grailautomation/synthesize-research

Synthesize Research

If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.

Synthesize user research from multiple sources into structured insights and recommendations.

Usage

/product-management: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
• ~~knowledge base (if connected): Search for research documents, interview notes, survey results
• ~~user feedback (if connected): Pull recent support tickets, feature requests, bug reports
• ~~product analytics (if connected): Pull usage data, funnel metrics, behavioral data
• ~~meeting transcription (if connected): Pull interview recordings, meeting summaries, and discussion notes

Ask the user what they have:

• What type of research? (interviews, surveys, usability tests, analytics, support tickets, sales call notes)
• 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 the user-research-synthesis skill for detailed methodology including 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)

Output Format

Use clear headers and structured formatting. Each finding should stand on its own — a reader should be able to read any single finding and understand it without reading the rest.

Tips

• Let the data speak. Do not force findings into a predetermined narrative.
• Distinguish between what users say and what they do. Behavioral data is stronger than stated preferences.
• Quotes are powerful evidence. Include them generously, with attribution to participant type (not name).
• Be explicit about confidence levels. A finding from 2 interviews is a hypothesis, not a conclusion.
• Contradictions in the data are interesting, not inconvenient. They often reveal distinct user segments.
• Recommendations should be specific enough to act on. "Improve onboarding" is not actionable. "Add a progress indicator to the setup flow" is.
• Resist the temptation to synthesize too many themes. 5-8 strong findings are better than 20 weak ones.