domain-research

name: domain-research description: Use when conducting systematic research in any domain (AI, healthcare, manufacturing, etc.), transforming vague interests into structured research through conversational discovery, or when users need evidence-based insights from broad exploration to actionable plans

Universal Research Framework

Core Purpose

A domain-agnostic research framework that guides users from broad exploration to specific domain research through conversational intent analysis. Works for any field:

• Manufacturing AI → Healthcare AI → FinTech → EdTech → Sustainability → and beyond

What It Does

1. Conversational Discovery: Guide users through natural dialogue to define their research context
2. Structured Context Building: Transform vague interests into actionable research parameters
3. Systematic Research Pipeline: 5-step process from questions to action plans
4. Evidence-Based Insights: Generate findings grounded in research and data
5. Practical Application: Convert insights into executable roadmaps

Target Audience

This framework serves domain practitioners across any field:

• Industry Professionals: Engineers, managers, analysts seeking evidence-based guidance
• Academic Researchers: Faculty, students bridging theory and practice
• Business Leaders: Decision-makers needing structured research for strategy
• Consultants: Professionals providing research-backed recommendations
• Policy Makers: Those needing comprehensive domain understanding

Research Pipeline

Step 0: Conversational Intent Analysis

Prompt: prompts/intent-analyzer.md Purpose: Guide users from vague interests to structured research context through dialogue Process:

• Open invitation → Context deepening → Synthesis → Confirmation
• Adaptive questioning based on user type (clear/vague/assigned/exploratory) Output: Structured YAML research context

Step 1: Key Question Generation

Prompt: prompts/key-questions.md Purpose: Generate 5 testable, meaningful research questions Input: Research context from Step 0 Output: Prioritized questions with importance, impact, and methodology

Step 2: Research Gap Identification

Prompt: prompts/research-gaps.md Purpose: Identify underexplored areas and limitations in existing research Input: Key questions from Step 1 Output: 4 priority gaps with proposed research ideas

Step 3: Key Insight Extraction (Single Source)

Prompt: prompts/insight-extraction.md Purpose: Deep analysis of individual research sources Input: Papers, reports, or documents relevant to the research context Output: Structured findings, implications, limitations

Step 4: Multi-Source Synthesis

Prompt: prompts/multi-source-synthesis.md Purpose: Integrate insights across multiple sources Input: Multiple sources + insights from Steps 1-3 Output: Common themes, integrated findings, knowledge gaps

Step 5: Practical Application

Prompt: prompts/practical-application.md Purpose: Transform insights into executable action plans Input: All previous step outputs Output: Actions, challenges, KPIs, comprehensive roadmap

Final: Comprehensive Guide

Prompt: prompts/comprehensive-guide.md Purpose: Single-page practitioner roadmap Output: Scannable guide with findings, timeline, metrics, risks

Example Domains

This framework has been applied to:

Governing Principles

principles:
  field_agnostic:
    description: "Works for any domain the user brings"
    enforcement: "Dynamic context building, not fixed templates"

  conversational_discovery:
    description: "Natural dialogue over form-filling"
    enforcement: "Adaptive questions based on user clarity level"

  evidence_based:
    description: "All claims require verifiable evidence"
    enforcement: "Citations, data references, empirical validation"

  practitioner_focus:
    description: "Prioritize real-world applicability"
    enforcement: "Each insight maps to concrete action"

  iterative_refinement:
    description: "Context evolves as research progresses"
    enforcement: "Each step builds on previous outputs"

  minimum_viable_context:
    description: "Don't over-question; proceed when sufficient"
    enforcement: "3-6 exchanges typically sufficient for context"

MCP Server Integration

WebSearch Server

• Trigger: Step 1 trend analysis, Step 2 gap validation
• Purpose: Real-time paper and industry trend discovery
• Query Pattern: Dynamic based on research context

Sequential Server

• Trigger: Step 4 synthesis, Step 5 action planning
• Purpose: Complex multi-step reasoning and analysis
• Use Case: Cross-source synthesis, conflict resolution

Quality Gates

1. Context Completeness: All critical fields populated through conversation
2. Question Testability: Each question can be empirically investigated
3. Evidence Strength: Findings ranked by quality and reliability
4. Practical Mapping: Every insight connects to practitioner action
5. Roadmap Clarity: Final guide provides clear implementation path

Usage

# Start research session
claude --skill domain-research

# Begin with conversational discovery
/research

# Or provide initial context
/research "I want to explore AI in healthcare"

# Execute specific steps
/research-questions    # Step 1
/research-gaps         # Step 2
/research-synthesize   # Step 4
/research-action       # Step 5

Conversation Examples

Quick Path (Clear Intent)

User: "I want to research AI quality inspection for manufacturing SMEs"
→ 2-3 clarifying questions → Research context generated

Exploratory Path (Vague Intent)

User: "I'm curious about AI in my industry"
→ 4-6 discovery questions → Narrowed focus → Research context generated

Assigned Path (External Mandate)

User: "My boss wants a report on blockchain"
→ Context questions → Stakeholder alignment → Research context generated