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Discover interview synthesis

Skill product-on-purpose/pm-skills/skills/discover-interview-synthesis

Synthesizes user research interviews into actionable insights, patterns, and recommendations. Use after conducting user interviews, customer calls, or usability sessions to extract and communicate findings across participants. Distinct from foundation-meeting-recap, which summarizes one internal meeting for its attendees; this skill aggregates research conversations into evidence-backed findings.From its SKILL.md

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npx -y skills add product-on-purpose/pm-skills --skill discover-interview-synthesis

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SKILL.md

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<!-- PM-Skills | https://github.com/product-on-purpose/pm-skills | Apache 2.0 -->

Interview Synthesis

An interview synthesis transforms raw user research data into structured insights that drive product decisions. Rather than simply listing what participants said, a good synthesis identifies patterns across conversations, connects observations to underlying user needs, and translates findings into actionable recommendations.

When to Use

  • After completing a round of user interviews (typically 5+ participants)
  • Following customer discovery calls or sales feedback sessions
  • After usability testing sessions to consolidate observations
  • When stakeholders need a summary of research findings
  • Before ideation sessions to ground the team in user reality

When NOT to Use

  • You are summarizing one internal meeting for its attendees -> use foundation-meeting-recap
  • You need patterns across multiple meetings over time -> use foundation-meeting-synthesize
  • Your data is survey responses rather than interviews -> use measure-survey-analysis
  • The findings are synthesized and you are ready to frame the problem -> use define-problem-statement
  • You have synthesized findings and want to map them onto a customer's journey across stages and touchpoints -> use discover-journey-map

Instructions

When asked to synthesize interview findings, follow these steps:

  1. Gather the Raw Material Collect all interview notes, transcripts, or recordings. Ensure you have data from at least 3 participants to identify meaningful patterns. Note the research objective and methodology used.

  2. Create Participant Profiles Document each participant with relevant context: their role, segment, tenure, and any notable characteristics. This helps readers assess the representativeness of findings.

  3. Identify Recurring Themes Read through all notes and tag observations by topic. Look for themes that appear across multiple participants (ideally 3+). Distinguish between frequently mentioned topics and one-off comments.

  4. Extract Meaningful Quotes Capture 3-5 verbatim quotes per theme that powerfully illustrate the insight. Good quotes are specific, emotional, or particularly articulate. Always attribute quotes to participant IDs.

  5. Synthesize into Insights Transform themes into insight statements. An insight goes beyond observation ("users mentioned X") to interpretation ("users need Y because of Z"). Connect what you heard to why it matters.

  6. Formulate Recommendations Based on the insights, propose prioritized actions. Each recommendation should tie directly to an insight. Note confidence level based on strength of evidence.

  7. Document Limitations Acknowledge what you didn't learn, sample biases, or areas needing further research. Honest limitations increase credibility.

Output Format

Use the template in references/TEMPLATE.md to structure the output. A complete synthesis fills every template section: Research Overview; Key Themes; Notable Quotes; Insights; Recommendations; and Appendix.

Quality Checklist

Before finalizing, verify:

  • Themes are supported by evidence from 3+ participants
  • Quotes are verbatim and attributed to participant IDs
  • Insights explain "why" not just "what"
  • Recommendations are specific and actionable
  • Participant identities are protected (no PII)
  • Limitations and biases are acknowledged

Examples

See references/EXAMPLE.md for a completed example.

What ships with it: 5 files

20.4 KB alongside SKILL.md

references/

Gives 0 of the 12 instructions most research analysis skills give in 683 tokens

Counted across 1,213 of the 2,113 authors here whose files we hold, read 2026-09-06

  • Cite sources for every important claimin 47 of 1213, across 38 files
  • Separate facts from inferences and recommendationsin 21 of 1213, across 12 files
  • Write findings to a markdown filein 19 of 1213
  • Label every insight with a confidence levelin 18 of 1213, across 8 files
  • Read product marketing context before asking questionsin 18 of 1213, across 8 files
  • Rank themes by frequency and intensityin 16 of 1213, across 6 files
  • Establish research mode before proceedingin 16 of 1213, across 6 files
  • Segment survey responses by customer tier or tenurein 16 of 1213, across 6 files
  • Categorize support tickets before analyzingin 16 of 1213, across 6 files
  • Weight research sources from the last twelve monthsin 16 of 1213, across 6 files
  • Use at least five data points per segmentin 15 of 1213, across 5 files
  • Extract verbatim quotes for all research findingsin 15 of 1213, across 5 files

Said here and by no other author read

  • collect interview notes and transcripts
  • document participant roles and segments
  • tag observations to identify recurring themes
  • transform themes into insight statements
  • propose prioritized actions based on insights
  • document research limitations and biases

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

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