bosskuai-customer-discovery · diff
git:20260716.6355db6 to git:20260813.53fd4ac
72 added, 19 removed. Audit A to A.
---
name: bosskuai-customer-discovery
description: Use this skill for user interview planning, customer discovery, survey design, interview transcript analysis, and persona building from primary research. Gathers and structures the evidence that product strategy acts on.
---
# BosskuAI Customer Discovery
- Use this skill for user interview planning, customer discovery, survey design, interview transcript analysis, and persona building from primary research. Gathers and structures the evidence that product strategy acts on.
+ ## When to use
- ## Fast Path
+ - Planning user interviews (who to recruit, what to ask)
+ - Designing a discovery survey or screener
+ - Analysing interview transcripts for patterns
+ - Building evidence-backed personas from primary research
+ - Validating or invalidating a product hypothesis with real users
+ - Extracting Jobs-to-be-Done (JTBD) from qualitative data
+ - Synthesising community feedback (Reddit, reviews, forums) into structured insight
- 1. Confirm the requested outcome and constraints.
- 2. Use the smallest checklist needed; do not load the full playbook by default.
- 3. Produce the artifact, review, or decision in the user-requested format.
- 4. State verification performed and any remaining risk.
+ ## How this differs from nearby skills
- ## When To Open The Playbook
+ - **`bosskuai-product-strategy`**: synthesises all evidence (customer, market, competitive) into what to build and why. Customer discovery gathers primary evidence from users — it is the input, not the conclusion.
+ - **`bosskuai-market-analysis`**: works with secondary market data (analyst reports, search volume, public signals). Customer discovery works with primary data — direct user conversations and first-hand observations.
+ - **`bosskuai-deep-research`**: broad multi-source investigation. Customer discovery is specifically about understanding specific users and their behaviours, motivations, and unmet needs.
- Open `../../references/playbooks/bosskuai-customer-discovery-playbook.md` only when the task needs detailed framework choices, longer checklists, examples, or implementation depth.
+ ## MCP requirements
- ## Default Output
+ | Tool | Role | Degradation |
+ |------|------|-------------|
+ | Exa | Finding relevant communities, forums, review sites (optional) | Without Exa, rely on user-supplied transcript or survey data |
+ | Firecrawl | Scraping review sites (G2, Capterra, Reddit threads) for existing signal (optional) | Without Firecrawl, manually paste review excerpts |
- - Start with the answer or changed recommendation.
- - Use concise bullets for tradeoffs.
- - Avoid generic AI/SaaS phrasing.
- - For implementation work, include exact files, commands, tests, or review notes.
+ ## Workflow
- ## Verification
+ 1. **Define the hypothesis to test** — State exactly what belief you are trying to validate or invalidate. Example: "B2B ops managers spend 3+ hours/week on manual reporting and would pay to automate it." Be specific about the segment and the behaviour.
- Before finalizing, check:
+ 2. **Identify the target segment** — Define: company size, role/title, industry, geography, and any qualifying behaviour (e.g., "currently using spreadsheets for X"). A too-broad segment produces unfocused findings.
- - Did the output solve the actual request?
- - Are assumptions and risks visible?
- - Is there a concrete next action?
- - Did we avoid loading unnecessary specialist context?
+ 3. **Design the interview script** — Apply JTBD principles:
+ - Open with context questions (tell me about your role, your team, a typical week).
+ - Focus on past behaviour, not hypothetical future behaviour ("tell me about the last time you...").
+ - Avoid leading questions ("do you find X frustrating?" → "walk me through how you handle X").
+ - Use the "five whys" to surface root motivations.
+ - End with: "Is there anything I didn't ask that you think is important?"
+ - Target 45–60 minutes per session; 8–12 interviews per segment to reach pattern saturation.
+
+ 4. **Analyse transcripts for patterns** — For each transcript: tag quotes by theme. After all interviews: count theme frequency, note intensity (mild mention vs. strong emotion), and flag outliers. Look for: recurring pain triggers, existing workarounds, decision criteria, switching triggers.
+
+ 5. **Synthesise into persona and insight cards** — For each distinct user type identified:
+ - Persona card: role, context, primary JTBD, top 3 pains, top 3 goals, current solutions.
+ - Insight cards: finding + evidence count + confidence level + product implication.
+
+ 6. **Map findings to product implications** — For each validated insight: state the specific product or positioning decision it informs. Separate observations (what users said/did) from interpretations (what it means).
+
+ ## Output format
+
+ ### Persona card
+ ```
+ ## Persona: [Name / Archetype]
+ - Role and context: ...
+ - Primary JTBD: "When I [situation], I want to [motivation] so I can [outcome]."
+ - Top pains: (1) ... (2) ... (3) ...
+ - Top goals: (1) ... (2) ... (3) ...
+ - Current solutions / workarounds: ...
+ - Quote: "..."
+ ```
+
+ ### Insight matrix
+ | Finding | Evidence count | Confidence | Product implication |
+ |---------|---------------|------------|---------------------|
+ | Users manually export data to Excel weekly | 7/10 interviews | HIGH | Automate export / native reporting is table stakes |
+ | Price sensitivity below $50/user/month | 4/10, mixed intensity | MEDIUM | Test pricing at $39 before $59 |
+
+ ### Interview script template
+ Included in the playbook.
+
+ ## Guardrails
+
+ - Never treat n=1 as validated — one enthusiastic user is not a pattern.
+ - Separate observation (verbatim quote, described behaviour) from interpretation (your conclusion) at all times.
+ - Label sample size on every finding — "6 of 9 interviews" not "most users."
+ - Minimum 5 interviews before drawing pattern-level conclusions; 8–12 for a new segment.
+ - Avoid confirmation bias — actively look for disconfirming evidence.
+ - Do not lead the interview toward your hypothesis — test it, don't sell it.
+ - Synthesised personas must map to actual interview data, not archetypes invented without evidence.
+ - If scraping community forums, distinguish aggregated public signals from direct interview evidence — they have different confidence weights.
+
+ ## Further reading
+
+ - `../../references/playbooks/customer-discovery-detailed-playbook.md` — extended step-by-step workflow and detailed templates that complement this playbook.