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Extract structured data from multiple documents into comparison matrix with citations.
Passed 21 of 23 test cases with the skill, and 9 of 23 without it.
and 16 more in the full report
13 fixed8 passed either way1 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 13 test cases the skill fixed. Same model, same prompt, with and without the skill.
An agent reviewed an NDA document and confirmed that no auto-renewal clause exists anywhere in the text. What string value and confidence level must be recorded in the cell?
| Checked | Without skill | With skill |
|---|---|---|
| Records 'NOT FOUND' as the extracted value. | Missed | Passed |
| Assigns LOW confidence to the entry. | Missed | Passed |
The instructions your model reads when the skill loads.
⚠️ EXPERIMENTAL — This skill is provided for educational and informational purposes only. It does NOT constitute legal advice. All responsibility for usage rests with the user. Consult qualified legal professionals before acting on any output.
Production-ready toolkit for extracting structured data from multiple legal documents into a comparison matrix with citations. Supports user-defined extraction columns, parallel processing with up to 10 agents, confidence scoring, and output in markdown table or structured JSON. Designed for legal teams performing bulk contract review, NDA comparison, employment agreement analysis, and lease review.
Before the review, confirm these inputs. If any is unknown or vague, ASK — do not assume:
Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the matrix.
scripts/document_discovery.py)Scan a directory for legal documents and generate an inventory manifest.
python scripts/document_discovery.py /path/to/contracts
python scripts/document_discovery.py /path/to/ndas --types pdf,docx --json
test cases passed, vs 9 / 23 without
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