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document-extraction

document-extraction

github.com/melodic-softwareDocuments2845 installsUpdated today
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About document-extraction

Document Extraction Skill helps analysts systematically mine requirements from existing documents, including specifications, transcripts, and regulatory material. It detects requirement patterns and supports categorization, deduplication, and different levels of human review.

Key features

  1. 01Handles Markdown, text, PDF, and Word
  2. 02Detects explicit and EARS requirement patterns
  3. 03Records extracted requirements and source locations
  4. 04Categorizes requirements
  5. 05Deduplicates semantically and flags items for review

Use cases

  1. 01Extract requirements and action items from meeting notes
  2. 02Convert legacy documents into structured requirements
  3. 03Mine compliance requirements from regulatory material
  4. 04Analyze competitor documents for feature requirements

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/melodic-software/claude-code-plugins --skill document-extraction

After installing, type/document-extractionin Claude Code to confirm it loaded.

Bundled filesreferences/document-types.mdreferences/extraction-prompts.md

Details

From the project README

Document Extraction Skill

Extract requirements from existing documentation sources for systematic requirement mining.

When to Use This Skill

Keywords: extract requirements, document mining, PDF requirements, transcript analysis, parse document, existing documentation, legacy requirements, competitive analysis

Invoke this skill when:

  • Mining requirements from existing documents
  • Processing meeting transcripts for requirements
  • Extracting requirements from competitor products
  • Analyzing regulatory documents for compliance requirements
  • Converting legacy documentation to structured requirements

Supported Document Types

TypeExtensionExtraction Method
Markdown.mdDirect Read
Text.txtDirect Read
PDF.pdfRead tool (PDF support)
Word.docxRead tool
Web PageURLWebFetch tool
Meeting Notes.md, .txtTranscript patterns
Specification.md, .docxRequirement patterns

Extraction Workflow

Step 1: Document Assessment

Analyze the document to determine extraction strategy:

document_assessment:
  path: "{file path or URL}"
  type: "{detected document type}"
  size: "{approximate size}"
  structure:
    has_sections: true|false
    has_lists: true|false
    has_tables: true|false
  quality:
    formal_language: true|false
    clear_requirements: true|false
    needs_interpretation: true|false
Step 2: Pattern Matching

Apply requirement detection patterns:

Explicit Requirement Markers:

- "The system shall..."
- "The system must..."
- "Users should be able to..."
- "REQ-XXX:"
- Numbered requirements (1.1, 1.2, etc.)

EARS Patterns:

- "When [trigger], the [system] shall [response]"
- "While [state], the [system] shall [behavior]"
- "Where [feature], the [system] shall [behavior]"
- "If [condition], then the [system] shall [response]"

Implicit Requirement Indicators:

- "It is important that..."
- "We need..."
- "The goal is to..."
- "Users expect..."
- "Performance should..."
Step 3: Requirement Extraction

For each identified requirement:

extracted_requirement:
  id: REQ-{sequence}
  text: "{cleaned requirement statement}"
  source: document
  source_file: "{file path}"
  source_location: "{section/page/line}"
  original_text: "{exact text from document}"
  type: functional|non-functional|constraint|assumption
  confidence: high|medium|low
  extraction_method: explicit|pattern|inferred
  needs_review: true|false
  review_notes: "{why review needed}"
Step 4: Categorization

Categorize extracted requirements:

categories:
  functional:
    - features
    - behaviors
    - interactions
  non_functional:
    - performance
    - security
    - usability
    - reliability
    - scalability
  constraints:
    - technical
    - business
    - regulatory
  assumptions:
    - environmental
    - user_behavior
    - dependencies
Step 5: Deduplication

Identify and merge duplicate requirements:

deduplication:
  strategy: semantic_similarity
  threshold: 0.8
  action: merge|flag_for_review
  merged_requirements:
    - id: REQ-merged-001
      sources: [REQ-001, REQ-015]
      text: "{consolidated requirement}"

Document-Specific Strategies

Meeting Transcripts
transcript_extraction:
  focus_on:
    - Action items
    - Decisions made
    - Requirements discussed
    - Concerns raised
  patterns:
    - "We decided that..."
    - "The requirement is..."
    - "Action item:"
    - "TODO:"
    - "Need to..."
  speaker_context:
    - Note who said what
    - Weight by speaker role
Regulatory Documents
regulatory_extraction:
  focus_on:
    - Mandatory requirements ("shall", "must")
    - Prohibited actions ("shall not", "must not")
    - Conditional requirements ("if...then")
  compliance_mapping:
    - Reference section numbers
    - Note effective dates
    - Track version/revision
Competitor Analysis
competitor_extraction:
  focus_on:
    - Feature descriptions
    - User capabilities
    - Unique selling points
  output:
    - Feature requirements
    - Differentiation opportunities
    - Gap identification
  confidence: low  # Based on external observation
Legacy Specifications
legacy_extraction:
  focus_on:
    - Existing requirements
    - System behaviors
    - Integration points
  modernization:
    - Update terminology
    - Convert to EARS format
    - Flag deprecated requirements

Output Format

Per-Document Output
extraction_result:
  source:
    file: "{path or URL}"
    type: "{document type}"
    extraction_date: "{ISO-8601}"
    confidence: high|medium|low

  statistics:
    total_candidates: {number}
    extracted: {number}
    filtered: {number}
    needs_review: {number}

  requirements:
    - id: REQ-{number}
      text: "{requirement}"
      type: functional|non-functional|constraint
      source_location: "{section/page}"
      confidence: high|medium|low
      original_text: "{exact source text}"

  review_items:
    - requirement_id: REQ-{number}
      reason: "{why review needed}"
      suggestion: "{proposed action}"

  metadata:
    sections_processed: {number}
    extraction_patterns_used: ["{pattern names}"]

Autonomy Levels

Guided Mode
guided_behavior:
  document_selection: Human selects
  extraction_strategy: AI suggests, human approves
  each_requirement: AI highlights, human confirms
  categorization: AI suggests, human validates
Semi-Autonomous Mode
semi_auto_behavior:
  document_selection: AI suggests priority, human approves list
  extraction_strategy: AI chooses autonomously
  requirements: AI extracts all, human reviews in batches
  categorization: AI categorizes, human spot-checks
Fully Autonomous Mode
full_auto_behavior:
  document_selection: AI processes all relevant
  extraction_strategy: AI optimizes per document
  requirements: AI extracts, deduplicates, categorizes
  output: Full extraction report for final review

Quality Indicators

High Confidence Extraction
  • Explicit requirement markers ("shall", "must")
  • EARS-pattern matches
  • Numbered requirement lists
  • Clear imperative statements
Medium Confidence Extraction
  • Implicit indicators ("should", "needs to")
  • Context-dependent interpretation
  • Partial pattern matches
  • Requires domain knowledge
Low Confidence Extraction
  • Inferred from descriptions
  • Narrative text interpretation
  • Competitive analysis
  • Assumptions based on context

Delegation

For related tasks, delegate to:

  • gap-analysis: Check extracted requirements for completeness
  • domain-research: Research unfamiliar terms or concepts
  • elicitation-methodology: Route back for technique selection

Output Location

Save extraction results to:

.requirements/{domain}/documents/DOC-{filename}-{timestamp}.yaml

Related

  • elicitation-methodology - Parent hub skill
  • gap-analysis - Post-extraction completeness checking
  • interview-conducting - Clarify extracted requirements with stakeholders

Comments

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