Affinity Diagram

Takes your messy pile of interview transcripts, survey responses, or research notes and organizes them into a proper affinity diagram with themed clusters and insight statements. Works bottom-up, grouping related observations naturally rather than forcing predefined categories, then builds a hierarchy of 3-5 top-level themes with "so what?" statements for each. The real value is in how it structures the output: you get frequency patterns, connections between themes, and prioritized insights ranked by design impact. Saves you from drowning in sticky notes or spending hours manually clustering qualitative data. Best for UX researchers who need to turn raw research into actionable findings quickly.

skills.sh installs: 2.3K
GitHub stars: 3.3K
last updated: Aug 21, 2026

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Install

$ npx -y skills add owl-listener/designer-skills --skill affinity-diagram --agent claude-code

Installs into .claude/skills of the current project.

Files

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.