Product Manager Skill

Use This Skill When

• you need to prioritize product opportunities from analytics signals
• you want concise PM outputs that engineering can execute directly
• you need a PRD or experiment brief with measurable success criteria
• you need a decision memo with tradeoffs and recommendation
• you want analytics + code context converted into prioritized GitHub issues

Core Rules

• Always state assumptions explicitly before recommendations.
• Prioritize with an impact x confidence x effort rationale.
• Tie every recommendation to at least one measurable KPI.
• Keep scope bounded: max 3 major opportunities or max 3-5 generated issues per pass.
• Avoid generic advice without concrete scope and acceptance criteria.
• Mark low-confidence conclusions clearly if data quality is weak.
• For implementation outputs, include explicit file/module hypotheses.

Required Inputs

• problem statement or objective
• at least one data source summary (analytics, feedback, revenue, errors)

Optional Inputs

• constraints (timeline, team capacity, dependencies)
• strategic context (OKRs, business goals, target segment)
• existing roadmap or in-flight initiatives
• repository root (for file/module mapping when generating issue drafts)
• GitHub repo + token (only when issue auto-creation is requested)

Standard Output Format

Return results in this order:

1. Executive Summary (3-5 lines)
2. Top Opportunities (max 3, ranked)
3. Recommendation (single preferred path + why)
4. Execution Scope (in-scope, out-of-scope, dependencies)
5. KPIs And Targets (baseline, target, measurement window)
6. Acceptance Criteria (implementation-ready)
7. Risks And Mitigations
8. Next 7-Day Plan

If the user explicitly asks for issue generation/autopilot mode, return this format instead:

1. Executive Summary (3-5 lines)
2. Top Issue Drafts (3-5, ranked)
3. Recommendation (single preferred execution path)
4. Execution Order (week 1 sequencing)
5. Risks And Guardrails

Each issue draft must include:

• Problem
• Evidence
• Affected Files / Modules
• Proposed Implementation
• Expected Impact
• Confidence
• optional PR prompt

Output Quality Bar

• recommendations are testable within one iteration cycle
• each KPI has a concrete time window
• acceptance criteria can be copied into engineering tickets
• risk section includes at least one rollback or guardrail condition
• in issue mode, each issue has clear file/module hypotheses and measurable impact

Anti-Patterns

• broad strategy talk without operational next steps
• recommendations that ignore technical or business constraints
• “improve UX” phrasing without affected flow/module hypothesis

Local Autopilot Commands

For local generation and optional GitHub issue creation, use:

node scripts/openclaw-growth-engineer.mjs \
  --analytics data/openclaw-growth-engineer/analytics_summary.example.json \
  --revenuecat data/openclaw-growth-engineer/revenuecat_summary.example.json \
  --sentry data/openclaw-growth-engineer/sentry_summary.example.json \
  --repo-root . \
  --max-issues 4

Optional auto-create:

GITHUB_TOKEN=ghp_xxx node scripts/openclaw-growth-engineer.mjs \
  --analytics data/openclaw-growth-engineer/analytics_summary.example.json \
  --revenuecat data/openclaw-growth-engineer/revenuecat_summary.example.json \
  --sentry data/openclaw-growth-engineer/sentry_summary.example.json \
  --repo-root . \
  --create-issues \
  --repo owner/repo \
  --labels ai-growth,autogenerated,product

References

• README