---
name: Affinity Diagram
slug: affinity-diagram
category: Data
description: Affinity Diagram clusters qualitative data points into themes and insight statements. Use it when synthesising across multiple sessions or sources to create a structured hierarchy with supporting evidence.
github: "https://github.com/Owl-Listener/designer-skills/tree/main/design-research/skills/affinity-diagram"
language: Markdown
stars: 2098
forks: 335
install: "npx degit https://github.com/Owl-Listener/designer-skills/tree/main/design-research/skills/affinity-diagram ~/.claude/skills/affinity-diagram"
installs_to: ~/.claude/skills/affinity-diagram
source_path: design-research/skills/affinity-diagram/SKILL.md
collection_size: 25
category_size: 668
collection_url: "https://dirskills.com/collections/Owl-Listener/designer-skills"
added: 2026-08-18T06:58:40.510Z
last_synced: 2026-08-18T06:58:40.510Z
canonical_url: "https://dirskills.com/skills/affinity-diagram"
---

# Affinity Diagram

Affinity Diagram clusters qualitative data points into themes and insight statements. Use it when synthesising across multiple sessions or sources to create a structured hierarchy with supporting evidence.

**Install:**

```bash
npx degit https://github.com/Owl-Listener/designer-skills/tree/main/design-research/skills/affinity-diagram ~/.claude/skills/affinity-diagram
```

## README

# 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.