Affinity Diagram
- 939 installs
- 1.9k repo stars
- Updated June 14, 2026
- owl-listener/designer-skills
affinity-diagram is a UX research agent skill that clusters interview notes, observations, and survey responses into themed groups and prioritized insight statements for developers synthesizing qualitative data.
About
affinity-diagram is a UX research agent skill from owl-listener/designer-skills that organizes qualitative research into affinity diagrams with themes, clusters, and insight statements. The skill instructs agents to read user-provided interview notes, observation logs, or survey responses, extract individual quotes and observations, then perform bottom-up clustering into related groups. Developers reach for affinity-diagram when synthesizing large volumes of qualitative data after user interviews, field observations, or open-ended survey feedback during discovery. The workflow produces themed clusters and concise insight statements prioritizing what the evidence shows, helping teams move from raw research files to structured findings before wireframing or roadmap decisions. Agents act as UX researchers for supplied arguments and source files, making the skill valuable when coding agents must process research artifacts into design-ready synthesis documents rather than code changes alone.
- Extracts individual data points, quotes, and observations from raw qualitative files
- Performs bottom-up clustering without preconceived categories
- Builds 3-5 top-level themes with descriptive labels and supporting evidence
- Produces ranked insight statements with 'so what?' implications for design decisions
- Identifies frequency, intensity, and cross-theme connections
Affinity Diagram by the numbers
- 939 all-time installs (skills.sh)
- +41 installs in the week ending Jul 29, 2026 (Skillselion tracking)
- Ranked #506 of 3,282 Productivity & Planning skills by installs in the Skillselion catalog
- Security screen: LOW risk (skills.sh audit)
- Data as of Jul 31, 2026 (Skillselion catalog sync)
npx skills add https://github.com/owl-listener/designer-skills --skill affinity-diagramAdd your badge
Show developers this skill is listed on Skillselion. Paste this into your README.
| Installs | 939 |
|---|---|
| repo stars | ★ 1.9k |
| Security audit | 3 / 3 scanners passed |
| Last updated | June 14, 2026 |
| Repository | owl-listener/designer-skills ↗ |
How do you synthesize qualitative UX research into themes?
Turn raw interview notes, user observations, and survey responses into structured themes, clusters, and prioritized insight statements.
Who is it for?
Developers and designers with piles of interview notes or survey text who need structured theme clusters before product decisions.
Skip if: Teams needing quantitative analytics dashboards or automated statistical analysis instead of qualitative synthesis.
When should I use this skill?
User provides interview notes, observations, or survey responses to cluster into affinity diagram themes
What you get
Themed affinity clusters, grouped data points, and prioritized UX insight statements from raw research
- Affinity theme clusters
- Prioritized insight statements
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
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.
Related skills
Grill MeHave an agent relentlessly pressure-test their plan or design until every decision branch is resolved and shared understanding is reached.645k185kGrill With DocsPressure-test architecture decisions against their live codebase and living documentation before writing production code.547k185k
Lark CalendarLet their coding agents read, create, and manage events inside Lark (Feishu) Calendar directly from natural language instructions.466kLark ContactLet their AI coding agents read and write contacts, send messages, and manage user data inside Lark (Feishu).466kLark ImLet their coding agent send instant notifications, post updates, and receive commands directly inside Lark (Feishu) workspaces.466kLark AttendanceAutomatically fetch and report daily attendance data from Lark (Feishu) into their own tools or agents.465kHow it compares
Use affinity-diagram for qualitative clustering; use analytics skills when the source data is metrics rather than interviews or open text.
FAQ
What inputs does affinity-diagram accept?
affinity-diagram reads interview notes, observation data, and survey responses provided as files or pasted content, then extracts individual quotes and observations before clustering them into affinity diagram themes.
What does affinity-diagram output?
affinity-diagram delivers themed clusters of related data points and prioritized insight statements summarizing patterns found in qualitative research, giving teams structured findings for downstream UX and product decisions.
Is Affinity Diagram safe to install?
skills.sh reports 3 of 3 security scanners passed. Review the Security Audits panel on this page before installing in production.