# North Star Metric

> Choose and operate one stage-fit product health metric with driver inputs, guardrails, ownership, and a review cadence. Use when teams track too many disconnected metrics, need a growth focus, or must distinguish a useful leading signal from vanity metrics.

- Skill: `majesticlabs-dev/north-star-metric` (Agent Skill)
- Install (CLI): `npx skillmds@latest add majesticlabs-dev/north-star-metric`
- Raw SKILL.md: https://api.skillmd.com/api/skills/majesticlabs-dev/north-star-metric/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Marketing & Growth
- Author: majesticlabs-dev (https://skillmd.com/u/majesticlabs-dev)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/majesticlabs-dev/north-star-metric

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# North Star Metric

## Boundary

Choose a focus metric, not a magical universal KPI. Do not prescribe fixed growth rates or public accountability without context.

## Required Inputs

- Business model, stage, strategy, and current constraint
- Revenue, retention, engagement, and customer-value evidence
- Candidate metrics, definitions, data quality, and controllability
- Known failure modes and guardrail metrics

## Workflow

1. State the current strategic question and time horizon.
2. Generate candidate metrics that reflect delivered customer value and business health.
3. Test candidates for sensitivity, controllability, clarity, durability, and gaming risk.
4. Choose one metric and document formula, source, owner, cadence, and exclusions.
5. Build a driver tree of actionable inputs and lagging outcomes.
6. Add guardrails for quality, retention, economics, safety, or segment harm.
7. Set a baseline and evidence-based target range, then define revisit triggers.

## Output

1. **Chosen metric and rationale**
2. **Definition, data source, owner, and cadence**
3. **Driver tree and guardrails**
4. **Baseline, target assumptions, and revisit triggers**

## Quality Gate

- The metric reflects value, not raw activity.
- Segments cannot hide behind an aggregate.
- Teams can influence drivers without gaming users.
- Target precision matches evidence quality.