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pm-discovery

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Plan or critique a product discovery process — figuring out what to build before building it. Establishes which risk you're de-risking and how much certainty the decision needs, applies the always-true core (continuous not phased, outcomes over output, problem before solution, ask about past behavior, match evidence to risk, generate solutions from validated problems, research ethically) and gates the context-dependent decisions (problem vs. solution research, qual vs. quant, lightweight vs. deep, how much certainty before committing). Trigger when asked to plan discovery, "figure out what to build," set up continuous discovery, design a research plan, do customer/user research, find problems worth solving, or de-risk a product direction before committing.

pm/processes/discovery/SKILL.md

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Jul 2, 2026
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# 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*.

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