liqiongyu/lenny_skills_plus/product-taste-intuition

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Build product taste via a Taste Calibration Sprint (benchmarks, critique notes, hypothesis log).

Install

npx vskill@latest install liqiongyu/lenny_skills_plus/product-taste-intuition

SKILL.md

Product Taste & Intuition

Scope

Covers

  • Developing product taste (what "good" looks like) through deliberate exposure, observation, and critique
  • Using intuition as a hypothesis generator (turning "gut feel" into testable hypotheses)
  • Building a repeatable practice loop (exposure hours → analysis → validation → updated taste rules)

When to use

  • "Help me improve my product taste / product sense."
  • "Calibrate what ‘good onboarding’ looks like for our product category."
  • "Turn my intuition about this flow into testable hypotheses."
  • "Create a structured way to study great products and extract patterns."

When NOT to use

  • You need to decide what to build (use problem-definition, prioritizing-roadmap, or defining-product-vision).
  • You need user evidence first (use conducting-user-interviews or usability-testing).
  • You want aesthetic critique only (this is product experience: value, UX, clarity, trust, speed--not just visuals).
  • You can’t name any target user, use case, or the "taste domain" you want to improve (we’ll narrow first).
  • You want to run a structured design review of your own product’s UI/UX (use running-design-reviews).
  • You want to dogfood your own product and capture feedback (use dogfooding).
  • You want to develop a PM’s skills through coaching conversations (use coaching-pms).
  • You need a competitive landscape analysis or market positioning (use competitive-analysis).

Inputs

Minimum required

  • Taste domain to improve (pick 1): onboarding, activation, navigation/IA, editor/workflow, pricing/packaging UX, notifications, retention loops, trust/safety, performance/latency feel, copy/voice
  • Target user + top job-to-be-done for that domain
  • 3–10 benchmark products/experiences to study (or "unknown—please propose")
  • Time box (e.g., 60–120 min sprint; or a 2–4 week practice plan)
  • Constraints (platform, geography, accessibility, compliance, brand voice, etc.)

Missing-info strategy

  • Ask up to 5 questions from references/INTAKE.md.
  • If inputs remain missing, proceed with explicit assumptions and provide 2 scope options (narrow vs broad).

Outputs (deliverables)

Produce a Taste Calibration Pack (in-chat Markdown; or as files if requested):

1) Taste Calibration Brief (domain, target user/job, what "good" means, constraints) 2) Benchmark Set (5–10 products) + "why these" + what to study 3) Product Study Notes (1 page per benchmark) using a consistent critique template 4) Taste Rules + Anti-Patterns (do/don’t rules derived from evidence) 5) Intuition → Hypothesis Log (testable hypotheses + predicted signals) 6) Validation Plan (qual + quant checks; smallest viable tests) 7) Practice Plan (2–4 weeks: exposure hours + weekly synthesis cadence) 8) Risks / Open questions / Next steps (always included)

Templates: references/TEMPLATES.md

Workflow (8 steps)

1) Intake + pick the taste domain (narrow the problem)
  • Inputs: User context; references/INTAKE.md.
  • Actions: Choose 1 taste domain and 1 "moment" (e.g., first-run onboarding). Define target user + job + constraints. Set time box.
  • Outputs: Taste Calibration Brief (draft).
  • Checks: A stakeholder can answer: "What specific experience are we calibrating taste for?"
2) Define "good taste" as decision criteria (not vibes)
  • Inputs: Domain + user/job.
  • Actions: Draft 6–10 criteria (e.g., clarity, time-to-value, trust, agency, error recovery, perceived speed, cognitive load). Add explicit tradeoffs (what you’ll sacrifice).
  • Outputs: Criteria list + tradeoffs section in the brief.
  • Checks: Criteria are observable in-product (you can point to UI/behavior), not generic adjectives.
3) Build the benchmark set (exposure hours, curated)
  • Inputs: Known benchmarks (or none).
  • Actions: Select 5–10 exemplars (direct, adjacent, and at least 1 "gold standard"). For each: what you’re studying and why it’s relevant.
  • Outputs: Benchmark Set table.
  • Checks: Set includes at least 2 "outside the category" references to avoid local maxima.
4) Study like a voracious user (structured observation)
  • Inputs: Benchmarks; critique template.
  • Actions: Use each product as the target user. Capture micro-moments: friction, delight, confusion, trust breaks. Record "what happened" before "why it’s good/bad".
  • Outputs: Product Study Notes (draft).
  • Checks: Each benchmark note includes at least 3 concrete moments with screenshots/quotes if available (or precise descriptions).
5) Synthesize: turn observations into taste rules + anti-patterns
  • Inputs: Study notes across benchmarks.
  • Actions: Cluster patterns. Convert into rules: DO/DO NOT, plus rationale and where it applies. Add anti-patterns that create "AI slop" (generic, incoherent, misaligned experiences).
  • Outputs: Taste Rules + Anti-Patterns.
  • Checks: Each rule is backed by ≥ 2 observations from different benchmarks (or explicitly marked "hypothesis").
6) Intuition as hypothesis generator (make it testable)
  • Inputs: Rules + your gut reactions.
  • Actions: Write intuition statements ("It feels off because…") and convert into testable hypotheses with predicted signals and counter-signals.
  • Outputs: Intuition → Hypothesis Log.
  • Checks: Each hypothesis has a clear falsification condition ("If X doesn’t change after Y, we were wrong.").
7) Validate with smallest viable checks (qual + quant)
  • Inputs: Hypothesis log; available data/research access.
  • Actions: Choose the lightest validation per hypothesis: usability task, intercept prompt, session replay review, funnel slice, A/B smoke test, copy test, etc. Define success metrics and sample.
  • Outputs: Validation Plan with owners/cadence if known.
  • Checks: Validation steps are feasible within the stated time box and don’t require sensitive data.
8) Create a practice loop + quality gate + finalize
  • Inputs: Draft pack.
  • Actions: Build a 2–4 week practice plan (exposure hours schedule + weekly synthesis). Run references/CHECKLISTS.md and score with references/RUBRIC.md. Add Risks/Open questions/Next steps.
  • Outputs: Final Taste Calibration Pack.
  • Checks: A reader can follow the practice plan without additional context; assumptions are explicit.

Quality gate (required)

Examples

Example 1 (Onboarding): "Calibrate our onboarding taste vs best-in-class. Target users are first-time PMs. Time box: 90 minutes. Output a Taste Calibration Pack." Expected: benchmark set, critique notes, taste rules, hypotheses, and a lightweight validation plan.

Example 2 (B2B workflow UX): "My gut says our ‘create project’ flow feels slow and confusing. Turn that into testable hypotheses and a validation plan." Expected: intuition→hypothesis log with falsification conditions and smallest viable checks.

Boundary example: "Tell me what good taste is in general." Response: require a specific domain + target user/job; otherwise produce a menu of domain options and propose a narrow starting point.

Boundary example 2: "Review our product's dashboard design and tell me what's wrong." Response: critiquing your own product's specific design is a design review, not taste calibration. Use running-design-reviews for structured UI/UX critique, or dogfooding to capture user-perspective feedback.

Anti-patterns (common failure modes)

  1. Benchmark tourism: Skimming 10 products in 20 minutes without structured observation. Taste calibration requires deep, moment-by-moment study, not surface-level impressions.
  2. Opinion without observation: Writing critique notes that say "this feels good" without recording specific micro-moments (friction, delight, confusion, trust breaks). Observations come before interpretations.
  3. Local maxima benchmarking: Only studying products in your exact category. Including 2+ "outside the category" references prevents copying competitors and reveals transferable patterns.
  4. Unfalsifiable taste rules: Deriving rules like "the UX should be intuitive" with no way to test or disprove them. Every taste rule should convert to a testable hypothesis with a clear falsification condition.
  5. One-off sprint with no practice loop: Running a single calibration session and calling it done. Taste is built through repeated exposure; the practice plan must include a weekly synthesis cadence over 2-4 weeks.

Security

  • Security scanPassed, 100 of 100Pattern checks on the skill's files.
  • Model reviewNot runA model review is scheduled for the next rescan.

Quality evaluation

  • Task evaluationNot runNo evaluation yet. Run one in Skill Studio.

Source

Popularity

Repository stars
52
Repository forks
8
7-day trend score
20

Stars and forks are for the whole GitHub repository, not this skill alone. Refreshed 24 Mar 2026, 7 months ago.

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