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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.