analytics-insights
Deep dive into product analytics — investigate a question, surface insights, build a data narrative. Use when you need to go beyond dashboards to understand…
Write a complete PRD from problem statement to builder-ready spec. Use when you need a formal product requirements document for a new initiative.
$ npx -y skills add mrthames/lean-pm-skills --skill prd-writing --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/prd-writingContext preview
The summary Claude sees to decide when to auto-load this skill.
Write a complete PRD from problem statement to builder-ready spec. Use when you need a formal product requirements document for a new initiative.
name: prd-writing description: Write a complete PRD from problem statement to builder-ready spec. Use when you need a formal product requirements document for a new initiative.
Turn a problem statement into a production-ready PRD in 1-2 hours instead of days. Claude handles the structure, boilerplate, edge case enumeration, and formatting. You provide the problem context, strategic framing, and the judgment calls about scope and trade-offs.
| Step | Time | Claude Does | You Do | |---|---|---|---| | Frame the problem | 15 min | Structure problem statement from your raw input | Validate the framing, add evidence | | Define success | 15 min | Propose metrics, baselines, and evaluation timeline | Confirm what "done" actually means | | Scope the solution | 20 min | Draft v1/deferred/out-of-scope with trade-off rationale | Make the cut calls | | Detail requirements | 30 min | Enumerate edge cases, error states, technical constraints | Validate with engineering | | Assemble the PRD | 15 min | Format the complete document with all sections | Review and distribute |
A PRD without a clear problem statement is a solution looking for a justification. Start here.
I need a PRD for [initiative]. Here's what I know: The problem: - Who is affected: [user segment] - What they experience: [the pain in their words] - How we know this: [evidence — data, support tickets, research, user quotes] - What happens if we don't solve it: [business/user consequence] Context: - Strategic alignment: [which company goal or OKR this supports] - Prior attempts: [what's been tried before, if anything] - Urgency: [why now — is there a deadline, competitive pressure, or growing pain?] Help me frame this as a crisp problem statement. Challenge me if the evidence is thin.
For this PRD, help me define success metrics: Primary metric: - What outcome are we trying to change? [metric] - Current baseline: [number + source] - Target: [number + rationale for why this target] - Evaluation timeline: [when we assess — e.g., 30 days post-launch] Secondary metrics: - Supporting signals that indicate we're on track Guardrail metrics: - What must NOT degrade as a result of this change - Thresholds for each guardrail If we can't measure any of these today, flag it — we may need to instrument first.
This is where lean discipline matters most. The default is to over-scope. Fight it.
Based on the problem and success metrics, help me scope the solution: v1 — Ship this (minimum that tests the hypothesis): - [Capability 1] - [Capability 2] - [Capability 3] For each: why is it in v1? What would break if we cut it? Deferred — Validated by v1 results: - [Item] — ship if [trigger condition] Out of scope — Explicitly not included: - [Item] — why it's out (not "later," but "not this") Questions to pressure-test scope: - What's the smallest version that moves the primary metric? - Are we bundling nice-to-haves with must-haves? - If we had half the engineering time, what would we cut?
This is where AI thoroughness adds the most value — catching the edge cases humans skip.
For the scoped solution, detail the requirements: User stories: - Draft user stories for each v1 capability with acceptance criteria - Make every acceptance criterion testable Edge cases and error states: - What happens when [input is empty / invalid / extremely large]? - What happens when [the user doesn't have permission]? - What happens when [a dependency is unavailable]? - What happens when [the user abandons mid-flow]? Technical constraints: - Platform/infrastructure constraints - API contracts or integration requirements - Performance requirements (latency, throughput, availability) - Data and privacy requirements - Accessibility requirements Dependencies: - What do we need from other teams, and by when? - What are other teams waiting on from us? Risks: - What could go wrong? Likelihood, impact, and mitigation for each.
Assemble the complete PRD with these sections: 1. Problem Statement (who, what, evidence, impact) 2. Success Metrics (primary, secondary, guardrail, evaluation timeline) 3. Solution — Scope (v1, deferred, out of scope) 4. User Stories with Acceptance Criteria 5. Technical Constraints 6. Dependencies 7. Risks 8. Readiness Requirements (support FAQ, help docs, release notes, monitoring, rollback) Format for [our documentation system — e.g., Confluence, Notion, Google Docs]. Include status field, author, engineering lead, design lead, and last updated date.
1. **Skipping the problem statement.** "We need dark mode" is not a problem statement. "Users report eye strain during evening use (34% of exit survey respondents)" is. The problem statement is the most important secti
AI-native product management skills that move at the speed your team needs. Traditional PM frameworks were built for a world without AI — multi-week discovery sprints, day-long planning offsites, months of analysis before a decision.
Deep dive into product analytics — investigate a question, surface insights, build a data narrative. Use when you need to go beyond dashboards to understand…
Evaluate whether to build in-house, buy a vendor solution, or partner. Use when facing a make-or-buy decision for a capability.
Build a complete business case for a product investment — strategic rationale, financial model, risk assessment, and recommendation. Use when you need…
Assess and respond to a competitor move within 24-48 hours. Use when a competitor launches, changes pricing, or makes a strategic shift.
Run a compressed 5-day product discovery cycle. Use when validating whether a problem is worth solving before committing engineering resources.
Define an epic with strategic context, feature breakdown, milestones, and success metrics. Use when scoping a large body of work for planning and tracking.