/pm-synthesize-research
Synthesize user research from interviews, surveys, and feedback into structured insights. Use when you have a pile of interview notes, survey responses, or support tickets to make sense of, need to extract themes and rank findings by frequency and impact, or want to turn raw
$ npx -y skills add evolution-foundation/evo-nexus --skill pm-synthesize-research --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/pm-synthesize-research
Context preview
The summary Claude sees to decide when to auto-load this skill.
Synthesize user research from interviews, surveys, and feedback into structured insights. Use when you have a pile of interview notes, survey responses, or support tickets to make sense of, need to extract themes and rank findings by frequency and impact, or want to turn raw
SKILL.md
pm-synthesize-research.SKILL.mdname: pm-synthesize-research
description: Synthesize user research from interviews, surveys, and feedback into structured insights. Use when you have a pile of interview notes, survey responses, or support tickets to make sense of, need to extract themes and rank findings by frequency and impact, or want to turn raw feedback into roadmap recommendations.
argument-hint: "<research topic or question>"
Synthesize Research
Synthesize user research from multiple sources into structured insights and recommendations.
Usage
/pm-synthesize-research $ARGUMENTS
Workflow
1. Gather Research Inputs
Accept research from any combination of:
- **Pasted text**: Interview notes, transcripts, survey responses, feedback
- **Uploaded files**: Research documents, spreadsheets, recordings summaries
- **Notion MCP** (if available): Search for research documents, interview notes, survey results
- **Community feedback via Discord** (`discord-get-messages`): Pull messages from product feedback channels, support channels, and feature request threads
- **WhatsApp community feedback** (`int-whatsapp`): Pull relevant group messages with user pain points and requests
- **Evo CRM** (`int-evo-crm`): Pull support tickets, feature requests, and user activity signals
- **Fathom** (`int-fathom`): Pull interview recordings, meeting summaries, and user call notes
- **Licensing data** (`int-licensing`): Pull behavioral usage data and instance metrics as quantitative context
Ask the user what they have:
- What type of research? (interviews, surveys, usability tests, analytics, support tickets, sales call notes, community messages)
- How many sources / participants?
- Is there a specific question or hypothesis they are investigating?
- What decisions will this research inform?
2. Process the Research
For each source, extract:
- **Key observations**: What did users say, do, or experience?
- **Quotes**: Verbatim quotes that illustrate important points
- **Behaviors**: What users actually did (vs what they said they do)
- **Pain points**: Frustrations, workarounds, and unmet needs
- **Positive signals**: What works well, moments of delight
- **Context**: User segment, use case, experience level
3. Identify Themes and Patterns
Apply thematic analysis — see **Research Synthesis Methodology** below for detailed guidance on thematic analysis, affinity mapping, and triangulation techniques.
Group observations into themes, count frequency across participants, and assess impact severity. Note contradictions and surprises.
Create a priority matrix:
- **High frequency + High impact**: Top priority findings
- **Low frequency + High impact**: Important for specific segments
- **High frequency + Low impact**: Quality-of-life improvements
- **Low frequency + Low impact**: Note but deprioritize
4. Generate the Synthesis
Produce a structured research synthesis:
Research Overview
- Methodology: what types of research, how many participants/sources
- Research question(s): what we set out to learn
- Timeframe: when the research was conducted
Key Findings
For each major finding (aim for 5-8):
- **Finding statement**: One clear sentence describing the insight
- **Evidence**: Supporting quotes, data points, or observations (with source attribution)
- **Frequency**: How many participants/sources support this finding
- **Impact**: How significantly this affects the user experience or business
- **Confidence level**: High (strong evidence), Medium (suggestive), Low (early signal)
Order findings by priority (frequency x impact).
User Segments / Personas
If the research reveals distinct user segments:
- Segment name and description
- Key characteristics and behaviors
- Unique needs and pain points
- Size estimate if data is available
Opportunity Areas
Based on the findings, identify opportunity areas:
- What user needs are unmet or underserved
- Where do current solutions fall short
- What new capabilities would unlock value
- Prioritized by potential impact
Recommendations
Specific, actionable recommendations:
- What to build, change, or investigate further
- Tied back to specific findings
- Prioritized by impact and feasibility
Open Questions
What the research did not answer:
- Gaps in understanding
- Areas needing further investigation
- Suggested follow-up research methods
5. Review and Extend
After generating the synthesis:
- Ask if any findings need more detail or different framing
- Offer to generate specific artifacts: persona documents, opportunity maps, research presentations
- Offer to create follow-up research plans for open questions
- Offer to draft product implications (how findings should influence the roadmap)
Research Synthesis Methodology
Thematic Analysis
The core method for synthesizing qualitative research:
1. **Familiarization**: Read through all the data. Get a feel for the overall landscape before coding anything. 2. **Initial coding**: Go through the data systematically. Tag each observation, quote, or data point with descriptive codes. Be generous with codes — it is easier to merge than to split later. 3. **Theme development**: Group related codes into candidate themes. A theme captures something important about the data in relation to the research question. 4. **Theme review**: Check themes against the data. Does each theme have sufficient evidence? Are themes distinct from each other? Do they tell a coherent story? 5. **Theme refinement**: Define and name each theme clearly. Write a 1-2 sentence description of what each theme captures. 6. **Report**: Write up the themes as findings with supporting evidence.
Affinity Mapping
A collaborative method for grouping observations:
1. **Capture observations**: Write each distinct observation, quote, or data point as a separate note 2. **Cluster**: Group related notes together based on similarity. Do not pre-define categories — let them emerge from the data. 3. **Label clusters
Read more
name: pm-synthesize-research description: Synthesize user research from interviews, surveys, and feedback into structured insights. Use when you have a pile of interview notes, survey responses, or support tickets to make sense of, need to extract themes and rank findings by frequency and impact, or want to turn raw feedback into roadmap recommendations. argument-hint: "<research topic or question>"
Synthesize Research
Synthesize user research from multiple sources into structured insights and recommendations.
Usage
/pm-synthesize-research $ARGUMENTS
Workflow
1. Gather Research Inputs
Accept research from any combination of:
- **Pasted text**: Interview notes, transcripts, survey responses, feedback
- **Uploaded files**: Research documents, spreadsheets, recordings summaries
- **Notion MCP** (if available): Search for research documents, interview notes, survey results
- **Community feedback via Discord** (`discord-get-messages`): Pull messages from product feedback channels, support channels, and feature request threads
- **WhatsApp community feedback** (`int-whatsapp`): Pull relevant group messages with user pain points and requests
- **Evo CRM** (`int-evo-crm`): Pull support tickets, feature requests, and user activity signals
- **Fathom** (`int-fathom`): Pull interview recordings, meeting summaries, and user call notes
- **Licensing data** (`int-licensing`): Pull behavioral usage data and instance metrics as quantitative context
Ask the user what they have:
- What type of research? (interviews, surveys, usability tests, analytics, support tickets, sales call notes, community messages)
- How many sources / participants?
- Is there a specific question or hypothesis they are investigating?
- What decisions will this research inform?
2. Process the Research
For each source, extract:
- **Key observations**: What did users say, do, or experience?
- **Quotes**: Verbatim quotes that illustrate important points
- **Behaviors**: What users actually did (vs what they said they do)
- **Pain points**: Frustrations, workarounds, and unmet needs
- **Positive signals**: What works well, moments of delight
- **Context**: User segment, use case, experience level
3. Identify Themes and Patterns
Apply thematic analysis — see **Research Synthesis Methodology** below for detailed guidance on thematic analysis, affinity mapping, and triangulation techniques.
Group observations into themes, count frequency across participants, and assess impact severity. Note contradictions and surprises.
Create a priority matrix:
- **High frequency + High impact**: Top priority findings
- **Low frequency + High impact**: Important for specific segments
- **High frequency + Low impact**: Quality-of-life improvements
- **Low frequency + Low impact**: Note but deprioritize
4. Generate the Synthesis
Produce a structured research synthesis:
Research Overview
- Methodology: what types of research, how many participants/sources
- Research question(s): what we set out to learn
- Timeframe: when the research was conducted
Key Findings
For each major finding (aim for 5-8):
- **Finding statement**: One clear sentence describing the insight
- **Evidence**: Supporting quotes, data points, or observations (with source attribution)
- **Frequency**: How many participants/sources support this finding
- **Impact**: How significantly this affects the user experience or business
- **Confidence level**: High (strong evidence), Medium (suggestive), Low (early signal)
Order findings by priority (frequency x impact).
User Segments / Personas
If the research reveals distinct user segments:
- Segment name and description
- Key characteristics and behaviors
- Unique needs and pain points
- Size estimate if data is available
Opportunity Areas
Based on the findings, identify opportunity areas:
- What user needs are unmet or underserved
- Where do current solutions fall short
- What new capabilities would unlock value
- Prioritized by potential impact
Recommendations
Specific, actionable recommendations:
- What to build, change, or investigate further
- Tied back to specific findings
- Prioritized by impact and feasibility
Open Questions
What the research did not answer:
- Gaps in understanding
- Areas needing further investigation
- Suggested follow-up research methods
5. Review and Extend
After generating the synthesis:
- Ask if any findings need more detail or different framing
- Offer to generate specific artifacts: persona documents, opportunity maps, research presentations
- Offer to create follow-up research plans for open questions
- Offer to draft product implications (how findings should influence the roadmap)
Research Synthesis Methodology
Thematic Analysis
The core method for synthesizing qualitative research:
1. **Familiarization**: Read through all the data. Get a feel for the overall landscape before coding anything. 2. **Initial coding**: Go through the data systematically. Tag each observation, quote, or data point with descriptive codes. Be generous with codes — it is easier to merge than to split later. 3. **Theme development**: Group related codes into candidate themes. A theme captures something important about the data in relation to the research question. 4. **Theme review**: Check themes against the data. Does each theme have sufficient evidence? Are themes distinct from each other? Do they tell a coherent story? 5. **Theme refinement**: Define and name each theme clearly. Write a 1-2 sentence description of what each theme captures. 6. **Report**: Write up the themes as findings with supporting evidence.
Affinity Mapping
A collaborative method for grouping observations:
1. **Capture observations**: Write each distinct observation, quote, or data point as a separate note 2. **Cluster**: Group related notes together based on similarity. Do not pre-define categories — let them emerge from the data. 3. **Label clusters
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