domain-research

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

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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

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Example Domains

This framework has been applied to:

Domain	Example Topic	Stakeholders
Manufacturing AI	AI adoption for SMEs	Engineers, Factory Managers
Healthcare AI	Staff scheduling optimization	Hospital Administrators
EdTech	AI tutoring implementation	University Faculty
FinTech	Blockchain for payments	Strategy Teams
Sustainability	Carbon neutrality pathways	Corporate Executives
HR Tech	AI in recruitment	HR Directors
AgTech	Precision agriculture	Farm Operators

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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"

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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

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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

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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

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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