Example 1: Analyze a Purchase Contract

User: Review this purchase contract
[upload contract.pdf]

→ PDF text extraction → type detection → AI extraction → risk annotation → Returns: risk report (summary + key terms table + risk list)

Example 2: Review Labor Contract and Push to Feishu

User: Review this labor contract and send me the report
[upload labor_contract.pdf]

→ Analysis complete → Feishu interactive card pushed to user

Output Format

Risk Report Structure

# Contract Risk Review Report

**Contract Type:** Purchase Contract
**Review Date:** 2026-04-20
**Disclaimer:** Auto-generated by AI, for reference only, not legal advice.

## 1. Summary

[200-word summary of core contract content]

## 2. Key Terms

| Category | Content | Risk Note |
|----------|---------|-----------|
| Parties | Party A: XXX Co. | — |
| Amount | ¥100,000 | — |
| Payment Terms | Within 30 days after signing | ⚠️ No specific latest payment date |
| Breach Liability | Breaching party compensates losses | ⚠️ No liquidated damages clause |
| ... | ... | ... |

## 3. Risk Points

### 🔴 High Risk (Requires Attention)
1. **Amount clause blank**: Contract amount not fully stated...
2. **Unilateral breach liability**: Party A's liability significantly lighter than Party B's...

### 🟠 Medium Risk (Review Recommended)
1. **Payment without clear deadline**: ...
2. **NDA period exceeds 2 years**: ...

### 🟡 Low Risk (Note)
1. **No termination clause**: ...

Contract Types & Extracted Fields

Risk Checklist

🔴 High Risk (Red Flags)

• Amount undefined or left blank
• Severely asymmetric breach liability (one-sided)
• Liquidated damages > 30% of actual loss
• Jurisdiction in distant/foreign court unfavorable to us
• No termination clause or extremely strict termination conditions
• Unlimited joint liability clause
• Standard terms not bolded or not explicitly disclosed

🟠 Medium Risk (Caution)

• Payment without clear deadline
• NDA period exceeds 2 years
• Non-compete scope too broad
• IP ownership ambiguous
• Force majeure clause missing or too loose
• Dispute resolution unfavorable for enforcement

🟡 Low Risk (Note)

• Notice method not specified
• Number of contract copies not agreed
• Attachment-body inconsistency risk
• Language version conflict not addressed

Technical Details

PDF Text Extraction

from scripts.pdf_extractor import extract_text

# PyMuPDF primary, pdfplumber fallback
text = extract_text(pdf_path)

AI Field Extraction

from scripts.ai_extractor import extract_fields

fields = extract_fields(
    text=contract_text,
    contract_type="Purchase",
    api_key="sk-...",
    base_url="https://api.openai.com/v1",
    model="gpt-4o-mini",
)

Risk Annotation

from scripts.risk_analyzer import annotate_risks

risks = annotate_risks(
    text=contract_text,
    fields=extracted_fields,
)

Report Generation

from scripts.report_generator import generate_report

report = generate_report(
    contract_type=contract_type,
    summary=summary,
    key_terms=key_terms,
    risks=risks,
)

Feishu Push

from scripts.feishu_notifier import send_feishu_notification

notification = send_feishu_notification(
    open_id="ou_xxx",
    report_markdown=report_markdown,
    contract_type=contract_type,
    contract_name="Contract Name",
    risk_summary={"🔴": 1, "🟠": 2, "🟡": 0},
    top_risks=[{"level": "🔴", "item": "High risk item"}],
)

# Agent sends via feishu_im_user_message tool:
feishu_im_user_message(
    action="send",
    receive_id_type="open_id",
    receive_id=notification["open_id"],
    msg_type="interactive",
    content=notification["card_json"],
)

Graceful degradation: If Feishu authorization is not granted, only output local report — do not error out.

Configuration

Users provide their own API key (OpenAI-compatible), supports:

• OpenAI API, Azure OpenAI, Claude (via compatible proxy), DeepSeek, any OpenAI-compatible API

Pricing

FAQ

Q: Are scanned PDFs supported? A: Basic support for text-based PDFs. Scanned documents need OCR — pair with miaoda-doc-parse skill.

Q: Are English contracts supported? A: Yes, configure a model that supports English in base_url.

Q: Is contract content stored? A: No — content is processed and deleted immediately, fully compliant with data privacy requirements.

Q: How is API configured? A: User passes api_key at call time via the api_key parameter, supports OpenAI-compatible endpoints.

Q: Can multiple contracts be processed in batch? A: Pro tier supports batch processing — upload multiple PDFs and generate comparison reports.

Directory Structure

contract-risk-analyzer/
├── SKILL.md
├── README.md
├── references/
│   ├── contract_types.md
│   └── risk_library.md
└── scripts/
    ├── main.py               # Entry: analyze_contract()
    ├── pdf_extractor.py      # PyMuPDF + pdfplumber text extraction
    ├── contract_type_detector.py  # Type detection
    ├── ai_extractor.py        # AI field extraction
    ├── risk_analyzer.py       # Risk annotation
    ├── report_generator.py    # Report generation
    ├── feishu_notifier.py     # Feishu card push
    └── test_contract_analyzer.py

License

MIT

For paid plans, visit YK-Global.com