skills/legal/bulk-document-extraction-review/SKILL.md
Extracts structured data from large sets of legal documents into tabular format for review, analysis, and reporting. Processes contracts, agreements, correspondence, filings, and other legal documents in bulk — extracting key terms, dates, parties, obligations, risks, and custom fields into organized tables. Use when conducting due diligence document review, bulk contract extraction, compliance audits across document sets, lease portfolio analysis, employment agreement review, regulatory filing review, or any task requiring structured extraction from multiple documents. Trigger keywords: tabular review, bulk extraction, document review table, data room review, batch document analysis, contract extraction, portfolio review, structured extraction, document comparison table.
npx skillsauth add casemark/skills bulk-document-extraction-reviewInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Extracts structured data from sets of legal documents into tabular format. Each document becomes a row; user-defined questions become columns. Produces reviewable tables with source citations, then supports analysis across the extracted dataset.
If extraction questions are not provided, propose a standard column set based on the document type.
Define extraction columns. Each column is a question asked of every document. Column types:
| Type | Description | Example | |---|---|---| | Verbatim | Extract exact language from the document | "What is the governing law clause?" | | Free response | Summarize or interpret | "Summarize the key obligations of the Seller" | | Classification | Yes/No or category | "Does this agreement contain a change-of-control provision?" | | Date | Extract specific dates | "What is the effective date?" | | Numeric | Extract amounts, percentages | "What is the liability cap amount?" | | List | Multiple items from same document | "List all defined terms" |
Standard column sets by document type:
Contracts / Agreements:
Correspondence / Emails:
Court Filings / Pleadings:
For each document in the set:
Compile results into a structured table:
| Doc # | Document Name | [Column 1] | [Column 2] | ... | [Column N] | Flags | |---|---|---|---|---|---|---|
Table conventions:
After extraction, analyze the dataset:
Based on the extracted table and analysis, produce:
Columns: Parties, Date, Contract type, Term, Renewal, Change of control, Assignment consent, Material financial terms, Restrictive covenants, IP provisions, Termination triggers, Red flags Analysis focus: Change-of-control provisions that could block closing, material contracts requiring consent, unusual termination rights, aggregate financial exposure
Columns: Property, Landlord, Tenant, Commencement, Expiration, Renewal options, Monthly rent, Escalation, CAM/NNN, Assignment/subletting, Termination rights, Restoration obligations Analysis focus: Upcoming expirations, below/above-market rents, assignment restrictions affecting transactions, aggregate lease obligations
Columns: Employee, Title, Start date, Term, Base compensation, Bonus/equity, Non-compete scope and duration, Non-solicit, IP assignment, Severance triggers, Change-of-control provisions, Governing law Analysis focus: Non-compete enforceability by jurisdiction, aggregate severance exposure, key person dependencies, inconsistencies across similar roles
Columns: Filing type, Filing date, Filer, Jurisdiction, Status, Key disclosures, Material changes from prior filing, Deficiencies noted, Response deadline Analysis focus: Compliance gaps, missed deadlines, material disclosure changes, cross-filing consistency
development
name: automated-contract-summary language: en description: Generates structured executive summaries of contracts using ML — captures key terms, party obligations, risk allocations, and compliance requirements in a standardized format. Optimized for high-volume review where speed and consistency matter. tags: - summarization - agreement - corporate --- # Automated Contract Summarization Produces standardized executive summaries of contracts using machine learning, capturing essential term
tools
Extracts regulatory obligations from dense regulations across jurisdictions. Breaks down multi-level regulations into clear article-level obligations, classifies applicability to a business, and prioritizes by risk level. Use when translating regulations into actionable compliance requirements.
development
Continuously monitors regulatory landscapes for changes relevant to a specific business. Ingests global regulatory updates, filters by relevance, summarizes impact, and produces an actionable change advisory. Use when tracking regulatory developments affecting a particular product or market.
testing
Compares an organization's existing compliance controls, policies, and procedures against extracted regulatory obligations to identify coverage gaps. Produces a remediation plan with prioritized actions. Use when assessing compliance maturity or preparing for regulatory audits.