User Research Synthesis Skill

SkillDev tools

Specialized skill for synthesizing qualitative user research into actionable insights. Analyzes interview transcripts, extracts patterns and themes, identifies pain points, creates affinity diagrams, and generates persona attributes from research data.

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Then ask your AI: use the User Research Synthesis Skill skill

What this skill tells your AI

The instructions your AI receives, as published by a5c-ai/babysitter in library/specializations/product-management/skills/user-research-synthesis/SKILL.md and read by ahel’s review.

Turn raw user research data (interviews, surveys, feedback, support tickets) into structured, actionable insights.

When to Use

  • User has interview notes and needs to synthesize findings
  • User has survey results to analyze
  • User wants to identify patterns across user feedback
  • User says /user-research-synthesis followed by research data
  • Any time qualitative or quantitative user data needs structure

Framework: Research Synthesis (5 Steps)

Step 1: Organize Raw Data

  • Source type: Interviews / Surveys / Support tickets / App reviews / Usage data
  • Sample size: How many data points?
  • User segments: Who was included? Any notable gaps?
  • Timeframe: When was this data collected?

Step 2: Code & Theme

Identify recurring themes across the data:

ThemeFrequencySentimentExample Quote
[Theme 1]X of Y participantsPositive/Negative/Mixed"..."
[Theme 2]X of Y participants"..."

Group themes into categories:

  • Pain Points: What's frustrating or broken
  • Unmet Needs: What users want but don't have
  • Bright Spots: What's working well (don't break these)
  • Surprises: Unexpected findings

Step 3: Prioritize Insights

For each insight, assess:

  • Prevalence: How many users mentioned this? (1 = rare, 5 = universal)
  • Severity: How painful is this? (1 = minor annoyance, 5 = deal-breaker)
  • Actionability: Can we do something about this? (1 = hard, 5 = clear path)

Priority Score = Prevalence x Severity x Actionability

Step 4: Generate Recommendations

For the top 3-5 insights:

  • Insight: Clear statement of what we learned
  • Evidence: Supporting data points and quotes
  • Implication: What this means for the product
  • Recommendation: Specific next step (build, test, investigate further)
  • Confidence: High / Medium / Low (based on data quality)

Step 5: Research Report

Executive Summary (2-3 sentences): What we studied, what we found, what we should do.

Key Findings (3-5 bullet points): The most important insights with supporting data.

Detailed Findings: Each theme with quotes, data, and implications.

Recommendations: Prioritized action items.

Methodology & Limitations: How research was done, sample biases, confidence level.

Input Formats Supported

  • Raw interview notes: Paste them in, the skill will code and theme them
  • Survey results: Paste summary stats or raw responses
  • Support tickets: Paste representative tickets for pattern analysis
  • App store reviews: Paste reviews for sentiment and theme analysis
  • Mixed: Combine multiple sources for triangulated insights

Output Format

Generate a clean research report in markdown. Use tables for theme coding. Include direct quotes as evidence. Be specific about confidence levels and limitations.

Tips for Better Synthesis

  • Look for contradictions — users who say opposite things often reveal a segmentation opportunity
  • Pay attention to workarounds — what users hack together reveals unmet needs
  • Note what users do vs. what they say — behavioral data trumps stated preferences
  • Flag sample bias — if you only talked to power users, say so

Signals

GitHub stars
2k
Forks
112
Last commit
Sep 2026
Advanced
Item type
skill
Key
user-research-synthesis
Source
github.com/a5c-ai/babysitter