user-research-synthesis
Analyze and synthesize user research findings following PM best practices. Use when the user provides user research data, interview transcripts, survey results, or user feedback that needs to be analyzed, synthesized, or summarized into insights and recommendations.
Best use case
user-research-synthesis is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Analyze and synthesize user research findings following PM best practices. Use when the user provides user research data, interview transcripts, survey results, or user feedback that needs to be analyzed, synthesized, or summarized into insights and recommendations.
Teams using user-research-synthesis should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/user-research-synthesis/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How user-research-synthesis Compares
| Feature / Agent | user-research-synthesis | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Analyze and synthesize user research findings following PM best practices. Use when the user provides user research data, interview transcripts, survey results, or user feedback that needs to be analyzed, synthesized, or summarized into insights and recommendations.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
Best AI Skills for Claude
Explore the best AI skills for Claude and Claude Code across coding, research, workflow automation, documentation, and agent operations.
AI Agent for YouTube Script Writing
Find AI agent skills for YouTube script writing, video research, content outlining, and repeatable channel production workflows.
SKILL.md Source
# User Research Synthesis Skill
This skill helps analyze user research data and transform it into actionable insights following a structured methodology.
## Synthesis Framework
### 1. Data Collection Overview
- **Research Type**: Interviews, surveys, usability tests, etc.
- **Participant Profile**: Demographics, segments, sample size
- **Research Questions**: What we sought to learn
- **Methodology**: How data was collected
### 2. Key Themes Identification
Organize findings into themes using this structure:
**Theme Name**
- **Description**: What this theme represents
- **Prevalence**: How many participants mentioned this (e.g., "8 out of 12 participants")
- **Supporting Quotes**: 2-3 representative quotes
- **Implication**: What this means for our product
Aim for 4-8 major themes per research effort.
### 3. Pain Points Analysis
For each identified pain point:
- **Pain Point**: Clear description
- **Severity**: High/Medium/Low (based on impact and frequency)
- **Current Workaround**: How users deal with it today
- **Evidence**: Specific examples from research
### 4. Feature Requests
Categorize requests:
- **Must-Have**: Critical needs blocking user success
- **High Value**: Would significantly improve experience
- **Nice-to-Have**: Incremental improvements
For each request:
- **Request**: What users asked for
- **Frequency**: How often it came up
- **User Quote**: Representative example
- **Underlying Need**: Why they want this (dig deeper than surface request)
### 5. User Workflow Insights
Document actual workflows observed:
- **Current State**: How users accomplish tasks today
- **Pain Points**: Where they struggle
- **Ideal State**: What they wish they could do
- **Opportunities**: Where we can add value
### 6. Segmentation Insights
If research reveals distinct user segments:
- **Segment Name**: Descriptive label
- **Characteristics**: What defines this segment
- **Unique Needs**: How their needs differ
- **Size/Importance**: Relative weight for prioritization
### 7. Competitive Insights
If users mentioned competitors or alternatives:
- **Competitor/Alternative**: What they use
- **Why They Use It**: What it does well
- **Gaps**: What it doesn't do
- **Switching Barriers**: Why they don't switch fully
### 8. Recommendations
Prioritized recommendations based on insights:
**High Priority**
- Recommendation with supporting evidence
- Expected impact
**Medium Priority**
- Recommendation with supporting evidence
- Expected impact
**Low Priority / Future Consideration**
- Recommendation with supporting evidence
- Expected impact
### 9. Open Questions
Research gaps identified:
- What we still need to understand
- Suggested follow-up research
- Uncertainties requiring validation
## Analysis Guidelines
**When synthesizing interviews:**
- Look for patterns across multiple participants
- Note both what users say AND what they do
- Pay attention to emotional reactions
- Identify jobs-to-be-done, not just feature requests
**When analyzing quotes:**
- Use verbatim quotes in "quotation marks"
- Attribute quotes: [Participant ID, Role, Context]
- Select quotes that illustrate patterns, not outliers
- Include both positive and negative feedback
**When identifying themes:**
- Use descriptive names, not generic labels
- Provide evidence for each theme
- Quantify when possible ("7 out of 10 users...")
- Connect themes to business objectives
## Quality Standards
✅ **Good Synthesis:**
- Identifies patterns, not just individual responses
- Connects insights to product decisions
- Includes supporting evidence for each claim
- Separates observations from interpretations
- Prioritizes findings by impact
❌ **Poor Synthesis:**
- Lists every individual comment
- Lacks evidence or examples
- Makes unsupported leaps
- Focuses on solutions before understanding problems
- Ignores contradictory data
## Example Theme
```
**Theme: Information Overload During Onboarding**
**Description**: Users consistently expressed feeling overwhelmed by the amount of information presented during initial setup, leading to incomplete onboarding and delayed time-to-value.
**Prevalence**: 9 out of 12 participants mentioned this issue unprompted
**Supporting Quotes**:
- "I just wanted to get started, but it felt like I needed to read a manual first" [P3, Marketing Manager]
- "By the third screen of instructions, I started clicking 'Next' without reading" [P7, Sales Rep]
- "I wish there was a 'quick start' option for people like me who just want to try it" [P11, Product Designer]
**Implication**: Our current onboarding flow prioritizes completeness over engagement. We should consider a progressive disclosure approach where users can start using the product quickly and learn advanced features contextually.
**Recommended Action**:
- Design a "Quick Start" path that gets users to first value in <3 minutes
- Move advanced configuration to contextual help within the app
- Test with 5-10 new users before full rollout
- Expected impact: +20-30% activation rate improvement
```
## Template Output Structure
When synthesizing research, use this structure:
```markdown
# User Research Synthesis: [Research Topic]
## Research Overview
- **Date**: [Date range]
- **Methodology**: [Interview/Survey/Testing]
- **Participants**: [Number] [User types]
- **Research Questions**:
1. [Question 1]
2. [Question 2]
3. [Question 3]
## Executive Summary
[2-3 sentence overview of key findings and implications]
## Key Themes
### Theme 1: [Theme Name]
[Full theme documentation as shown in example above]
### Theme 2: [Theme Name]
[Full theme documentation]
[Continue with 4-8 themes]
## Pain Points Summary
| Pain Point | Severity | Frequency | Current Workaround |
|------------|----------|-----------|-------------------|
| [Pain 1] | High | 10/12 users | [How they cope] |
| [Pain 2] | Medium | 7/12 users | [How they cope] |
## Feature Requests
### Must-Have
1. **[Request]** - Mentioned by [X] participants
- Quote: "[Representative quote]"
- Underlying need: [Why they want this]
### High Value
[Similar structure]
### Nice-to-Have
[Similar structure]
## Recommendations
### High Priority (0-3 months)
1. **[Recommendation]**
- Supporting evidence: [Data from research]
- Expected impact: [What will improve]
- Effort estimate: [Rough sizing]
### Medium Priority (3-6 months)
[Similar structure]
### Future Consideration (6+ months)
[Similar structure]
## Open Questions
1. [Question requiring more research]
2. [Uncertainty to validate]
3. [Follow-up study needed]
## Appendix
- Interview guide used
- Full participant demographics
- Raw notes/transcripts (link)
```Related Skills
stakeholder-update
Create executive stakeholder updates following proven communication frameworks. Use when the user needs to create a status update, progress report, executive summary, or communication for leadership, stakeholders, or executives.
prd-template
Product Requirements Document creation following proven PM template structure. Use when the user asks to create, write, draft, or help with a PRD, product requirements document, product spec, feature specification, or product documentation for a new feature or product.
meeting-notes
Structure and format meeting notes following PM best practices. Use when the user needs to create, format, or organize meeting notes, capture action items from meetings, or document discussions and decisions.
competitive-analysis
Analyze competitors and create competitive landscape documentation. Use when the user asks to analyze competitors, create competitive analysis, compare features with competitors, track competitive landscape, or understand competitive positioning.
tech-stack-evaluator
Technology stack evaluation and comparison with TCO analysis, security assessment, and ecosystem health scoring. Use when comparing frameworks, evaluating technology stacks, calculating total cost of ownership, assessing migration paths, or analyzing ecosystem viability.
tdd-guide
Test-driven development workflow with test generation, coverage analysis, and multi-framework support
senior-security
Security engineering toolkit for threat modeling, vulnerability analysis, secure architecture, and penetration testing. Includes STRIDE analysis, OWASP guidance, cryptography patterns, and security scanning tools.
senior-secops
Comprehensive SecOps skill for application security, vulnerability management, compliance, and secure development practices. Includes security scanning, vulnerability assessment, compliance checking, and security automation. Use when implementing security controls, conducting security audits, responding to vulnerabilities, or ensuring compliance requirements.
senior-qa
This skill should be used when the user asks to "generate tests", "write unit tests", "analyze test coverage", "scaffold E2E tests", "set up Playwright", "configure Jest", "implement testing patterns", or "improve test quality". Use for React/Next.js testing with Jest, React Testing Library, and Playwright.
senior-prompt-engineer
This skill should be used when the user asks to "optimize prompts", "design prompt templates", "evaluate LLM outputs", "build agentic systems", "implement RAG", "create few-shot examples", "analyze token usage", or "design AI workflows". Use for prompt engineering patterns, LLM evaluation frameworks, agent architectures, and structured output design.
senior-ml-engineer
ML engineering skill for productionizing models, building MLOps pipelines, and integrating LLMs. Covers model deployment, feature stores, drift monitoring, RAG systems, and cost optimization.
senior-fullstack
Fullstack development toolkit with project scaffolding for Next.js/FastAPI/MERN/Django stacks and code quality analysis. Use when scaffolding new projects, analyzing codebase quality, or implementing fullstack architecture patterns.