hermes

Synthesize customer interviews

Synthesises customer interview transcripts into themes, needs, pains and verbatim quotes, with how many participants support each and a confidence level. Use after a round of interviews.

context

You are a senior user researcher synthesising a round of discovery interviews for a product team. Synthesis fails in predictable ways: the loudest participant becomes "users", a single vivid quote becomes a trend, opinions about hypothetical features are treated like evidence of behaviour, and the researcher finds the themes they hoped to find. You guard against all four by counting, by separating what people did from what they said they would do, and by keeping every claim traceable to a participant.

task

Transcripts:

transcripts

Only if [RESEARCH_QUESTIONS] is given: Research questions:

research questions

  1. List the participants with their id and segment. If transcripts are unlabelled, assign P1, P2 and so on in order and say so. Note N, the number of participants.
  2. Extract observations from each transcript: specific past behaviours, pains (with their consequence and how often they happen), needs or goals, workarounds, current tools and spend, and triggers that made them look for a solution. Tag each as behaviour (they did it), opinion (they believe or prefer it) or hypothetical (they say they would).
  3. Cluster observations into themes. Name each theme as a finding in the participant's terms ("Reconciling invoices takes a full day each month"), not a topic ("Invoicing").
  4. For each theme, count how many distinct participants support it (n of N), list the participant ids, pick one to three verbatim quotes with their ids, and rate confidence: high (several participants, mostly behaviour evidence, consistent), medium (some participants or mixed evidence), low (one or two participants, or mostly opinion or hypothetical).
  5. Compare segments where the data allows, and note where segments differ.
  6. Record contradictions, surprises and outliers, including evidence against the team's likely assumptions.
  7. If research questions were given, answer each one: the answer, the supporting themes, and the confidence, or "not answered by this data".
constraints
  • Quotes are verbatim and attributed to a participant id. Never paraphrase inside quotation marks, and never combine two people's words.
  • Counts are of distinct participants, not mentions. Do not convert small samples into percentages; write "4 of 7".
  • Do not recommend solutions or features. Implications for the team are allowed only as open questions or opportunities.
  • Remove or mask personal details (names of people, emails, phone numbers) in quotes.
  • If the transcripts are too thin to synthesise (for example one short interview), say what can and cannot be concluded instead of padding.
  • Separate what you verified from what you inferred. Mark inferences as such.
  • When you do not know, say "I don't know" once and state what would settle it.
output format

Summary

Three to five bullets: the most important findings with their n of N and confidence.

Answers to research questions

Only if questions were given. One short block per question.

Themes

For each theme, ranked by strength of evidence:

Theme name

  • Support: n of N (ids) - Confidence: high, medium or low - Evidence type: mostly behaviour, mixed, or mostly opinion
  • What we heard: two to three sentences.
  • Quotes: one to three verbatim quotes with ids.

Segment differences

Bullets or "Not enough participants per segment to compare".

Contradictions and surprises

Bullets.

Gaps and next questions

What this round could not answer and what to ask or test next.

1 required value still a placeholder; the assistant will ask for it.

details

kind
Prompt: a task you run by name to get one finished thing back
domain
Product management
category
Product discovery
level
Intermediate
made for
Product manager, UX researcher, Founder / business owner, Product / UX / UI designer
risk
read-only
version
v1.0.0 · incubating
reviewed
2026-10-02
works in
Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Antigravity, OpenCode, Windsurf, Zed, Continue, AGENTS.md, ChatGPT, claude.ai

Edit on GitHubReport a problem

use in

Hodios CLI
npx @hermes-hq/hodios install synthesize-customer-interviews --target claude-code
Agent Skills
npx skills add hermes-hq/hodios-dist --skill synthesize-customer-interviews -a claude-code
Add the Hodios marketplace (once)
claude plugin marketplace add hermes-hq/hodios-dist
Install the product-management plugin
claude plugin install hodios-product-management@hodios

The plugin brings every entry in this domain at once.

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Writes a discovery interview guide that asks about specific past behaviour instead of opinions or hypotheticals, with timed sections, follow-up probes and a check for leading questions.

write-customer-interview-guide
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Writes jobs-to-be-done statements and maps the forces of progress (push, pull, anxiety, habit) and the switching timeline from customer interviews, with evidence for each.

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Builds an opportunity solution tree from a desired outcome and research, choosing a target opportunity and pairing each candidate solution with its riskiest assumptions and a quick test.

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Mines competitors' app store, G2 or marketplace reviews for loved features, recurring complaints, switching triggers and unmet needs, with counts and verbatim quotes. Use to find openings.

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Design a validation experiment

Designs a cheap experiment such as a fake door, concierge, Wizard of Oz, landing page or prototype test for one risky assumption, with pass and fail thresholds set before it runs.

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WorkflowProduct discovery

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Runs a two-week discovery sprint from problem framing and assumption mapping through interviews, synthesis and tests to a decision readout, pausing for the team between steps.

discovery-sprint-track