# Metric Dashboard

> 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.

- Skill: `aroyburman-codes/metric-dashboard` (Agent Skill)
- Install (CLI): `npx skillmds@latest add aroyburman-codes/metric-dashboard`
- Raw SKILL.md: https://api.skillmd.com/api/skills/aroyburman-codes/metric-dashboard/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: aroyburman-codes (https://skillmd.com/u/aroyburman-codes)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/aroyburman-codes/metric-dashboard

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# Metric Dashboard Skill

Design a comprehensive metric dashboard and KPI tracking plan for any product or feature.

## When to Use
- User needs to define metrics for a new product or feature
- User is setting up monitoring and alerting
- User needs to design a dashboard layout
- User says `/metric-dashboard` followed by the product/feature
- Any time measurement strategy needs to be defined

## Framework: Metric Dashboard Design (5 Steps)

### Step 1: Define the Metric Hierarchy

**North Star Metric (NSM)**:
The single metric that best captures the value your product delivers.
- Must reflect user value, not just business value
- Must be measurable with current instrumentation
- Formula: NSM = [engagement unit] per [user segment] per [time period]

**Decompose into a metric tree:**
```
North Star Metric
├── Input Metric A (e.g., new users)
│   ├── Sub-metric A1
│   └── Sub-metric A2
├── Input Metric B (e.g., activation rate)
│   ├── Sub-metric B1
│   └── Sub-metric B2
└── Input Metric C (e.g., retention)
    ├── Sub-metric C1
    └── Sub-metric C2
```

### Step 2: Categorize Metrics

**Product Metrics:**
- Acquisition: How users find you (sign-ups, installs, registrations)
- Activation: First value moment (onboarding completion, first action)
- Engagement: Core usage (DAU/MAU, session length, feature adoption)
- Retention: Coming back (D1/D7/D30, cohort retention curves)
- Revenue: Monetization (ARPU, conversion, LTV, churn)

**Technical Metrics:**
- Performance: Latency (p50, p95, p99), throughput, error rate
- Reliability: Uptime, incident count, MTTR
- Infrastructure: CPU/memory utilization, cost per request

**AI/ML Metrics (if applicable):**
- Quality: Accuracy, hallucination rate, eval scores
- Safety: Content policy violation rate, false refusal rate
- Cost: Cost per inference, token usage
- Latency: Time to first token, tokens per second

**Business Metrics:**
- Revenue: MRR, ARR, revenue growth rate
- Unit economics: CAC, LTV, LTV/CAC ratio
- Market: Market share, competitive win rate

### Step 3: Set Targets & Alerts

For each metric, define:

| Metric | Current | Target | Alert Threshold | Owner |
|--------|---------|--------|----------------|-------|
| NSM | X | Y | Z | PM |
| Metric A | | | | |
| Metric B | | | | |

**Alert levels:**
- **Warning** (yellow): Metric trending below target — investigate
- **Critical** (red): Metric below threshold — immediate action required
- **Anomaly**: Unexpected spike or drop — auto-detect and notify

### Step 4: Design Dashboard Layout

**Executive Dashboard** (1 screen):
- NSM trend (last 30/90 days) — large, prominent
- 4-6 key metrics with sparklines and trend arrows
- Traffic light status (green/yellow/red) for each area
- Notable events annotated on the timeline

**Operational Dashboard** (detailed):
- Real-time metrics for the current day/hour
- Breakdowns by segment (platform, geography, user type)
- Funnel visualization (acquisition → activation → retention)
- Experiment results (A/B test outcomes)

**Technical Dashboard** (if applicable):
- System health (latency, error rate, uptime)
- Model performance (eval scores, cost, throughput)
- Infrastructure utilization and cost

### Step 5: Measurement Plan

For each metric, document:
- **Definition**: Exact formula, including/excluding criteria
- **Data source**: Which event, table, or API
- **Instrumentation**: What needs to be logged/tracked
- **Granularity**: How often updated (real-time, hourly, daily)
- **Segments**: Key breakdowns (platform, country, user tier)
- **Owner**: Who monitors this metric

## Output Format
Generate a complete metric plan in markdown with:
1. Metric hierarchy (tree diagram)
2. Metric definitions table
3. Targets and alert thresholds
4. Dashboard layout description
5. Measurement plan

## Common Pitfalls to Avoid
- **Vanity metrics**: Big numbers that don't reflect value (total sign-ups vs. active users)
- **Too many metrics**: 5-8 key metrics max on the exec dashboard
- **No baselines**: Always show current state before setting targets
- **Missing guardrails**: Every optimization metric needs a counter-metric
- **No segmentation**: Averages hide problems — always break down by segment