question-refiner
将原始研究问题细化为结构化的深度研究任务。通过提问澄清需求，生成符合 OpenAI/Google Deep Research 标准的结构化提示词。当用户提出研究问题、需要帮助定义研究范围、或想要生成结构化研究提示词时使用此技能。
When & Why to Use This Skill
The Question Refiner skill is a specialized tool designed to transform vague or unstructured research queries into high-quality, actionable research prompts. By utilizing progressive questioning and automatic research type detection, it ensures that research tasks are well-defined, meet professional standards (like OpenAI/Google Deep Research), and are optimized for execution. It effectively eliminates ambiguity, ensuring that deep research agents produce precise, relevant, and comprehensive results.
Use Cases
- Case 1: Converting a broad query like 'AI market trends' into a structured market analysis prompt with specific sub-questions, timeframe constraints, and required source types.
- Case 2: Refining technical research requests into detailed 'Deep Dive' frameworks to investigate complex software architectures or emerging technologies with high specificity.
- Case 3: Standardizing research workflows within teams by ensuring all research prompts pass a quality validation score (≥8.0) before being sent to execution agents.
- Case 4: Clarifying ambiguous user requests through strategic, multi-round questioning to define the target audience, output format, and specific research objectives.
| name | question-refiner |
|---|---|
| description | Transform raw research questions into structured, validated research prompts with automatic research type detection and output format validation. Ensures prompts are ready for research-executor with comprehensive quality checks. |
Question Refiner
Overview
Transform vague research questions into structured, actionable research prompts through strategic clarifying questions with automatic research type detection and quality validation.
When to Use
- User provides a raw, unstructured research question
- Research scope is unclear or too broad
- Need validated structured prompt for research-executor
- Want to ensure prompt meets quality standards (≥8.0)
Core Approach
Progressive Questioning (2 rounds max):
- Round 1 (3 questions): Topic focus, output format, audience
- Round 2 (conditional): Scope, sources, special requirements
- Auto-detect research type → Select template → Generate & validate
Research Type Detection
| Type | Indicators | Example |
|---|---|---|
| Exploratory | "what is", "overview", "landscape" | "What is the AI market like?" |
| Comparative | "vs", "compare", "difference" | "Compare GPT-4 vs Claude" |
| Problem-Solving | "how to", "solve", "fix" | "How to improve API performance" |
| Forecasting | "future", "trend", "prediction" | "Future of quantum computing" |
| Deep Dive | "technical", "architecture" | "How does BERT work internally" |
| Market Analysis | "market", "industry", "competition" | "AI chip market analysis" |
Output Structure
### RESEARCH TYPE
[auto-detected type]
### TASK
[Clear, specific research objective]
### CONTEXT/BACKGROUND
[Why this matters, who will use it]
### SPECIFIC QUESTIONS
1-7 concrete sub-questions
### KEYWORDS
[Search terms ≥5]
### CONSTRAINTS
- Timeframe: [e.g., 2020-present]
- Geography: [e.g., global]
- Source types: [academic, industry, news]
### OUTPUT FORMAT
- Type: [comprehensive_report|executive_summary|comparison_table]
- Citation style: [inline-with-url|footnotes]
### QUALITY SCORE
[0-10, must be ≥8.0]
Quality Validation
| Component | Weight | Criteria |
|---|---|---|
| Completeness | 30% | All required fields present |
| Specificity | 30% | Questions are specific, not vague |
| Keyword Richness | 20% | ≥5 search terms with synonyms |
| Constraint Clarity | 20% | Clear, realistic constraints |
Process: Generate → Validate → If score < 8.0: Refine (max 2 attempts)
Token Optimization
📋 Reference:
.claude/shared/constants/token_optimization.md
Context Budget: 10k tokens max
Error Handling
📋 Reference:
.claude/shared/constants/error_codes.md
- E001: Insufficient context → Ask clarifying questions
- E003: Validation failed → Refine and retry
- E004: Quality < 8.0 after retries → Request manual review
See also: Skill Base Template
Examples
See examples.md for detailed interaction patterns.
Detailed Instructions
See instructions.md for complete questioning strategy.