synthesize-research

What this skill does

# Synthesize Research

> If you see unfamiliar placeholders or need to check which tools are connected, see [CONNECTORS.md](../../CONNECTORS.md).

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

## Usage

```
/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 **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**: Give each cluster a descriptive name that captures the common thread
4. **Organize clusters**: Arrange clusters into higher-level groups if patterns emerge
5. **Identify themes**: The clusters and their relationships reveal the key themes

**Tips for affinity mapping**:
- One observation per note. Do not combine multiple insights.
- Move notes between clusters freely. The first grouping is rarely the best.
- If a cluster gets too large, it probably contains multiple themes. Split it.
- Outliers are interesting. Do not force every observation into a cluster.
- The process of grouping is as valuable as the output. It builds shared understanding.

### Triangulation
Strengthen findings by combining multiple data sources:

- **Methodological triangulation**: Same question, different methods (interviews + survey + analytics)
- **Source triangulation**: Same method, different participants or segments
- **Temporal triangulation**: Same observation at different points in time

A finding supported by multiple sources and methods is much stronger than one supported by a single source. When sources disagree, that is interesting — it may reveal different user segments or contexts.

## Interview Note Analysis

### Extracting Insights from Interview Notes
For each interview, identify:

**Observations**: What did the participant describe doing, experiencing, or feeling?
- Distinguish between behaviors (what they do) and attitudes (what they think/feel)
- Note context: when, where, with whom, how often
- Flag workarounds — these are unmet needs in disguise

**Direct quotes**: Verbatim statements that powerfully illustrate a point
- Good quotes are specific and vivid, not generic
- Attribute to participant type, not name: "Enterprise admin, 200-person team" not "Sarah"
- A quote is evidence, not a finding. The finding is your interpretation of what the quote means.

**Behaviors vs stated preferences**: What people DO often differs from what they SAY they want
- Behavioral observations are stronger evidence than stated preferences
- If a participant says "I want feature X" but their workflow shows they never use similar features, note the contradiction
- Look for revealed preferences through actual behavior

**Signals of intensity**: How much does this matter to the participant?
- Emotional language: frustration, excitement, resignation
- Frequency: how often do they encounter this issue
- Workarounds: how much effort do they expend working around the problem
- Impact: what is the consequence when things go wrong

### Cross-Interview Analysis
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