user-research-synthesis
npx machina-cli add skill aroyburman-codes/pm-skills/user-research-synthesis --openclawUser Research Synthesis Skill
Turn raw user research data (interviews, surveys, feedback, support tickets) into structured, actionable insights.
When to Use
- User has interview notes and needs to synthesize findings
- User has survey results to analyze
- User wants to identify patterns across user feedback
- User says
/user-research-synthesisfollowed by research data - Any time qualitative or quantitative user data needs structure
Framework: Research Synthesis (5 Steps)
Step 1: Organize Raw Data
- Source type: Interviews / Surveys / Support tickets / App reviews / Usage data
- Sample size: How many data points?
- User segments: Who was included? Any notable gaps?
- Timeframe: When was this data collected?
Step 2: Code & Theme
Identify recurring themes across the data:
| Theme | Frequency | Sentiment | Example Quote |
|---|---|---|---|
| [Theme 1] | X of Y participants | Positive/Negative/Mixed | "..." |
| [Theme 2] | X of Y participants | "..." |
Group themes into categories:
- Pain Points: What's frustrating or broken
- Unmet Needs: What users want but don't have
- Bright Spots: What's working well (don't break these)
- Surprises: Unexpected findings
Step 3: Prioritize Insights
For each insight, assess:
- Prevalence: How many users mentioned this? (1 = rare, 5 = universal)
- Severity: How painful is this? (1 = minor annoyance, 5 = deal-breaker)
- Actionability: Can we do something about this? (1 = hard, 5 = clear path)
Priority Score = Prevalence x Severity x Actionability
Step 4: Generate Recommendations
For the top 3-5 insights:
- Insight: Clear statement of what we learned
- Evidence: Supporting data points and quotes
- Implication: What this means for the product
- Recommendation: Specific next step (build, test, investigate further)
- Confidence: High / Medium / Low (based on data quality)
Step 5: Research Report
Executive Summary (2-3 sentences): What we studied, what we found, what we should do.
Key Findings (3-5 bullet points): The most important insights with supporting data.
Detailed Findings: Each theme with quotes, data, and implications.
Recommendations: Prioritized action items.
Methodology & Limitations: How research was done, sample biases, confidence level.
Input Formats Supported
- Raw interview notes: Paste them in, the skill will code and theme them
- Survey results: Paste summary stats or raw responses
- Support tickets: Paste representative tickets for pattern analysis
- App store reviews: Paste reviews for sentiment and theme analysis
- Mixed: Combine multiple sources for triangulated insights
Output Format
Generate a clean research report in markdown. Use tables for theme coding. Include direct quotes as evidence. Be specific about confidence levels and limitations.
Tips for Better Synthesis
- Look for contradictions — users who say opposite things often reveal a segmentation opportunity
- Pay attention to workarounds — what users hack together reveals unmet needs
- Note what users do vs. what they say — behavioral data trumps stated preferences
- Flag sample bias — if you only talked to power users, say so
Source
git clone https://github.com/aroyburman-codes/pm-skills/blob/main/skills/user-research-synthesis/SKILL.mdView on GitHub Overview
Turns raw user research data—interviews, surveys, feedback, and tickets—into structured, actionable insights. It identifies cross-cutting themes, pain points, and opportunities for your product. The output is stakeholder-ready research reports that inform strategy and roadmaps.
How This Skill Works
The skill ingests raw data, categorizes by source, and notes sample size, segments, and timeframe. It codes recurring themes with frequency and sentiment, computes a Priority Score (prevalence x severity x actionability), then generates top insights and a markdown research report with quotes, implications, and recommendations.
When to Use It
- User has interview notes and needs to synthesize findings
- User has survey results to analyze
- User wants to identify patterns across user feedback
- User says `/user-research-synthesis` followed by research data
- Any time qualitative or quantitative user data needs structure
Quick Start
- Step 1: Paste your raw data (interview notes, survey data, or tickets) into the tool
- Step 2: The skill organizes data, codes themes, and computes priority scores
- Step 3: Review the generated executive summary, findings, and recommendations
Best Practices
- Organize data by source, sample size, segments, and timeframe
- Code themes with frequency and sentiment; use a theme table for clarity
- Compute a Priority Score = Prevalence x Severity x Actionability to rank insights
- Anchor insights with direct quotes and concrete data points as evidence
- Document methodology and limitations to flag sample bias and data quality
Example Use Cases
- Synthesize 40 interview notes to surface top pain points and unmet needs
- Analyze survey results to prioritize feature requests by impact
- Triangulate support tickets with app reviews to reveal recurring frustrations
- Produce an executive-ready research report with an actionable roadmap
- Identify contradictions and workarounds to uncover segmentation opportunities