Domain Research Skill

MCP-powered domain research for enriching requirements elicitation with external knowledge.

MANDATORY: Documentation-First Approach

Before conducting domain research:

1. Invoke docs-management skill for requirements elicitation patterns
2. Use MCP servers as primary research tools (perplexity, context7, firecrawl)
3. Base all guidance on official documentation and authoritative sources

When to Use This Skill

Keywords: domain research, MCP research, industry standards, best practices, competitive analysis, technology research, regulatory requirements

Invoke this skill when:

• Unfamiliar with a domain and need background
• Researching industry standards and best practices
• Investigating regulatory requirements
• Analyzing competitor features
• Exploring technology constraints
• Supplementing stakeholder knowledge

Available MCP Servers

Perplexity (General Research)

Use for:

• Industry best practices
• Recent developments
• Comparative analysis
• Regulatory overviews

mcp_tool: mcp__perplexity__search
example_queries:
  - "e-commerce checkout best practices 2025"
  - "GDPR compliance requirements for SaaS"
  - "authentication patterns for financial applications"

Context7 (Library Documentation)

Use for:

• Framework requirements
• API constraints
• Library capabilities
• Technical limitations

mcp_tools:
  - mcp__context7__resolve-library-id
  - mcp__context7__query-docs
example_queries:
  - Library: "react" → Query: "state management patterns"
  - Library: "fastapi" → Query: "authentication requirements"

Firecrawl (Web Scraping)

Use for:

• Competitor analysis
• Documentation extraction
• Feature comparison
• Market research

mcp_tools:
  - mcp__firecrawl__firecrawl_search
  - mcp__firecrawl__firecrawl_scrape
example_queries:
  - Search: "inventory management software features"
  - Scrape: Competitor feature pages

Research Patterns

Pattern 1: Domain Background

Build foundational domain knowledge:

research_pattern: domain_background
steps:
  1. Use perplexity for industry overview
  2. Identify key concepts and terminology
  3. Research common requirements in domain
  4. Note regulatory considerations
output: Domain context document

Pattern 2: Best Practices

Research current best practices:

research_pattern: best_practices
steps:
  1. Search for "best practices" in domain
  2. Filter for recent (last 2 years)
  3. Identify common patterns
  4. Note recommended approaches
output: Best practices summary

Pattern 3: Competitive Analysis

Research competitor features:

research_pattern: competitive_analysis
steps:
  1. Identify key competitors
  2. Scrape feature pages with firecrawl
  3. Extract capability lists
  4. Compare and contrast
output: Competitive feature matrix

Pattern 4: Regulatory Research

Research compliance requirements:

research_pattern: regulatory
steps:
  1. Identify applicable regulations
  2. Research specific requirements
  3. Note mandatory vs recommended
  4. Document compliance criteria
output: Regulatory requirements list

Pattern 5: Technology Constraints

Research technical requirements:

research_pattern: technology
steps:
  1. Identify technologies in scope
  2. Use context7 for library docs
  3. Research integration requirements
  4. Document technical constraints
output: Technical requirements document

Research Workflow

Step 1: Define Research Scope

research_scope:
  domain: "{domain name}"
  topic: "{specific focus area}"
  depth: shallow|moderate|deep
  sources: [perplexity, context7, firecrawl]

Step 2: Execute Research Queries

For each research need:

1. Select appropriate MCP server
2. Formulate effective query
3. Process results
4. Extract requirements

Step 3: Synthesize Findings

Combine research into actionable requirements:

• Identify common patterns
• Note conflicts or options
• Highlight mandatory items
• Suggest priorities

Step 4: Document Results

Save research findings and derived requirements.

Output Format

Research Results

research_session:
  id: "RES-{timestamp}"
  domain: "{domain}"
  topic: "{research topic}"
  timestamp: "{ISO-8601}"

  queries_executed:
    - server: perplexity
      query: "{query text}"
      results_count: {number}

    - server: firecrawl
      url: "{scraped URL}"
      content_type: feature_page

  findings:
    domain_context:
      - "{key finding 1}"
      - "{key finding 2}"

    best_practices:
      - "{recommended practice 1}"
      - "{recommended practice 2}"

    regulatory:
      - regulation: "GDPR"
        requirements:
          - "{requirement 1}"
          - "{requirement 2}"

    competitive:
      - competitor: "{name}"
        features:
          - "{feature 1}"
          - "{feature 2}"

  derived_requirements:
    - id: REQ-RES-001
      text: "{requirement statement}"
      source: research
      source_detail: "{where this came from}"
      confidence: low  # Research-derived = low confidence
      needs_validation: true
      category: "{category}"

  recommendations:
    - topic: "{topic}"
      finding: "{what research showed}"
      implication: "{what this means for requirements}"

  gaps_in_research:
    - "{area where more research needed}"

Query Optimization

Effective Perplexity Queries

query_patterns:
  best_practices:
    template: "{domain} {topic} best practices {year}"
    example: "e-commerce checkout best practices 2025"

  requirements:
    template: "{domain} {topic} requirements specifications"
    example: "healthcare application HIPAA requirements"

  comparison:
    template: "{topic A} vs {topic B} for {use case}"
    example: "OAuth 2.0 vs SAML for enterprise SSO"

  regulatory:
    template: "{regulation} requirements for {industry}"
    example: "PCI-DSS requirements for payment processing"

Effective Context7 Queries

query_patterns:
  library_features:
    resolve: "{library name}"
    get_docs: topic="{specific feature}"

  integration:
    resolve: "{library name}"
    get_docs: topic="integration authentication"

Effective Firecrawl Queries

query_patterns:
  competitor_features:
    search: "{competitor} features {product type}"
    scrape: Feature page URLs

  documentation:
    search: "{technology} documentation requirements"
    scrape: Official docs

Confidence Levels

Research-derived requirements have inherent confidence limits:

confidence_levels:
  high:
    sources: [official documentation, regulatory text]
    note: "Verified from authoritative source"

  medium:
    sources: [industry articles, best practice guides]
    note: "Generally accepted but verify with stakeholders"

  low:
    sources: [competitor analysis, general web]
    note: "Use as starting point, requires validation"

Delegation

For follow-up actions:

• interview-conducting: Validate research with stakeholders
• gap-analysis: Check research fills identified gaps
• elicitation-methodology: Return for technique selection

Output Location

Save research results to:

.requirements/{domain}/research/RES-{timestamp}.yaml

Related

• elicitation-methodology - Parent hub skill
• gap-analysis - Research to fill gaps
• interview-conducting - Validate research findings

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Last Updated: 2025-12-29