Synthesize Research

What This Skill Does

This skill synthesizes user research from multiple sources into structured insights and recommendations. It helps researchers and product teams identify key themes and patterns in user data. It's useful for anyone looking to understand user needs and inform product decisions.

When to Use

• Analyzing interview notes and transcripts.
• Synthesizing survey responses and feedback.
• Processing research documents and spreadsheets.
• Identifying themes in user support tickets.
• Analyzing product usage data and funnel metrics.
• Extracting insights from meeting recordings.

Key Features

• Accepts research from text, files, and connected tools.
• Extracts key observations, quotes, and behaviors.
• Identifies pain points and positive signals.
• Applies thematic analysis to group observations.
• Assesses impact severity and frequency.
• Supports thematic analysis, affinity mapping, and triangulation.

Manual Installation

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:

• Familiarization: Read through all the data.
• Get a feel for the overall landscape before coding anything. 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. Theme development: Group related codes into candidate themes. A theme captures something important about the data in relation to the research question. 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?
• Theme refinement: Define and name each theme clearly.
• Write a 1-2 sentence description of what each theme captures. Report: Write up the themes as findings with supporting evidence.

Affinity Mapping

A collaborative method for grouping observations:

Capture observations: Write each distinct observation, quote, or data point as a separate note

• Cluster: Group related notes together based on similarity.
• Do not pre-define categories — let them emerge from the data. Label clusters: Give each cluster a descriptive name that captures the common thread Organize clusters: Arrange clusters into higher-level groups if patterns emerge 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