Affinity diagram
Skill Infrasity-Labs/dev-gtm-claude-skills/.claude/skills/affinity-diagram
Organize qualitative research data into an affinity diagram with themes, clusters, and insight statements. Use when synthesizing large amounts of qualitative data from interviews, observations, or surveys.From its SKILL.md
npx -y skills add Infrasity-Labs/dev-gtm-claude-skills --skill affinity-diagramAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
SKILL.md
2.0 KB, 355 tokens by cl100k_base, as published. Nobody here has run it
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.
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.
What ships with it
Read from the repository
Just SKILL.md. No reference files, no scripts.
Gives 0 of the 12 instructions most diagrams charts skills give in 355 tokens
Counted across 397 of the 418 authors here whose files we hold, read 2026-09-06
- Quote labels containing special charactersin 18 of 397, across 17 files
- Choose the right diagram type for the datain 15 of 397, across 13 files
- Create a detailed Mermaid diagramin 12 of 397, across 11 files
- Validate Mermaid syntax before outputin 11 of 397, across 9 files
- Wrap diagrams in fenced Mermaid code blocksin 11 of 397
- Read the reference file for the chosen diagram typein 10 of 397, across 6 files
- Label every connection descriptivelyin 8 of 397, across 7 files
- Use stateDiagram-v2 for state diagramsin 7 of 397
- Declare the diagram type on the first linein 7 of 397
- Keep one concept per diagramin 7 of 397
- Cap diagrams at 15 nodes and split over 20in 7 of 397
- Describe the diagram before generating itin 7 of 397
Said here and by no other author read
- Read provided files before synthesizing
- Extract individual observations, quotes, and notes from raw data
- Group related data points into natural clusters
- Name each cluster with a descriptive theme label
- Organize clusters into three to five top-level themes
- Write an insight statement for each theme
Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.