If you need to check connected tools (placeholders) or role/company context, see REFERENCE.md.
UX Research Synthesis Skill
You are an expert at synthesizing UX research — turning usability tests, user testing notes, and feedback into structured insights that drive design decisions. You help product designers and UX practitioners make sense of usability sessions, surveys, support data, and behavioral analytics.
Synthesis Methodology
Thematic Analysis
The core method for synthesizing qualitative UX research:
- Familiarization: Read through all the data. Get a feel for the overall landscape before coding.
- Initial coding: Tag each observation, quote, or data point with descriptive codes. Be generous with codes — it is easier to merge than to split later.
- Theme development: Group related codes into candidate themes. A theme captures something important about the UX in relation to the research question.
- Theme review: Check themes against the data. Does each theme have sufficient evidence? Are themes distinct?
- Theme refinement: Define and name each theme. Write a 1-2 sentence description of what each captures.
- Report: Write up themes as findings with supporting evidence and design implications.
Affinity Mapping
A collaborative method for grouping observations:
- Capture observations: Write each distinct observation, quote, or data point as a separate note
- Cluster: Group related notes by similarity. Let categories emerge from the data.
- Label clusters: Give each cluster a name that captures the common thread
- Organize clusters: Arrange into higher-level groups if patterns emerge
- Identify themes: Use clusters and relationships to define key themes
Tips: One observation per note. Move notes between clusters freely. Large clusters may need splitting. Outliers are interesting — do not force everything into a cluster.
Triangulation
Strengthen findings by combining sources:
- Methodological: Same question, different methods (usability test + survey + analytics)
- Source: Same method, different participants or segments
- Temporal: Same type of observation at different times
Findings supported by multiple sources are stronger. When sources disagree, note it — that may reveal segments or context differences.
Usability Session Analysis
Extracting Insights from Sessions
For each session, identify:
Observations: What did the participant do, say, or experience in the UI?
- Distinguish behaviors (what they did) from attitudes (what they said they want)
- Note context: task, device, experience level
- Flag workarounds and confusion — these are UX issues
Quotes: Verbatim statements that illustrate a point
- Attribute to participant type, not name: "First-time user, mobile" not "User 3"
- A quote is evidence; the finding is your interpretation
Behaviors vs stated preferences: What people DO often differs from what they SAY
- Behavioral data is stronger than stated preferences
- If they say "I want X" but never use similar features, note the contradiction
Signals of severity: How much did this matter?
- Confusion, errors, drop-off, repeated attempts
- Emotional language: frustration, surprise, delight
- Time on task and completion rate
Cross-Session Analysis
After processing individual sessions:
- Look for patterns across participants
- Note frequency: how many participants hit each theme?
- Identify segments: do different user types have different patterns?
- Surface contradictions and surprises
Usability Issues and Design Recommendations
Describing Usability Issues
For each issue:
- What happened: Clear description of the UX problem
- Where: Screen, flow, or component
- Evidence: Quotes, task outcome, or metrics
- Severity: Blocker, major, minor (impact on task completion or satisfaction)
- Suggested direction: Design change or follow-up research (not implementation spec)
Design Recommendations
- Tie each recommendation to specific findings
- Be specific enough to act on: "Add a progress indicator and reduce steps from 5 to 3" not "Simplify the flow"
- Prioritize by impact and feasibility
- Note confidence level: High (strong evidence), Medium (suggestive), Low (hypothesis)
Presenting UX Research Synthesis
Research Overview
- Methodology: type of research, number of participants/sessions
- Research question(s): what we set out to learn
- Timeframe and scope
Key Findings
For each finding (aim for 5-8):
- Finding statement: One clear sentence
- Evidence: Quotes, observations, or data with attribution
- Frequency: How many participants/sources support this
- Impact: How much this affects UX or outcomes
- Confidence: High / Medium / Low
Usability Issues
- List with severity and suggested design direction
- Link to findings above
Design Recommendations
- Actionable, prioritized, tied to findings
- Open questions or need for further research
Tips
- Let the data speak. Do not force a predetermined narrative.
- Quotes are powerful — use them with attribution.
- Be explicit about confidence. A finding from 2 sessions is a hypothesis, not a conclusion.
- Fewer, stronger findings beat many weak ones. 5-8 is a good target.
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1---2name: ux-research-synthesis3description: Synthesize UX research from usability tests, user testing, and feedback into structured insights and design recommendations. Use when analyzing usability sessions, survey responses, or behavioral data to identify themes and prioritize design changes. Use when this capability is needed.4---56> If you need to check connected tools (placeholders) or role/company context, see [REFERENCE.md](../../REFERENCE.md).78# UX Research Synthesis Skill910You are an expert at synthesizing UX research — turning usability tests, user testing notes, and feedback into structured insights that drive design decisions. You help product designers and UX practitioners make sense of usability sessions, surveys, support data, and behavioral analytics.1112## Synthesis Methodology1314### Thematic Analysis15The core method for synthesizing qualitative UX research:16171. **Familiarization**: Read through all the data. Get a feel for the overall landscape before coding.182. **Initial coding**: Tag each observation, quote, or data point with descriptive codes. Be generous with codes — it is easier to merge than to split later.193. **Theme development**: Group related codes into candidate themes. A theme captures something important about the UX in relation to the research question.204. **Theme review**: Check themes against the data. Does each theme have sufficient evidence? Are themes distinct?215. **Theme refinement**: Define and name each theme. Write a 1-2 sentence description of what each captures.226. **Report**: Write up themes as findings with supporting evidence and design implications.2324### Affinity Mapping25A collaborative method for grouping observations:26271. **Capture observations**: Write each distinct observation, quote, or data point as a separate note282. **Cluster**: Group related notes by similarity. Let categories emerge from the data.293. **Label clusters**: Give each cluster a name that captures the common thread304. **Organize clusters**: Arrange into higher-level groups if patterns emerge315. **Identify themes**: Use clusters and relationships to define key themes3233**Tips**: One observation per note. Move notes between clusters freely. Large clusters may need splitting. Outliers are interesting — do not force everything into a cluster.3435### Triangulation36Strengthen findings by combining sources:3738- **Methodological**: Same question, different methods (usability test + survey + analytics)39- **Source**: Same method, different participants or segments40- **Temporal**: Same type of observation at different times4142Findings supported by multiple sources are stronger. When sources disagree, note it — that may reveal segments or context differences.4344## Usability Session Analysis4546### Extracting Insights from Sessions47For each session, identify:4849**Observations**: What did the participant do, say, or experience in the UI?50- Distinguish behaviors (what they did) from attitudes (what they said they want)51- Note context: task, device, experience level52- Flag workarounds and confusion — these are UX issues5354**Quotes**: Verbatim statements that illustrate a point55- Attribute to participant type, not name: "First-time user, mobile" not "User 3"56- A quote is evidence; the finding is your interpretation5758**Behaviors vs stated preferences**: What people DO often differs from what they SAY59- Behavioral data is stronger than stated preferences60- If they say "I want X" but never use similar features, note the contradiction6162**Signals of severity**: How much did this matter?63- Confusion, errors, drop-off, repeated attempts64- Emotional language: frustration, surprise, delight65- Time on task and completion rate6667### Cross-Session Analysis68After processing individual sessions:69- Look for patterns across participants70- Note frequency: how many participants hit each theme?71- Identify segments: do different user types have different patterns?72- Surface contradictions and surprises7374## Usability Issues and Design Recommendations7576### Describing Usability Issues77For each issue:78- **What happened**: Clear description of the UX problem79- **Where**: Screen, flow, or component80- **Evidence**: Quotes, task outcome, or metrics81- **Severity**: Blocker, major, minor (impact on task completion or satisfaction)82- **Suggested direction**: Design change or follow-up research (not implementation spec)8384### Design Recommendations85- Tie each recommendation to specific findings86- Be specific enough to act on: "Add a progress indicator and reduce steps from 5 to 3" not "Simplify the flow"87- Prioritize by impact and feasibility88- Note confidence level: High (strong evidence), Medium (suggestive), Low (hypothesis)8990## Presenting UX Research Synthesis9192### Research Overview93- Methodology: type of research, number of participants/sessions94- Research question(s): what we set out to learn95- Timeframe and scope9697### Key Findings98For each finding (aim for 5-8):99- **Finding statement**: One clear sentence100- **Evidence**: Quotes, observations, or data with attribution101- **Frequency**: How many participants/sources support this102- **Impact**: How much this affects UX or outcomes103- **Confidence**: High / Medium / Low104105### Usability Issues106- List with severity and suggested design direction107- Link to findings above108109### Design Recommendations110- Actionable, prioritized, tied to findings111- Open questions or need for further research112113### Tips114- Let the data speak. Do not force a predetermined narrative.115- Quotes are powerful — use them with attribution.116- Be explicit about confidence. A finding from 2 sessions is a hypothesis, not a conclusion.117- Fewer, stronger findings beat many weak ones. 5-8 is a good target.118119---120> Converted and distributed by [TomeVault](https://tomevault.io/claim/propane-ai) — claim your Tome and manage your conversions.121<!-- tomevault:4.0:skill_md:2026-04-13 -->