AgentSkillsCN

synthesize-research

将原始采访笔记转化为带有模式与引语的结构化见解

SKILL.md
--- frontmatter
name: synthesize-research
description: Turn raw interview notes into structured insights with patterns and quotes
argument-hint: "[path-to-research-folder]"
user-invocable: true

Research Synthesizer

Synthesize findings across multiple research files into actionable insights.

Instructions

  1. Locate Research Files

    • Search for interview notes, research summaries related to the topic
    • Include: interview-*.md, research-summary-*.md
    • Check _temp/ for quick notes that might be relevant
  2. Extract Key Data From each file, pull:

    • Key findings and confidence levels
    • Pain points with severity ratings
    • Verbatim quotes (especially emotional or insightful ones)
    • Recommendations made
  3. Identify Patterns

    • Group similar findings across sources
    • Count frequency of themes
    • Note contradictions or outliers
    • Assess overall confidence based on consistency
  4. Generate Synthesis

    Output format:

    code
    ## Research Synthesis: {{topic}}
    
    **Sources:** {{N}} interviews, {{N}} surveys, etc.
    **Date Range:** {{earliest}} to {{latest}}
    
    ### Top Findings
    
    #### 1. {{Finding}}
    - **Frequency:** Mentioned by X/Y participants
    - **Confidence:** High/Medium/Low
    - **Evidence:** {{summary}}
    - **Key Quote:** "{{quote}}" — P{{X}}
    
    ### Patterns & Themes
    
    | Theme | Frequency | Segments Most Affected |
    |-------|-----------|------------------------|
    | | | |
    
    ### Contradictions / Nuances
    - {{where findings differed and why}}
    
    ### Recommendations
    1. **Do Now:** {{high confidence, high impact}}
    2. **Explore:** {{needs more research}}
    3. **Avoid:** {{anti-patterns identified}}
    
    ### Gaps / Open Questions
    - {{what we still don't know}}
    
  5. Link to Sources

    • Include references to all source documents
    • Make findings traceable

Notes

  • Weight findings by recency if research spans long periods
  • Call out when sample size is too small for confidence
  • Suggest follow-up research for low-confidence areas