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tabular-review

Extract structured spreadsheet rows with verbatim quotes from legal documents.

Updated May 26, 2026
Search Tags:#contract review#tabular extraction#M&A diligence#spreadsheet output#verbatim citations#evidence traceability
One-click install
npx skills add https://github.com/yachela/claude-for-legal-ar --skill tabular-review-yachela
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: tabular-review
Source: https://github.com/yachela/claude-for-legal-ar/tree/main/corporate-legal/skills/tabular-review
Command: npx skills add https://github.com/yachela/claude-for-legal-ar --skill tabular-review-yachela

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Contract diligence and batch document review require consistent answers across many agreements, but manual review often produces inconsistent wording, missing citations, and untraceable spreadsheet cells.

Core Features & Use Cases

  • One row per document, one column per field for predictable, spreadsheet-friendly outputs.
  • Typed schema with evidence: each cell includes an interpretation plus the verbatim quote and location that support it.
  • Explicit review states (not_present, unclear, needs_review) to keep the difference between “silent,” “ambiguous,” and “needs judgment” clear.
  • Batch M&A diligence workflow with sampling to validate/adjust the schema before fanning out to the full corpus.
  • Outputs for collaboration: Markdown for immediate review, plus CSV and Excel/Google Sheets formats for team workflows.

Quick Start

Ask the plugin to run a tabular review for your folder of target contracts and produce an auditable spreadsheet of the specified diligence fields for attorney verification.

Frequently Asked Questions about tabular-review

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I extract data from multiple contracts into a spreadsheet with verbatim citations?▼

Yes, batch M&A diligence can be automated by building a typed schema for your diligence fields, validating it on a sample of target contracts, and fanning out extraction to generate a structured spreadsheet with explicit review states like not_present, unclear, and needs_review.

What is the best way to handle missing or ambiguous clauses during contract diligence?▼

Yes, you can export your contract review results to CSV and Excel or Google Sheets formats, providing a structured spreadsheet-style output with one row per document and one column per field for immediate team collaboration and attorney verification.

How do I ensure evidence traceability when reviewing a large batch of legal documents?▼

Yes, you can export your contract review results to CSV and Excel or Google Sheets formats, providing a structured spreadsheet-style output with one row per document and one column per field for immediate team collaboration and attorney verification.

Can I export contract review results to Excel or Google Sheets for team collaboration?▼

Yes, batch M&A diligence can be automated by building a typed schema for your diligence fields, validating it on a sample of target contracts, and fanning out extraction to generate a structured spreadsheet with explicit review states like not_present, unclear, and needs_review.

Does contract review work for hundreds of documents at once?▼

To ensure evidence traceability when reviewing a large batch of legal documents, extract structured columnar data where each cell includes an interpretation plus the verbatim quote and its specific location, creating an auditable table for verification.

Why should I use a typed schema for M&A diligence instead of manual contract review?▼

The best way to handle missing or ambiguous clauses during contract diligence is to enforce explicit review states such as not_present, unclear, and needs_review, ensuring the difference between silent, ambiguous, and judgment-required fields remains clear in your spreadsheet output.

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