User Interview Insight Synthesizer

Synthesizing user research shouldn't feel like guessing. This skill processes interview transcripts and researcher notes into evidence-backed findings while maintaining strict research integrity. It eliminates the manual drudgery of tag-and-cluster workflows by organizing raw data into structured insights without losing the original context or verbatim quotes.

What it does

• Evidence-linked synthesis connects every finding to specific participant labels and transcript locations.
• Pain point mapping breaks down frustrations by context, consequence, and existing workarounds.
• Verbatim extraction captures quotes exactly as spoken to preserve the user's voice for stakeholders.
• Conflict detection highlights contradictions and outliers instead of forcing a false consensus.
• Gap identification flags missing metadata, ambiguous evidence, and unanswered research questions.

How it works

1. Supply raw transcripts, interview notes, or meeting excerpts along with your research objectives.
2. The skill audits the dataset to confirm participant counts and identifies any gaps in metadata.
3. It clusters evidence into themes, separating explicit feature requests from underlying needs.
4. You receive a structured readout including evidence tables, pain point analysis, and suggested next steps.

Frameworks & tools

Works with any text-based UX research data including transcripts from Zoom, Otter.ai, or Gong, and notes from Notion, Dovetail, or FigJam.

Why this beats prompting it yourself

Generic prompts often "hallucinate" user needs or smooth over important contradictions to provide a clean summary. This skill is hard-coded to preserve research integrity, maintain verbatim accuracy, and explicitly flag when evidence is too weak to support a conclusion.

Use cases

• Analyzing a week of discovery calls to build a prioritized product roadmap.
• Extracting specific user workarounds for a complex B2B workflow audit.
• Identifying recurring usability hurdles from a round of prototype testing.
• Summarizing stakeholder interviews to align project requirements.

Known limitations

Requires text-based transcripts or notes as input. It cannot process raw audio or video files directly. Results are limited by the quality and depth of the provided documentation.