Contract Risk Review

Contract Risk Analyzer — Upload contract PDF, AI auto-extracts key clauses and annotates risk points, outputs structured risk report (summary + key terms table + 🔴🟠🟡 three-tier risk list). Supports 6 contract types: purchase/sales/service/labor/lease/NDA. Trigger: contract review, contract risk, contract analysis, contract check

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

python

# 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

text

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

text

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

markdown

# 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

Contract Type	Core Fields
Purchase	Party A, Party B, items, amount, payment terms, delivery, quality standards, breach liability
Sales	Party A, Party B, items, amount, payment terms, delivery, quality warranty, breach liability
Service	Party A, Party B, services, amount, payment terms, service period, acceptance criteria, breach liability
Labor	Employer, Employee, position, salary, work location, work hours, contract term, social insurance
Lease	Landlord, Tenant, property, rent, deposit, term, renewal, maintenance responsibility
NDA	Party A, Party B, confidential content, period, scope, breach liability

---

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

python

from scripts.pdf_extractor import extract_text

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

AI Field Extraction

python

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

python

from scripts.risk_analyzer import annotate_risks

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

Report Generation

python

from scripts.report_generator import generate_report

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

Feishu Push

python

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

Tier	Price	Contracts/Mo	Features
Free	¥0	3/mo	Basic risk annotation, text summary
Standard	¥29/mo	30/mo	6 contract types, Excel report
Pro	¥99/mo	200/mo	Batch processing, risk comparison
Max	¥299/mo	Unlimited	API priority

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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

text

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

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