discovery-interview-guide
This Claude Code skill generates a structured discovery interview guide that includes screener questions for participant recruitment, a narrative-driven discussion framework emphasizing past behavior over opinions, and a per-session synthesis template. Use it when preparing user interviews, customer discovery sessions, Jobs-to-be-Done research, or problem validation to ensure interviews surface genuine insights rather than validate existing assumptions.
git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skills /tmp/discovery-interview-guide && cp -r /tmp/discovery-interview-guide/plugins/pm-discovery/skills/discovery-interview-guide ~/.claude/skills/discovery-interview-guideSKILL.md
# Discovery Interview Guide Skill
Design interviews that surface genuine insight — not validation of what you already believe. Every guide follows a story-based, past-behaviour-focused structure.
## Core Principles
1. **Never ask about the future.** "Would you use X?" tells you nothing. "Tell me about the last time you did X" tells you everything.
2. **Interview for behaviour, not opinion.** Opinions are cheap. Behaviour is evidence.
3. **The 5 Whys.** Every surface answer is a door. Keep opening doors.
4. **Confirm the problem before exploring the solution.** Never show a prototype until you've confirmed the pain exists unprompted.
## Interview Structure (60 minutes standard)
### 1. Warm-Up (5 min)
Build rapport. Get them talking. Don't discuss the topic yet.
- "Tell me a bit about your role and what a typical week looks like for you."
- "What tools do you rely on most day-to-day?"
### 2. Context Setting (10 min)
Understand their world before diving into the problem space.
- "Walk me through how you currently [handle the domain area]."
- "What does that process look like from start to finish?"
- "Who else is involved when you do this?"
### 3. Problem Exploration (25 min) — THE CORE
Surface pain without leading.
- "Tell me about the last time you had to [relevant task]. What happened?"
- "What was the hardest part of that?"
- "How did you handle it?"
- "What did you try before settling on that approach?"
- "What does it cost you when this goes wrong?" (time, money, stress, reputation)
- "If you could wave a magic wand and change one thing about this process, what would it be?"
⚠️ **Do not mention your product or feature during this phase.**
### 4. Current Solutions (10 min)
Understand the competitive landscape from their perspective.
- "What tools or workarounds do you use today for this?"
- "What do you like about [current solution]? What frustrates you?"
- "Have you tried other approaches? What happened?"
### 5. Wrap-Up (10 min)
- "Is there anything about this topic we haven't covered that you think I should know?"
- "Is there anyone else you'd recommend I speak to?"
- "Would you be open to a follow-up if I have more questions?"
---
## Output Format
### Discovery Interview Guide — [Topic] — [Date]
**Research Goal:** [One sentence: what decision will this research inform?]
**Target Participant Profile:** [Role, company size, behaviour qualifier]
**Screener Questions** (for recruiting):
1. [Question] → Must answer: [Y/N or specific]
2. [Question] → Must answer: [Y/N or specific]
3. [Disqualifier question] → Disqualify if: [answer]
**Interview Guide:**
[Full structured guide using the format above, customised to the specific research topic]
**Synthesis Template** (fill after each interview):
- Key quote: "[verbatim]"
- Core pain: [1 sentence]
- Current workaround: [what they're doing today]
- Intensity (1–5): [how painful is this?]
- Surprise/unexpected finding: [anything that challenged your assumptions]
**Pattern Detection** (after 5+ interviews):
- Pain mentioned by [X/N] participants: [theme]
- Workaround used by [X/N] participants: [theme]
- Most emotionally charged moment in interviews: [observation]
---
## Required Inputs
Ask the user for these if not provided:
- **Research topic or question** (what decision will this inform?)
- **Target participant profile** (role, behaviour, company type)
- **Session length** (30 / 45 / 60 / 90 minutes)
- **Number of interviews planned**
- **Known hypotheses to test or avoid confirming prematurely** (optional)
## Quality Checks
- [ ] No future-tense questions ("would you...") — only past-behaviour questions
- [ ] Product or solution not mentioned until after pain is confirmed
- [ ] Questions open-ended (cannot be answered yes/no)
- [ ] Synthesis template included for per-session notes
- [ ] Screener questions identify and disqualify wrong participants
## Guidelines
- Recommend 5–8 interviews to reach thematic saturation for most discovery questions
- Always record with permission — transcripts beat notes
- If user is new to interviewing: remind them to stay silent after asking a question (aim for 80/20 participant-to-interviewer talking ratio)
- Never synthesise during the interview — do it after, when you can look across sessions
- Flag confirmation bias: if user writes questions that lead toward a predetermined answer, rewrite them as open-ended alternatives
## Anti-Patterns
- [ ] Do not use future-tense questions ("Would you use this?") — hypothetical responses do not predict real behaviour and produce false confidence in an idea
- [ ] Do not mention your product or solution before problem exploration is complete — doing so anchors the participant's responses and invalidates the discovery
- [ ] Do not synthesise across fewer than 5 interviews — themes from 2–3 interviews reflect anecdote, not pattern; wait for saturation
- [ ] Do not write screener questions that are too easy to pass — if participants can guess the "right" answer, you will recruit the wrong people
- [ ] Do not treat participant opinions as evidence of future behaviour — what people say they will do consistently diverges from what they actually doConduct a structured ethical review of an AI or ML feature, model, or product. Use when preparing to deploy an AI system, assessing algorithmic risk, auditing a model for bias, or producing a responsible AI impact assessment. Produces a structured ethics review covering fairness, transparency, privacy, safety, accountability, and societal impact with a risk tier score, pre-deployment checklist, and prioritised mitigations.
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