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/affinity-diagram

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`.

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designer-skills
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$ npx -y skills add Owl-Listener/designer-skills --skill affinity-diagram --agent claude-code

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  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/affinity-diagram

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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`.

SKILL.md

affinity-diagram.SKILL.md
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

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.

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.

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