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discovery-interview

When running discovery interviews — question-bank build, bias audit, insight extraction. Triggers on 'audit my guide', 'extract insights from transcript', 'is my hypothesis falsifiable'.

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discovery-interview

When to use

  • A discovery slice has been framed (customer-research ran), but the interview guide is still rough or untested.
  • A transcript exists and the team needs structured insight extraction, not narrative summary.
  • An interview round produced surprising findings; a bias audit is needed before the team acts on them.

Do NOT use for the upstream framing of the discovery slice (frame sentence, recruit criteria, JTBD focal job) — that is customer-research. Do NOT use for quantitative survey design or scale-bound research.

Cognition cluster

  • Mental model 22 — Data-informed, not data-driven. Interview data is signal at low N; treat it as evidence to reason with, not a vote count. See docs/contracts/mental-models.md § 22.
  • Mental model 15 — Signal vs noise. A vivid quote from one articulate user can swamp three muted but consistent signals; frequency-rank by distinct people, never by quote count. See mental-models.md § 15.
  • Mental model 28 — Eisenhower matrix. Sort post-interview insights into urgent / important quadrants so the team acts on high-importance signals, not the loudest ones. See mental-models.md § 28.
  • Product context-spine slot. Read product for the focal job
    • competitor names; do not re-derive these inside this skill. See context-spine.

Procedure

1. Build the question bank

  1. Anchor on the switch event from customer-research. The first question is always "Walk me through the day you decided to ..." — never "would you ...".
  2. Three layers:
    • Past behaviour (what did you do? when? alternative considered?)
    • Anxiety / habit (what feared in switching? what habit died?)
    • Outcome (what changed for you? expected? unexpected?)
  3. Cap at 8 open questions per 45-min slot. Beyond that, the interview becomes a survey delivered in person.

2. Audit the bank for bias

Before running, inspect each question and review the bank against the four common biases:

  • Leading — "Don't you think X is annoying?" → rewrite as past behaviour.
  • Hypothetical — "Would you use Y?" → replace with "Last time you needed Y, what did you do?".
  • Confirmation — every question presupposes the team's hypothesis is correct. At least two questions must be able to disconfirm it.
  • Recall ceiling — questions that ask for events ≥ 90 days back produce confabulation; bound the timeframe.

A bank that survives the audit unchanged is suspect — re-read.

3. Run-time discipline

  1. Open with the switch event. Stay silent for 8 seconds after the user finishes; the second answer is usually the truer one.
  2. Capture verbatim, not paraphrase. "It made me anxious" is data; "the user expressed concern" is interpretation.
  3. Probe with "tell me more about X" on any anxiety / habit mention; do not switch topics until the thread is exhausted.

4. Extract insights

For each transcript:

  1. One quote per insight. Tag: switch / anxiety / habit / expected-outcome / unexpected-outcome / disconfirmation.
  2. Frequency-rank by distinct interviewees (≥ 3 = signal; 1 = anecdote).
  3. Mark disconfirmations explicitly — these are the most valuable rows, because they are the cheapest to ignore.

5. Hand back

Produce the three artifacts (see ## Output); hand the disconfirmation log to whoever owns the original hypothesis.

Related Skills

WHEN to use this

  • The discovery slice is framed and the interview guide / transcript is the unit of work.
  • A round of interviews ran and the insights need structured extraction (frequency-ranked, bias-audited, disconfirmations highlighted).

WHEN NOT to use this

  • The slice itself is unframed — start with customer-research; this skill inherits its frame, never re-derives it.
  • The signal needs to come from existing artefacts (issues, PRs, errors) rather than a live interview — route to voc-extract.
  • Insights translate into AC for a ticket — hand off to refine-ticket.
  • The output is a quantitative funnel — route to funnel-analysis.

When the agent should load this

  • "Hilf mir den Interview-Leitfaden auditieren."
  • "Welche Fragen sind biased?"
  • "Extract die Insights aus diesem Transkript."
  • "Wir haben 6 Interviews geführt — was ist Signal, was ist Anekdote?"
  • "Ist meine Hypothese widerlegbar mit dem aktuellen Frageset?"

Output

  1. question-bank.md — 8 open questions, each tagged past-behaviour / anxiety-habit / outcome; bias-audit notes per rewritten question.
  2. insight-log.md — one row per insight: quote · interviewee ID · tag · distinct-people frequency. Sorted descending. Verbatim.
  3. disconfirmation-log.md — each row names the original hypothesis, the interview-derived disconfirmation, and the named owner who must respond before the team acts on the round.

Gotcha

  • A bank that survived the audit unchanged is rare; usually means the audit was rushed, not that the bank was perfect.
  • One articulate interviewee biases insight-extraction toward their vocabulary — frequency-rank by people, not quotes.
  • Disconfirmations are the cheapest insight to ignore and the most valuable to act on; the log exists so they survive the round.

Do NOT

  • Do NOT re-derive the frame inside this skill — read the product spine slot or hand back to customer-research.
  • Do NOT translate insights into AC inside this skill — that is refine-ticket.
  • Do NOT collapse disconfirmations into "we also heard X" prose; they earn their own log.

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

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