AI-native product management skills for Claude Code — designed from the ground up for how PM actually works when AI is part of the team. Spend less time on artifacts and more time with stakeholders, users, and the decisions that shape what gets built.
What this project is not: A rehash of traditional PM playbooks with an AI label. Only skills optimized for AI-native speed belong here. If a framework assumes weeks of manual synthesis or months of waterfall planning, it doesn't belong in this project.
Compressed discovery cycle — frame, research, interview, map, decide
5 daysCraft a product vision, north star metric, and strategy-on-a-page
2-4 hoursLean build-measure-learn loop — hypothesis, cheapest test, validated learning
1-2 hoursDecision-forcing document to get stakeholders aligned fast
2-4 hoursTranslate strategy and OKRs into sequenced, audience-specific roadmaps
2-4 hoursMessaging, channels, enablement, and launch timeline
1-2 daysBreak epics into stories, model capacity, flag risks
1 hourSpot patterns, flag anomalies, build the data narrative
30 minutesAssess and respond to a competitor move fast
2-4 hoursValue-based pricing analysis, tier design, revenue modeling
1-2 daysDiagnose churn with cohort analysis and root cause identification
Half dayObjectives, key results, alignment, stress-test
2-3 hoursQuantify tech debt as a business investment case
2-4 hoursStructured, blameless post-mortem after a failure or incident
2-3 hoursEvaluate build in-house vs. vendor vs. partner with TCO analysis
Half dayPlan and execute interviews, usability tests, or surveys
1-3 daysPlan and communicate a feature deprecation or product end-of-life
Half dayProblem statement to builder-ready PRD with edge cases and readiness
1-2 hoursStrategic context, feature breakdown, milestones, and success metrics
1 hourDetailed requirements, edge cases, and integration points
30-60 minDeveloper-ready stories with testable Given/When/Then criteria
15-25 minDeep product data investigation, pattern analysis, and insight narratives
1-2 hoursDecompose large problems or epics into independently shippable slices
30-60 minROI, IRR, NPV, payback period, and cost-benefit with sensitivity modeling
2-4 hoursComplete business case with strategic rationale, financial model, and risk assessment
Half day"We need to validate whether this problem is worth solving."
Run a compressed 5-day discovery cycle instead of 3-8 weeks. Frame the problem, synthesize research, interview users, map opportunities, and reach a build/validate/kill decision.
"Stakeholders disagree and I need a decision by Friday."
Build a stakeholder map, draft a decision-forcing document, pre-empt objections, and produce a decision memo in a single focused session.
"We think this will work, but I want to test before we commit."
Map your riskiest assumptions, design the cheapest valid experiment, set ship/iterate/kill criteria, and make the call when data comes in.
"Leadership wants to know what we're building and why."
Translate your strategy and OKRs into a sequenced roadmap with three audience-specific views — team, leadership, and stakeholder.
"A competitor just launched something — what do we do?"
Assess the competitive move, evaluate response options, draft sales talking points, and prepare a leadership brief in 2-4 hours.
"Churn went up and nobody can tell me why."
Run a structured diagnostic with cohort analysis, root cause identification, and a prioritized retention intervention plan in half a day.
"This epic is too big — where do we even start?"
Decompose it into independently shippable slices using splitting patterns. Each slice has clear acceptance criteria and delivers user value on its own.
"Leadership wants ROI numbers before greenlighting this initiative."
Build a financial model with NPV, IRR, payback period, and sensitivity analysis. Package it into a complete business case with strategic rationale and risk assessment.