user-research-synthesis
Skill25 starsChanged 9 months agoMITView on GitHub
Synthesize user interviews, survey results, feedback, and support tickets into structured insights. Identifies themes, pain points, and opportunities. Generates research reports ready for stakeholders.
14 other projects use a version of this.
Adopted by
Most recently taken up by
- insight68/Skillstheir own version, first seen Nov 8, 2026
- RBraga01/builder-producttheir own version, first seen Nov 1, 2026
- codeApe-7/ai-agent-workflowGrouptheir own version, first seen Nov 1, 2026
- Maudeunfledged834/startup-founder-skillstheir own version, first seen Oct 25, 2026
- MaxMiksa/Autonomous-Companytheir own version, first seen Oct 15, 2026
- akachandan1/proofstacktheir own version, first seen Oct 11, 2026
Used with
Projects with it also use mvp-scoping, competitive-analysis and 1 more.
Other files people use alongside this one.
Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
shawnpangSkillmvp-scoping
When the user needs to decide what to build, cut, and defer for a first release or minimum viable version of a product or feature.
Also in 3 projects that use this
AnthropicSkillcompetitive-analysis
Framework for building competitive landscape decks — market positioning, competitor deep-dives, comparative analysis, strategic synthesis. Use when the user asks for a competitive landscape, competitor analysis, peer comparison, market positioning assessment, strategic review, or investment memo deck. Also triggers on "who are the competitors to X", "benchmark X against peers", "build a market map", or any request to systematically evaluate competitive dynamics across an industry.
Also in 4 projects that use this
silvainfmSkilllaunch-strategy
When the user wants to plan a product launch, feature announcement, or release strategy. Also use when the user mentions 'launch,' 'Product Hunt,' 'feature release,' 'announcement,' 'go-to-market,' 'beta launch,' 'early access,' 'waitlist,' or 'product update.' This skill covers phased launches, channel strategy, and ongoing launch momentum.
Also in 3 projects that use this
Other versions14 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 14
- MaxMiksa/Auto-Company★ 3.2ksame name
- MaxMiksa/Autonomous-Company★ 3.2ksame name
- shawnpang/startup-founder-skills★ 331same name
- nicepkg/auto-company★ 188same name
- NikitaDmitrieff/auto-co-meta★ 43same name
- codeApe-7/ai-agent-workflowGroup★ 18same name
- Maudeunfledged834/startup-founder-skills★ 8same name
- insight68/Skills★ 5same name
- Uwayxt/agent-skills★ 4same name
- RBraga01/builder-product★ 2same name
4 more not listed
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository16 other files it gives its agents.
- ai-ethics-tradeoffsSkill
- ai-market-landscapeSkill
- ai-product-teardownSkill
- analytical-pmSkill
- behavioral-pmSkill
- launch-checklistSkill
- metric-dashboardSkill
- mock-interviewerSkill
- pm-case-studySkill
- pm-writingSkill
- prd-writerSkill
- prioritization-matrixSkill
- product-senseSkill
- product-strategySkill
- stakeholder-briefSkill
- technical-pmSkill
The file
We split this skill into the rules it gives an agent
2 rules. 0 are common, 2 are distinctive, and 0 are very specific to this project.
0
Common
Most of the 14 edited versions we read keep these.
2
Distinctive
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 24
Timeframe: When was this data collected?
- Distinctive: line 84
Look for contradictions — users who say opposite things often reveal a segmentation opportunity
“Common” is measured: kept by at least 6 in 10 of the 14 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
User 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
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