⚠️ 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

Reference	Purpose
references/extraction_methodology.md	Document extraction best practices, JSON schema, agent prompts
references/common_extraction_columns.md	Pre-defined column sets for contracts, NDAs, employment, leases

Workflows

5-Step Document Review Pipeline

Step	Action	Tool	Output
1. Gather Requirements	Define document folder, output filename, columns to extract	Manual	Column list, file path
2. Discover Documents	Scan directory for target documents	document_discovery.py	Document manifest JSON
3. Process Documents	Extract values per column with citations (parallel agents)	AI agents (external)	Per-document extraction JSONs
4. Collect Results	Aggregate extraction JSONs into unified matrix	extraction_aggregator.py	Consolidated matrix
5. Generate Output	Export as markdown table or structured JSON	extraction_aggregator.py	Final deliverable

Parallel Processing Strategy

Agents	Documents per Agent	Use When
1	All	1-5 documents
2-3	ceil(N/agents)	6-15 documents
4-6	ceil(N/agents)	16-40 documents
7-10	ceil(N/agents)	41-100 documents
10 (max)	ceil(N/10)	100+ documents

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

Level	Color Code	Definition
HIGH	Green	Exact value found with clear citation
MEDIUM	Yellow	Value inferred from context; multiple possible interpretations
LOW	Red / Not Found	Value uncertain or not found in document

Output Format

Sheet 1: Document Review

Document	Parties	Effective Date	Term	Governing Law	...
contract_a.pdf	Acme / Beta [p.1]	2026-01-15 [p.2]	3 years [p.3]	Delaware [p.12]	...
contract_b.pdf	Gamma / Delta [p.1]	NOT FOUND	2 years [p.4]	New York [p.10]	...

Sheet 2: Summary

Metric	Value
Documents processed	25
Columns extracted	8
Average confidence	87%
Not found rate	12%

Extraction Scenarios

Contract Review

Column	What to Extract
Parties	All contracting parties with full legal names
Effective Date	Contract effective or execution date
Term	Duration of the agreement
Renewal	Auto-renewal terms and notice period
Governing Law	Jurisdiction governing the agreement
Liability Cap	Maximum liability amount or formula
Indemnification	Indemnification obligations and scope
IP Ownership	Intellectual property ownership provisions
Termination Rights	Termination triggers and notice requirements
Data Protection	Data protection or privacy obligations

NDA Review

Column	What to Extract
Parties	Disclosing and receiving parties
Type	Mutual or one-way
Definition Scope	How "confidential information" is defined
Exceptions	Standard exceptions to confidentiality
Term	Duration of confidentiality obligations
Survival	Survival period after termination
Return/Destruction	Obligations on termination
Remedies	Available remedies for breach

Troubleshooting

Problem	Cause	Solution
Discovery finds 0 documents	Wrong path or file types	Verify path exists; check --types matches actual file extensions
Extraction JSONs have wrong schema	Agent prompt incomplete	Use the extraction schema from extraction_methodology.md
Aggregator shows conflicts	Multiple values for same cell	Review source documents; aggregator marks conflicts for manual review
High "NOT FOUND" rate	Columns too specific for document type	Use column definitions from common_extraction_columns.md; broaden definitions
Confidence all LOW	Agent unable to locate values	Check column definitions are specific enough; verify document is readable
Aggregator crashes on large set	Too many result files loaded at once	Process in batches of 50 results; use --columns to limit output width
Markdown table misaligned	Long values or special characters	Use --format json for machine processing; truncate long values
Missing citations	Agent did not include page/section references	Reinforce citation requirement in agent prompt; check extraction schema

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

Anti-Pattern	Why It Fails	Better Approach
Vague column definitions	"Date" could match dozens of dates in a contract	Use specific definitions: "Effective Date" with guidance on where to look
Skipping document discovery	Unknown document count leads to wrong agent allocation	Always run discovery first; use manifest for pipeline planning
Ignoring LOW confidence results	Missing or uncertain data treated as fact	Review all LOW confidence cells manually; flag in final report
Processing 100+ docs with 1 agent	Slow, context window overflow, quality degradation	Use parallel processing: ceil(N/10) documents per agent, max 10 agents
No citation requirement	Cannot verify extracted values against source	Require page/section citation for every extraction; reject uncited values

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