research-synthesis

name	research-synthesis
description	Synthesize user research into actionable themes, insights, and recommendations. TRIGGER when: user says "synthesize research", "analyze interviews", "find patterns", "research themes", "insight mapping", or has raw research data to distill.
argument-hint	[research data file or description]
user-invocable	true

Research Synthesis

Transform raw user research (interviews, surveys, usability tests, support tickets) into structured insights that drive design and product decisions.

Synthesis Process

Phase 1 — Ingest Raw Data

1. If $ARGUMENTS is a file path, read it.
2. Identify the research type:
   • Interviews: transcripts, notes
   • Surveys: quantitative + open-ended responses
   • Usability tests: task success/failure, observations, quotes
   • Support tickets: categorized complaints, feature requests
   • Analytics: behavioral data, funnel metrics
3. If data is insufficient, ask what research was conducted and key findings.

Phase 2 — Code & Cluster

Affinity Mapping

1. Extract observations: Pull discrete data points — quotes, behaviors, pain points, needs.
2. Tag each observation with:
   • Participant/source ID
   • Sentiment: positive / negative / neutral
   • Category: behavior / pain-point / need / workaround / delight / confusion
3. Cluster into themes: Group related observations. A theme needs 3+ observations from 2+ sources.
4. Name each theme: Use user-centric language ("Users distrust auto-save" not "Save feature issue").

Phase 3 — Generate Insights

For each theme, produce a structured insight:

### Insight: {descriptive title}

**Observation**: {What we saw — grounded in data}
- "{direct quote}" — P3
- "{direct quote}" — P7
- {N} of {total} participants exhibited this behavior

**Interpretation**: {What it means — our analysis}

**Implication**: {What we should do about it — actionable recommendation}

**Confidence**: High / Medium / Low
- Based on: {N} sources, {method type}, consistency of signal

**Priority**: {Critical / High / Medium / Low}
- Impact: {how many users affected, severity of pain}
- Effort: {estimated implementation complexity}

Phase 4 — Prioritization Matrix

Plot insights on an impact/effort matrix:

## Priority Matrix

### High Impact, Low Effort (Do First)
1. {Insight title} — {one-line summary}

### High Impact, High Effort (Plan)
1. {Insight title} — {one-line summary}

### Low Impact, Low Effort (Quick Wins)
1. {Insight title} — {one-line summary}

### Low Impact, High Effort (Deprioritize)
1. {Insight title} — {one-line summary}

Phase 5 — Output Report

## Research Synthesis — {project/study name}

### Study Overview
- **Method**: {interviews / survey / usability test / mixed}
- **Participants**: {N} participants, {demographics or segments}
- **Date**: {when research was conducted}
- **Research questions**: {what we were trying to learn}

### Key Themes
1. {Theme name} — {N} observations from {N} sources
2. {Theme name} — ...

### Insights (ordered by priority)
{structured insights from Phase 3}

### Recommendations
1. **{Action}** — addresses insights {1, 3}. Priority: {High}
2. **{Action}** — addresses insight {2}. Priority: {Medium}

### Open Questions
- {Questions the research raised but didn't answer}
- {Suggested follow-up research}

### Raw Data Summary
- Total observations coded: {N}
- Themes identified: {N}
- Insights generated: {N}

Quality Standards

• Ground every insight in data: No interpretation without at least 2 supporting observations.
• Distinguish observation from interpretation: Keep them separate — observations are facts, interpretations are analysis.
• Include dissenting data: If 8/10 users loved a feature but 2 hated it, note both.
• Quantify where possible: "Most users" is weak. "7 of 10 users" is strong.
• Preserve participant voices: Include direct quotes — they're the most compelling evidence.
• Flag confidence levels: Be honest about what the data supports vs. what's inferred.

Quality Checklist

• Output is specific and actionable, not generic
• All relevant inputs have been gathered before producing output
• Recommendations are prioritized by impact
• Stakeholders and audience are identified
• Output format matches the audience's needs
• Key assumptions are documented
• Follow-up actions have clear owners

Edge Cases

• If input data is incomplete, state assumptions explicitly and flag gaps
• For time-sensitive situations, prioritize speed over comprehensiveness
• If multiple stakeholders have conflicting needs, document the tradeoffs
• For first-time use, start with a simplified version and iterate
• Adapt the depth and detail to the audience's expertise level