Affinity DiagramSAFE

Designer Skills Collection: agentic skills, commands, and plugins for design — from research to systems, UI, interaction, and delivery.

The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
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.

SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.

What the source does

Filesystem

none-observed

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none-observed

Shell

none-observed

Dependencies

pinned

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none-found

Findings (0)

No findings outside the package's declared scope.

Gates applied: no_behavioural_pass.

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