⚠️ 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.

Tabular Document Review Skill

Overview

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.

Table of Contents

• Tools
• Reference Guides
• Workflows
• Extraction Scenarios
• Troubleshooting
• Success Criteria
• Scope & Limitations
• Anti-Patterns
• Tool Reference

Tools

1. Document Discovery (

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

python scripts/document_discovery.py /path/to/leases --types pdf,docx,txt,md --min-size 1024

2. Extraction Aggregator (

scripts/extraction_aggregator.py

)

Aggregate multiple extraction result JSONs into a unified comparison matrix.

python scripts/extraction_aggregator.py \
  --results extraction_1.json extraction_2.json extraction_3.json

python scripts/extraction_aggregator.py \
  --results-dir ./extraction_results/ --json

python scripts/extraction_aggregator.py \
  --results-dir ./extraction_results/ \
  --format markdown \
  --output review_matrix.md

python scripts/extraction_aggregator.py \
  --results extraction_1.json extraction_2.json \
  --columns "Parties,Effective Date,Term,Governing Law"

Reference Guides

references/extraction_methodology.md

references/common_extraction_columns.md

Workflows

5-Step Document Review Pipeline

document_discovery.py

extraction_aggregator.py

extraction_aggregator.py

Parallel Processing Strategy

Agent Prompt Template

Each agent receives a prompt structured as:

You are reviewing {count} legal documents. For each document, extract the
following columns:

{column_definitions}

For each value extracted:
1. Provide the exact value found
2. Include the page number (PDF) or section/paragraph (DOCX/MD)
3. Rate your confidence: HIGH (exact match), MEDIUM (inferred), LOW (uncertain)
4. If not found, record "NOT FOUND" with confidence LOW

Output as JSON per the extraction schema.

Confidence Scoring

Output Format

Sheet 1: Document Review

Sheet 2: Summary

Extraction Scenarios

Contract Review

NDA Review

Troubleshooting

--types

extraction_methodology.md

common_extraction_columns.md

--columns

--format json

Success Criteria

• Extraction Coverage: 90%+ of defined columns populated across all documents
• Confidence Distribution: 70%+ of extractions rated HIGH confidence
• Citation Accuracy: Every extracted value includes verifiable page/section citation
• Processing Speed: 50+ documents processed within 30 minutes using parallel agents
• Matrix Completeness: Final matrix includes all documents and all columns with no orphan rows

Scope & Limitations

This skill covers:

• Document inventory and discovery across PDF, DOCX, TXT, and MD formats
• Aggregation of extraction results from parallel agent processing into unified matrix
• Pre-defined column sets for contracts, NDAs, employment agreements, and leases
• Confidence scoring and conflict detection for extracted values
• Markdown and JSON output formats

This skill does NOT cover:

• Actual document parsing or text extraction (requires external libraries or AI agents)
• OCR processing for scanned documents
• Excel/XLSX output generation (use JSON output and convert externally)
• Automated legal analysis or risk assessment of extracted values
• Document comparison or redlining between versions

Anti-Patterns

Tool Reference

scripts/document_discovery.py

Scan directory for legal documents and generate inventory manifest.

usage: document_discovery.py [-h] [--json]
                              [--types TYPES]
                              [--min-size MIN_SIZE]
                              [--max-size MAX_SIZE]
                              directory

positional arguments:
  directory             Path to directory containing documents

options:
  -h, --help            Show help message and exit
  --json                Output in JSON format
  --types TYPES         Comma-separated file extensions to include
                        (default: pdf,docx,doc,txt,md,rtf)
  --min-size MIN_SIZE   Minimum file size in bytes (default: 0)
  --max-size MAX_SIZE   Maximum file size in bytes (default: no limit)

scripts/extraction_aggregator.py

Aggregate extraction results into unified comparison matrix.

usage: extraction_aggregator.py [-h] [--json]
                                 [--results RESULTS [RESULTS ...]]
                                 [--results-dir RESULTS_DIR]
                                 [--format {markdown,json}]
                                 [--columns COLUMNS]
                                 [--output OUTPUT]

options:
  -h, --help            Show help message and exit
  --json                Output in JSON format (alias for --format json)
  --results             One or more extraction result JSON files
  --results-dir         Directory containing extraction result JSON files
  --format              Output format: markdown table or JSON (default: markdown)
  --columns             Comma-separated column names to include (default: all)
  --output              Write output to file instead of stdout