deep-research-query

name	deep-research-query
description	This skill should be used when a user wants to build a structured research query interactively before starting deep research. Example queries include "/deep-research query", "리서치 쿼리 만들어줘", "research query builder", "structured research query", "쿼리 빌더".

Deep Research Query Builder

Transform vague research ideas into structured, actionable research queries.

Trigger Conditions

# Primary triggers
- "/deep-research query"
- "리서치 쿼리 만들어줘"
- "research query builder"
- "structured research query"
- "쿼리 빌더"

---

WHEN TRIGGERED - EXECUTE IMMEDIATELY

Phase 1: Discovery (REQUIRED)

Use AskUserQuestion to gather core requirements. Detect user language and translate all labels.

{
  "questions": [
    {
      "question": "What topic do you want to research?",
      "header": "Topic",
      "options": [
        {"label": "Type your topic", "description": "Enter a specific research topic or question"},
        {"label": "Browse examples", "description": "See example queries for inspiration"}
      ],
      "multiSelect": false
    },
    {
      "question": "What type of research is this?",
      "header": "Type",
      "options": [
        {"label": "Exploratory", "description": "Discover what exists, map the landscape"},
        {"label": "Comparative", "description": "Compare technologies, approaches, or products"},
        {"label": "Analytical", "description": "Deep analysis of causes, effects, and mechanisms"},
        {"label": "Predictive", "description": "Future trends, forecasts, and projections"}
      ],
      "multiSelect": false
    }
  ]
}

If user selects "Browse examples", load and present examples from: ${CLAUDE_PLUGIN_ROOT}/skills/deep-research-main/examples/

Phase 2: Detailed Scoping

After getting the core topic, gather detailed requirements:

{
  "questions": [
    {
      "question": "What geographic scope?",
      "header": "Geography",
      "options": [
        {"label": "Global", "description": "Worldwide perspective"},
        {"label": "US/North America", "description": "Focus on United States and North America"},
        {"label": "Asia-Pacific", "description": "Focus on APAC region"},
        {"label": "Europe", "description": "Focus on European markets"}
      ]
       
    
    
       
       
       
           
           
           
           
      
       
    
  

Phase 3: Query Generation

After gathering all inputs, generate:

1. Structured JSON Query following the schema at: ${CLAUDE_PLUGIN_ROOT}/skills/deep-research-query/references/query_schema.json

2. Human-Readable Research Brief in markdown format

3. Execution Checklist for quality verification

Output Format

JSON Query Structure

{
  "task": {
    "title": "[Concise 5-15 word title]",
    "objective": "[Clear statement of research goal]",
    "type": "exploratory|comparative|analytical|predictive|evaluative"
  },
  "context": {
    "background": "[Why this research matters]",
    "audience": "technical|executive|academic|general|policy_maker",
    "use_case": "[How the research will be used]",
    "prior_knowledge": ["assumption 1", "assumption 2"]
  },
  "questions": {
    "primary": "[Main research question]",
    "secondary": ["Sub-question 1", "Sub-question 2" 
     
     
  
   
          
          
     
        
       
       
    
  
   
     
        
     
       
       
       
    
     
     
  
    
   

Human-Readable Brief

# Research Brief: [Title]

## Objective
[Clear statement]

## Research Questions
### Primary Question
> [Main question]

### Secondary Questions
1. [Sub-question 1]
2. [Sub-question 2]

## Scope & Constraints
| Dimension | Specification |
|-----------|--------------|
| Timeframe | [period] |
| Geography | [scope] |
| Min Quality | Grade [X] |

## Execution Checklist
- [ ] Primary question fully answered
- [ ] All secondary questions addressed
- [ ] Sources meet quality threshold
- [ ] Citations properly formatted

Phase 4: Confirmation and Handoff

Present the generated query to the user and ask:

{
  "questions": [
    {
      "question": "Query looks good? Ready to start research?",
      "header": "Action",
      "options": [
        {"label": "Start research now", "description": "Launch deep research with this query immediately"},
        {"label": "Save query only", "description": "Save the JSON query for later use"},
        {"label": "Adjust query", "description": "Modify some parameters before starting"}
      ],
      "multiSelect": false
    }
  ]
}

• Start research now -> Pass the JSON query to deep-research-main skill
• Save query only -> Write the JSON to a file for the user
• Adjust query -> Loop back to gather adjustments

---

Quality Validation Rules

Before finalizing the query, verify:

Task Validation

• Title is specific (not generic like "AI Research")
• Objective is measurable/verifiable
• Type matches the research approach

Questions Validation

• Primary question is answerable (not too broad)
• Secondary questions support primary (not tangential)
• Exclusions prevent scope creep

Constraints Validation

• Timeframe is realistic for the topic
• Geography matches topic relevance
• Source requirements are achievable

Output Validation

• Length matches depth requested
• Format suits the audience

---

Anti-Patterns to Avoid

DO NOT Generate:

• Overly broad questions ("What is AI?")
• Unbounded timeframes ("all history")
• Conflicting constraints
• Generic keywords ("technology", "innovation")
• Unmeasurable objectives ("understand everything about...")

DO Generate:

• Specific, answerable questions ("What is the current adoption rate of AI diagnostic tools in US hospitals?")
• Realistic scope boundaries (2-3 year timeframe for fast-moving fields)
• Concrete success criteria ("Identify top 10 tools by market share")
• Actionable search terms ("AI radiology FDA approved 2024 2025 adoption rate")
• Clear exclusions ("Exclude consumer health apps and administrative AI")

---

Example Transformation

Input (Vague)

"I want to know about AI in healthcare"

Discovery Process

After Phase 1-2 questions, the vague input transforms into:

Dimension	Vague	Structured
Title	"AI in healthcare"	"AI Diagnostic Systems in Clinical Healthcare: Adoption and Impact 2023-2026"
Scope	Everything	US hospitals, diagnostic AI only, 2023-present
Exclusions	None	Consumer apps, billing AI, drug discovery
Sources	Any	FDA databases, PubMed, Gartner reports
Metrics	None	Adoption rate %, sensitivity/specificity, ROI timeline

Generated Keywords

From the vague "AI healthcare", generate specific search terms:

"AI diagnostics FDA approved 2025"
"clinical AI adoption rate hospital"
"radiology AI sensitivity specificity study"
"healthcare AI ROI implementation cost"
"medical AI regulatory compliance HIPAA"

---

Language Adaptation

All AskUserQuestion labels and descriptions adapt to the user's detected language.

Korean Input Handling

When user inputs Korean (e.g., "헬스케어 AI 리서치 쿼리 만들어줘"):

• All question labels in Korean
• Geographic options include Korea-relevant choices
• Source options include Korean research databases
• Output includes Korean citation conventions

Multi-language Keywords

Generate search keywords in both the user's language and English for maximum coverage:

Korean input: "AI 의료 진단"
Generated: ["AI 의료 진단 2026", "AI medical diagnostics 2026", "의료 AI 도입 현황", "clinical AI adoption"]

---

Integration with Deep Research

The generated query feeds directly into the deep-research-main skill:

1. Query builder outputs structured JSON
2. User confirms or adjusts
3. If "Start research now" selected, the JSON is passed to deep-research-main
4. Phase 1 (Question Scoping) is skipped since requirements are already defined
5. Research begins from Phase 2 (Retrieval Planning)

Save location for queries: RESEARCH/queries/{topic}_{timestamp}.json

---

References

• Query schema: ${CLAUDE_PLUGIN_ROOT}/skills/deep-research-query/references/query_schema.json
• Example queries: ${CLAUDE_PLUGIN_ROOT}/skills/deep-research-main/examples/