Agent skill
document-extract
Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/other/other/document-extract
SKILL.md
/document-extract
Extract and analyse content from scanned documents, PDFs, and document images using Sonnet sub-agents.
Usage
/document-extract <file-path>
/document-extract +Attachments/meeting-whiteboard.jpg
/document-extract +Attachments/spec-document.pdf
/document-extract +Attachments/handwritten-notes.png --type "meeting notes"
Instructions
This skill uses Sonnet model sub-agents for comprehensive document analysis and text extraction.
Phase 1: Document Loading
- Verify the file exists at the specified path
- Identify document type (PDF, image, photo of document)
- Note any context about document purpose
Phase 2: Comprehensive Analysis (Sonnet Sub-Agents)
Launch these sub-agents using model: "sonnet":
Agent 1: Text Extraction (Sonnet)
Task: Extract all text content from the document
- Read the file using the Read tool
- Perform comprehensive OCR on all visible text
- Preserve structure (headings, paragraphs, lists)
- Handle multiple columns if present
- Extract text from tables maintaining structure
- Note any text that is unclear or uncertain
Return: Complete text extraction with structure preserved
Agent 2: Structure Analysis (Sonnet)
Task: Analyse document structure and formatting
- Read the file
- Identify document type (letter, form, spec, notes, etc.)
- Map section hierarchy
- Identify tables, lists, and special formatting
- Note headers, footers, page numbers
- Identify logos, stamps, signatures
Return: Document structure map
Agent 3: Content Classification (Sonnet)
Task: Classify and categorise the content
- Read the file
- Determine document purpose
- Identify key entities (people, projects, dates, systems)
- Extract action items or tasks
- Find decisions or commitments
- Note any deadlines or dates mentioned
- Identify references to YourOrg projects or systems
Return: Classified content with entity extraction
Agent 4: Quality Assessment (Sonnet)
Task: Assess extraction quality and completeness
- Read the file
- Evaluate image/scan quality
- Identify areas with low confidence extraction
- Note any missing or obscured content
- Assess if re-scan might be needed
- Check for multiple pages
Return: Quality report with confidence scores
Phase 3: Compile Report
# Document Extraction
**Source**: {{filename}}
**Extracted**: {{DATE}}
**Document Type**: {{type}}
**Quality Score**: {{High/Medium/Low}}
## Summary
{{Brief description of document content and purpose}}
## Extracted Content
### Full Text
{{complete extracted text with formatting preserved}}
---
## Structured Data
### Key Information
| Field | Value |
|-------|-------|
| Date | {{if found}} |
| Author | {{if found}} |
| Subject | {{if found}} |
| Reference | {{if found}} |
### People Mentioned
{{list of names with context}}
### Dates & Deadlines
| Date | Context |
|------|---------|
{{dates found}}
### Projects/Systems Referenced
{{links to matching vault notes}}
## Tables Extracted
{{any tables found, formatted as markdown}}
## Action Items Found
- [ ] {{action items extracted from document}}
## Decisions/Commitments
{{any decisions or commitments mentioned}}
## Extraction Notes
### Confidence Assessment
- Overall quality: {{assessment}}
- Unclear sections: {{list any problematic areas}}
- Recommendations: {{re-scan suggestions if needed}}
### Areas of Uncertainty
{{text that couldn't be confidently extracted}}
## Suggested Actions
1. **Create Note**: {{suggest note type and title}}
2. **Link to**: {{suggest related notes}}
3. **Follow up**: {{any actions needed}}
Integration
After extraction, offer to:
- Create a new vault note from the content
- Append to an existing meeting or project note
- Create tasks from action items found
Notes
- Works with photos of whiteboards, handwritten notes, printed documents
- PDF support for text extraction
- Can recognise organization-specific terminology and systems
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