Product Discovery Skill

How this skill behaves (read first)

This is a generative skill, and "do some discovery" hides several default traps. The instinctive moves are: treat discovery as a one-time research phase before building (then never revisit it), jump straight to solutions and features, run a generic "interview a few users + send a survey" plan without matching the rigor to what's actually at stake, measure success by what shipped rather than what changed, and ask users "would you use this?" (everyone says yes). The result is a feature factory that's busy but not learning. Good discovery is continuous, problem-first, outcome-oriented, and evidence-matched-to-risk. So this skill gates:

1. Establish which risk you're de-risking and how much certainty the decision needs — value, usability, feasibility, or business-viability risk; a cheap reversible call needs far less evidence than an expensive irreversible bet.
2. Apply the always-true core — continuous cadence, outcomes over output, problem before solution, past behavior over hypotheticals, evidence matched to risk, solutions from validated problems, ethical research.
3. Surface the context-dependent decisions (problem vs. solution research, qual vs. quant, lightweight vs. deep, DIY vs. specialist, how much certainty before committing) with trade-offs.

Then it hands off to pm-assumption-rigor-audit, pm-okr-metric-validity-audit, and pm-prioritization-rigor-audit for validation. The actual test/experiment design for a specific assumption belongs to the generative pm-assumption-testing skill — discovery decides what to learn and how much it matters; assumption-testing designs the test.

Step 0 — Establish context before planning discovery

Ask if not known; state the assumption if proceeding without an answer: