Agent skill · product strategy · borghei
interview-synthesis
Customer interview synthesis: raw transcripts to themed insights, an opportunity solution tree, and follow-up questions (Teresa Torres continuous discovery). Use for post-interview synthesis, opportunity mapping, and evidence gap analysis.
Why this skill is useful
Adds a Python script for synthesizing customer interviews into actionable insights and opportunity maps, which the AI wouldn't reliably generate on its own.
What it needs
Requires python installed locally. About 3k tokens when loaded. Last updated 2026-07-21. 462 stars on the source repository.
What this skill does
Customer Interview Synthesis Expert Overview Turn customer interview transcripts into actionable product opportunities. This skill takes raw question-and-answer transcripts and produces three artifacts: (1) themed insight clusters, (2) an opportunity solution tree mapping outcomes to opportunities and candidate solutions, and (3) a prioritized list of follow-up questions to close evidence gaps. The synthesis approach is grounded in Teresa Torres' Continuous Discovery Habits (opportunity solution trees), Steve Portigal's interview methodology (looking for stories and contradictions), and the Jobs-To-Be-Done synthesis approach popularized by Alan Klement (situation-motivation-outcome decomposition). Core Capabilities Snippet extraction & coding — stories, contradictions, surprises, emotions coded by need/job/pain/gain and evidence strength Theme clustering — evidence thresholds (>=3 snippets from >=2 participants) with scannable headlines Opportunity solution tree — measurable outcome → customer-side opportunities → candidate solutions, with intact evidence trails Follow-up generation — story-prompt questions targeting weak-evidence themes and unmapped assumptions Multi-format output — markdown, JSON, mermaid, confluence, notion, linear When to Use Post-interview synthesis -- You have 3-20 interview transcripts and need to extract themes before they become stale. Opportunity space mapping -- Building an opportunity solution tree before committing to solutions. Discovery sprint readout -- Sharing findings with the product trio (PM, Design, Engineering) and stakeholders. Evidence gap analysis -- Identifying which assumptions still lack interview evidence and need targeted follow-ups. When NOT to Use Quantitative survey synthesis -- use a data analysis skill instead. Usability test debriefs -- use a UX research-specific workflow. Sales call analysis for win/loss -- use business-growth/ skills. Clarify First Before synthesizing, confirm these inputs. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills borghei/interview-synthesis