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 or draft proposal PRs

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.
• For autopilot mode, run a preflight checklist and list missing dependencies/secrets explicitly.
• If the user says "start/run the skill", do not ask generic discovery questions first. Run the startup protocol below.
• In start/run, never require workspace-local helper files under scripts/ or data/ as a hard prerequisite.

Required Inputs (Manual PM Mode Only)

• 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 (required baseline; use least-privilege fine-grained token)

Autopilot Preconditions (Mandatory)

Before running issue generation/autopilot mode, verify and report:

• Data sources:
  • analytics_summary.json (required)
  • revenuecat_summary.json (recommended for monetization decisions)
  • sentry_summary.json (recommended for stability prioritization)
  • feedback_summary.json (optional, but high value)
• Code-readiness:
  • --repo-root points to the target repository checkout
  • agent user has read access to the codebase
  • if needed, restrict scan with --code-roots apps,packages
• Runtime dependencies:
  • node for analyzer/runner
  • analyticscli CLI for analytics data extraction
  • analyticscli-cli skill must be installed/fetched (for canonical analytics source refresh workflow)
  • optional charting: python3 + matplotlib
• Secrets:
  • GITHUB_TOKEN (required baseline; fine-grained PAT with repository Issues: Read/Write, Contents: Read)
  • ANALYTICSCLI_READONLY_TOKEN (recommended; required for non-keychain CLI auth)
  • REVENUECAT_API_KEY
  • SENTRY_AUTH_TOKEN
• optional FEEDBACK_API_TOKEN
• optional connector-specific env vars used by sources.extra[]

If anything is missing, stop autopilot and return a concrete "missing items" list with where to obtain each value.

OpenClaw Startup Protocol (Mandatory)

When the user asks to start/run/kick off the skill, execute this exact sequence. This protocol must work even when the user prompt is vague and even when repo-specific helper scripts are missing.

0. ClawHub layout (only when scripts/openclaw-growth-start.mjs is missing at workspace root):

   • ClawHub installs skills under skills/<slug>/. If skills/product-manager-skill/scripts/openclaw-growth-start.mjs exists but scripts/openclaw-growth-start.mjs does not, run once from workspace root:
     • bash skills/product-manager-skill/scripts/bootstrap-openclaw-workspace.sh
   • Then the standard paths scripts/... and data/openclaw-growth-engineer/... exist at the workspace root for tools that expect them.

1. Start in portable mode first (always):

   • Ensure dependencies and auth without asking for manual analytics summaries:
     • check analyticscli binary (command -v analyticscli)
     • check analytics auth (analyticscli projects list with token or existing login)
     • check GITHUB_TOKEN presence (fine-grained token: repository Issues: Read/Write, Contents: Read)
     • detect GitHub repo from git remote origin if available; if not available, ask once for owner/repo
   • If any check fails, return only a concrete blocker checklist with exact fix commands.

2. Portable mode execution:

   • run first pass directly via analyticscli commands (bounded, deterministic)
   • generate 3-5 prioritized issue drafts and create GitHub issues or draft pull requests when allowed

3. After run:

   • report whether drafts were generated and whether GitHub issues or PRs were created
   • include command to repeat the same run path

Never block on "please provide goal + datasource" if config and sources already exist. Never fail only because local helper files are missing in the workspace. If config or runtime prerequisites are missing, return only a concrete missing-items checklist (config path, API keys, repo access, missing binaries/skills). Do not ask for manual data summaries in start/run mode.

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

Portable Start Commands

Dependency/auth checks:

command -v analyticscli
analyticscli projects list

Baseline analytics pull (bounded):

analyticscli schema events --limit 200 --last 30d --format json

Optional additional signals:

analyticscli timeseries --metric unique_users --interval 1d --last 30d --format json

References

• README
• Required Secrets
• Setup And Scheduling
• Input Schema
• Issue Template