Data Extraction From Documents
Extract structured data from documents into JSON or CSV
What You Can Do
You can systematically extract key information from multiple documents and normalize it into consistent, schema-enforced formats. Claude identifies entities (people, organizations, dates, products), maps relationships between them, standardizes values (currencies, units, date formats), and flags data quality issues—all while maintaining accuracy across batches of similar or diverse document types.
Features
automatically detects and classifies people, organizations, locations, products, dates, and domain-specific entities
captures connections between entities (contracts, dependencies, hierarchies, transactions)
standardizes currencies, units, date formats, and categorical data to consistent formats
ensures extracted data matches your required field structure and data types
extracts comparable data across multiple documents with consistent output structure
includes confidence scoring, missing value tracking, and anomaly flagging
generates structured formats ready for databases, analytics tools, or downstream processing
handles PDFs, research papers, financial reports, contracts, and industry publications
Example Output
Example 1: Research Paper Extraction
{
"title": "Machine Learning Applications in Healthcare",
"authors": ["Smith, J.", "Chen, L."],
"publication_year": 2023,
"key_findings": [
{"finding": "Model achieved 94% accuracy", "confidence": 0.98},
{"finding": "Reduced diagnostic time by 40%", "confidence": 0.95}
],
"methodologies": ["Random Forest", "Neural Networks"],
"data_quality_flags": ["missing_sample_size"]
}
Example 2: Contract Extraction (CSV)
contract_id,parties,effective_date,termination_date,payment_terms,renewal_clause,data_quality
CTR-2024-001,Acme Inc|Widget Corp,2024-01-15,2025-01-14,Net 30,12-month auto-renewal,complete
CTR-2024-002,Tech Solutions LLC|CloudServ Inc,2024-02-01,2026-01-31,Net 45,manual renewal required,missing_liability_cap
Example 3: Financial Report Metrics
{
"company": "TechCorp Inc",
"fiscal_year": 2023,
"metrics": {
"revenue_usd": 1250000000,
"gross_margin_percent": 72.5,
"operating_expenses_usd": 425000000,
"net_income_usd": 187500000
}
}
What's Included
- SKILL.md instruction file with entity taxonomy and extraction patterns:
- JSON schema template for common document types (contracts, research papers, financial reports):
- CSV header checklist for structured output validation:
- Entity classification framework (categories, subtypes, confidence thresholds):
- Data normalization reference guide (currency codes, date formats, unit conversions):
- Quality assessment rubric (confidence scoring, completeness validation, anomaly detection):
Who It's For
- Research coordinators — synthesizing findings from multiple academic papers into comparable datasets
- Legal analysts — extracting contract terms, obligations, and party information for compliance tracking
- Financial analysts — mining earnings reports and filings for key metrics and ratio analysis
- Business operations teams — building vendor/supplier databases from RFP responses and proposals
- Data scientists — preparing training data by normalizing information from heterogeneous document sources
Best For
- Batch extraction from 5+ structurally consistent documents (contracts, reports, research papers)
- Converting unstructured text into relational database-ready formats
- Normalizing multi-format data (currency, dates, units, categorical values) across documents
- Building entity databases and relationship maps from industry publications or internal records
- Quality-flagged extraction with confidence scoring and missing-data tracking
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