Extract requirements from existing documents including PDFs, Word docs, meeting transcripts, specifications, and web content. Identifies requirement candidates, categorizes them, and outputs in pre-canonical format.
Extract requirements from existing documents including PDFs, Word docs, meeting transcripts, specifications, and web content. Identifies requirement candidates, categorizes them, and outputs in pre-canonical format.
/plugin marketplace add melodic-software/claude-code-plugins/plugin install requirements-elicitation@melodic-softwareThis skill is limited to using the following tools:
references/document-types.mdreferences/extraction-prompts.mdExtract requirements from existing documentation sources for systematic requirement mining.
Keywords: extract requirements, document mining, PDF requirements, transcript analysis, parse document, existing documentation, legacy requirements, competitive analysis
Invoke this skill when:
| Type | Extension | Extraction Method |
|---|---|---|
| Markdown | .md | Direct Read |
| Text | .txt | Direct Read |
| Read tool (PDF support) | ||
| Word | .docx | Read tool |
| Web Page | URL | WebFetch tool |
| Meeting Notes | .md, .txt | Transcript patterns |
| Specification | .md, .docx | Requirement patterns |
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
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..."
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}"
Categorize extracted requirements:
categories:
functional:
- features
- behaviors
- interactions
non_functional:
- performance
- security
- usability
- reliability
- scalability
constraints:
- technical
- business
- regulatory
assumptions:
- environmental
- user_behavior
- dependencies
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}"
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_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_extraction:
focus_on:
- Feature descriptions
- User capabilities
- Unique selling points
output:
- Feature requirements
- Differentiation opportunities
- Gap identification
confidence: low # Based on external observation
legacy_extraction:
focus_on:
- Existing requirements
- System behaviors
- Integration points
modernization:
- Update terminology
- Convert to EARS format
- Flag deprecated requirements
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}"]
guided_behavior:
document_selection: Human selects
extraction_strategy: AI suggests, human approves
each_requirement: AI highlights, human confirms
categorization: AI suggests, human validates
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
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
For related tasks, delegate to:
Save extraction results to:
.requirements/{domain}/documents/DOC-{filename}-{timestamp}.yaml
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