{"id":"84ac039e-c158-435f-b77d-6c66cf28d920","shortId":"FqNFqP","kind":"skill","title":"metric-dashboard","tagline":"Design metric dashboards and KPI tracking plans for products and features. Defines what to measure, how to measure it, alert thresholds, and dashboard layout. Covers product, business, and technical metrics.","description":"# Metric Dashboard Skill\n\nDesign a comprehensive metric dashboard and KPI tracking plan for any product or feature.\n\n## When to Use\n- User needs to define metrics for a new product or feature\n- User is setting up monitoring and alerting\n- User needs to design a dashboard layout\n- User says `/metric-dashboard` followed by the product/feature\n- Any time measurement strategy needs to be defined\n\n## Framework: Metric Dashboard Design (5 Steps)\n\n### Step 1: Define the Metric Hierarchy\n\n**North Star Metric (NSM)**:\nThe single metric that best captures the value your product delivers.\n- Must reflect user value, not just business value\n- Must be measurable with current instrumentation\n- Formula: NSM = [engagement unit] per [user segment] per [time period]\n\n**Decompose into a metric tree:**\n```\nNorth Star Metric\n├── Input Metric A (e.g., new users)\n│   ├── Sub-metric A1\n│   └── Sub-metric A2\n├── Input Metric B (e.g., activation rate)\n│   ├── Sub-metric B1\n│   └── Sub-metric B2\n└── Input Metric C (e.g., retention)\n    ├── Sub-metric C1\n    └── Sub-metric C2\n```\n\n### Step 2: Categorize Metrics\n\n**Product Metrics:**\n- Acquisition: How users find you (sign-ups, installs, registrations)\n- Activation: First value moment (onboarding completion, first action)\n- Engagement: Core usage (DAU/MAU, session length, feature adoption)\n- Retention: Coming back (D1/D7/D30, cohort retention curves)\n- Revenue: Monetization (ARPU, conversion, LTV, churn)\n\n**Technical Metrics:**\n- Performance: Latency (p50, p95, p99), throughput, error rate\n- Reliability: Uptime, incident count, MTTR\n- Infrastructure: CPU/memory utilization, cost per request\n\n**AI/ML Metrics (if applicable):**\n- Quality: Accuracy, hallucination rate, eval scores\n- Safety: Content policy violation rate, false refusal rate\n- Cost: Cost per inference, token usage\n- Latency: Time to first token, tokens per second\n\n**Business Metrics:**\n- Revenue: MRR, ARR, revenue growth rate\n- Unit economics: CAC, LTV, LTV/CAC ratio\n- Market: Market share, competitive win rate\n\n### Step 3: Set Targets & Alerts\n\nFor each metric, define:\n\n| Metric | Current | Target | Alert Threshold | Owner |\n|--------|---------|--------|----------------|-------|\n| NSM | X | Y | Z | PM |\n| Metric A | | | | |\n| Metric B | | | | |\n\n**Alert levels:**\n- **Warning** (yellow): Metric trending below target — investigate\n- **Critical** (red): Metric below threshold — immediate action required\n- **Anomaly**: Unexpected spike or drop — auto-detect and notify\n\n### Step 4: Design Dashboard Layout\n\n**Executive Dashboard** (1 screen):\n- NSM trend (last 30/90 days) — large, prominent\n- 4-6 key metrics with sparklines and trend arrows\n- Traffic light status (green/yellow/red) for each area\n- Notable events annotated on the timeline\n\n**Operational Dashboard** (detailed):\n- Real-time metrics for the current day/hour\n- Breakdowns by segment (platform, geography, user type)\n- Funnel visualization (acquisition → activation → retention)\n- Experiment results (A/B test outcomes)\n\n**Technical Dashboard** (if applicable):\n- System health (latency, error rate, uptime)\n- Model performance (eval scores, cost, throughput)\n- Infrastructure utilization and cost\n\n### Step 5: Measurement Plan\n\nFor each metric, document:\n- **Definition**: Exact formula, including/excluding criteria\n- **Data source**: Which event, table, or API\n- **Instrumentation**: What needs to be logged/tracked\n- **Granularity**: How often updated (real-time, hourly, daily)\n- **Segments**: Key breakdowns (platform, country, user tier)\n- **Owner**: Who monitors this metric\n\n## Output Format\nGenerate a complete metric plan in markdown with:\n1. Metric hierarchy (tree diagram)\n2. Metric definitions table\n3. Targets and alert thresholds\n4. Dashboard layout description\n5. Measurement plan\n\n## Common Pitfalls to Avoid\n- **Vanity metrics**: Big numbers that don't reflect value (total sign-ups vs. active users)\n- **Too many metrics**: 5-8 key metrics max on the exec dashboard\n- **No baselines**: Always show current state before setting targets\n- **Missing guardrails**: Every optimization metric needs a counter-metric\n- **No segmentation**: Averages hide problems — always break down by 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