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affinity-diagram
GitHub将定性研究数据（如访谈、观察）聚类为主题簇并生成洞察陈述。适用于多源数据综合，强调自下而上聚类及跨受访者均衡采样，防止代表性偏差。
Trigger Scenarios
需要整理和分析多个访谈记录或调研数据
从定性数据中提取主题模式和用户洞察
Install
npx skills add Owl-Listener/designer-skills --skill affinity-diagram -g -y
SKILL.md
Frontmatter
{
"name": "affinity-diagram",
"description": "Cluster many qualitative data points into themes and insight statements. Use when synthesising across multiple sessions or sources. For a single transcript use `summarize-interview`; for one segment's inner state use `empathy-map`."
}
Affinity Diagram
Organize qualitative research data into themed clusters and insight statements.
Context
You are a UX researcher synthesizing qualitative data for $ARGUMENTS. If the user provides files (interview notes, observation data, survey responses), read them first.
Instructions
- Extract data points: Pull individual observations, quotes, and notes from the raw data.
- Bottom-up clustering: Group related data points into natural clusters (do not start with predefined categories).
- Name each cluster: Create descriptive theme labels that capture the essence of each group.
- Create hierarchy: Organize clusters into higher-level themes (typically 3-5 top-level themes).
- Write insight statements: For each theme, write a clear insight statement that captures the "so what?"
- Identify patterns: Note frequency, intensity, and connections between themes.
- Prioritize: Rank insights by impact on design decisions.
- Present the affinity diagram as a structured hierarchy with insight statements and supporting evidence.
Cross-Interview Sampling Principle
Index evenly across all participants. When working from multiple interview transcripts, process each one fully before clustering. Do not over-represent early transcripts or the most recent input.
- Treat each participant as an equal source of signal
- Tag every observation with its participant ID (P1, P2, P3...) before grouping
- After clustering, check that each participant appears at least once in the output — if any are absent, go back
- Patterns that appear in only one interview should be flagged as single-source, not discarded
This prevents the common LLM failure mode of building themes from the first one or two transcripts and fitting the rest retroactively.
Version History
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20e34c4
Current 2026-08-19 23:19
优化技能描述，明确使用场景与边界，解决与其他技能的重叠问题，确保描述包含触发条件且不超过400字符。
- acc3e57 2026-07-25 05:14
