design-critique
Facilitate a structured team critique — framing, feedback rules, and actionable outcomes. Use…
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`.
$ npx -y skills add Owl-Listener/designer-skills --skill affinity-diagram --agent claude-codeHow it fires
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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`.
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`.
Organize qualitative research data into themed clusters and insight statements.
You are a UX researcher synthesizing qualitative data for $ARGUMENTS. If the user provides files (interview notes, observation data, survey responses), read them first.
1. **Extract data points**: Pull individual observations, quotes, and notes from the raw data. 2. **Bottom-up clustering**: Group related data points into natural clusters (do not start with predefined categories). 3. **Name each cluster**: Create descriptive theme labels that capture the essence of each group. 4. **Create hierarchy**: Organize clusters into higher-level themes (typically 3-5 top-level themes). 5. **Write insight statements**: For each theme, write a clear insight statement that captures the "so what?" 6. **Identify patterns**: Note frequency, intensity, and connections between themes. 7. **Prioritize**: Rank insights by impact on design decisions. 8. Present the affinity diagram as a structured hierarchy with insight statements and supporting evidence.
**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.
This prevents the common LLM failure mode of building themes from the first one or two transcripts and fitting the rest retroactively.
Design skills for the agent era, written so an AI agent can actually use them. 273 skills and 76 commands across 33 plugins, in five collections, for Claude Code and Gemini CLI. Not sure which skill you need?
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