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Affinity Diagram · owl-listener | DecimalAI

researchby owl-listener

Affinity Diagram

owl-listener/affinity-diagram

Source owl-listener/designer-skills ↗License MIT

Organize qualitative research data into an affinity diagram with themes, clusters, and insight statements.

SkillScore100Maintained

Automatic

Design & UX Review pack

Last verified 8/12/2026

What it changes

Passed 22 of 22 test cases with the skill, and 21 of 22 without it.

Without skill95%

With skill100%

Checks it passed that the model alone missed

Groups notes into a bottom-up theme based on navigation or feature discoverability rather than using the predefined taxonomy categories

1 fixed21 passed either way0 still failing0 broke

Pass-rate lift: +5 pp
Tokens: +24%
Agent turns: 0%
Response time: +2%

Model gemini-3.6-flashMeasured 8/12/2026v122 of 22 cases passed with skill

Tokens, turns and time describe resource use, not a dollar price.

Use this skill

Save it into your project's skills folder. Claude Code picks it up in its next session.

curl -fsSL --create-dirs -o .claude/skills/owl-listener-affinity-diagram/SKILL.md https://app.decimal.ai/s/owl-listener-affinity-diagram/SKILL.md

Want it pinned and updatable? pip install "decimalai>=0.15", then decimalai skills add owl-listener-affinity-diagram, and start Claude Code with claude --plugin-dir "$(git rev-parse --show-toplevel)/.agents"

1 · One-time — install the CLI (docs ↗)

pip install "decimalai>=0.15"

2 · Get the skill — writes .agents/skills/owl-listener-affinity-diagram/ and pins it in .decimal/skills.lock

decimalai skills add owl-listener-affinity-diagram

No CLI? Get the same file — curl · raw file

curl -fsSL --create-dirs -o .claude/skills/owl-listener-affinity-diagram/SKILL.md https://app.decimal.ai/s/owl-listener-affinity-diagram/SKILL.md

Raw file: /s/owl-listener-affinity-diagram/SKILL.md ↗ · machine-readable summary at /s/owl-listener-affinity-diagram.json ↗

Safety checks: 3 of 3 passed

Code scan CleanIntent PassedContent Clean

See it in action

A test case the skill fixed. Same model, same prompt, with and without the skill.

Prompt

I have raw interview notes from 3 user sessions about our mobile banking app. I usually organize feedback into predefined categories like 'Usability', 'Performance', and 'Visual Design'. Here are the notes: P1: 'The transfer button is hidden under three menus.' P2: 'I spent five minutes looking for my monthly statement.' P3: 'I can never find where my account details live.' Synthesize these notes into themed clusters.

What was checked, without and with the skill
Checked	Without skill	With skill
Groups notes into a bottom-up theme based on navigation or feature discoverability rather than using the predefined taxonomy categories	Missed	Passed
Includes a descriptive theme label reflecting user difficulty finding core account features	Passed	Passed

case-01 · gemini-3.6-flash · 8/12/2026 · answers are capped and redacted

What's inside

The instructions your model reads when the skill loads.

SKILL.md29 linessingle file

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.

Track record

Benchmark

22 / 22

test cases passed, vs 21 / 22 without

Adoption

Users: 0
Times used: 0

Safety and source details

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 8/3/2026

View 15 scanner checks

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

owl-listener / designer-skills

design-research/skills/affinity-diagram/SKILL.md

View on GitHub ↗

License MITImported 8/10/2026Version v1

More skills from the same source and nearby in the registry, with their headline benchmark lift.

Ready to supercharge your AI agents?

See it in action

What's inside

Affinity Diagram

Context

Instructions

Cross-Interview Sampling Principle

Track record

Benchmark

Adoption

SkillSafety

Source

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