Writing North Star Metrics

by liqiongyu

skill

Define or refresh a product North Star metric + driver tree and produce a shareable North Star Metric Pack (narrative, metric spec, inputs, guardrails, rollout).

Skill Details

Repository Files

10 files in this skill directory


name: "writing-north-star-metrics" description: "Define or refresh a product North Star metric + driver tree and produce a shareable North Star Metric Pack (narrative, metric spec, inputs, guardrails, rollout)."

Writing North Star Metrics

Scope

Covers

  • Defining or refreshing a product/company North Star and North Star Metric
  • Translating a qualitative value model into measurable, decision-useful metrics
  • Creating a simple driver tree: leading input/proxy metrics + guardrails
  • Producing a “North Star Metric Pack” teams can use as a decision tie-breaker

When to use

  • “We need one metric that defines success.”
  • “Teams are optimizing different KPIs.”
  • “We’re setting quarterly OKRs and need leading indicators.”
  • “We’re launching a new strategy and need a metric that aligns decisions.”

When NOT to use

  • You only need OKRs for an already-agreed North Star
  • You need a full analytics taxonomy/event tracking plan from scratch
  • Stakeholders haven’t aligned on the customer value model / mission at all (do product vision/strategy first)
  • You’re choosing a single experiment metric for a one-off test

Inputs

Minimum required

  • Product/company + primary customer segment
  • The “value moment” (what the customer gets when things go well)
  • Business model + strategic goal (growth, activation, retention, margin, trust, etc.)
  • Time horizon (next quarter vs next year)
  • Measurement constraints (what you can measure today; data latency; known gaps)

Missing-info strategy

  • Ask up to 5 questions from references/INTAKE.md.
  • If still missing, proceed with clearly labeled assumptions and provide 2–3 options.

Outputs (deliverables)

Produce a North Star Metric Pack in Markdown (in-chat; or as files if the user requests):

  1. North Star Narrative (value model, tie-breaker, scope)
  2. Candidate metrics (3–5) + selection rationale (evaluation table)
  3. Chosen North Star Metric spec (definition, formula, window, segmentation, owner, data source)
  4. Driver tree (leading input/proxy metrics + guardrails)
  5. Validation & rollout plan (instrumentation checks, dashboard cadence, decision rules)
  6. Risks / Open questions / Next steps (always included)

Templates: references/TEMPLATES.md

Workflow (8 steps)

1) Intake + constraints

  • Inputs: User context; use references/INTAKE.md.
  • Actions: Confirm product, customer, value moment, horizon, constraints, stakeholders.
  • Outputs: 5–10 bullet “Context snapshot”.
  • Checks: You can explain the customer value in one sentence.

2) Define the qualitative North Star (before numbers)

  • Inputs: Context snapshot.
  • Actions: Write a North Star statement and value model from the customer’s perspective.
  • Outputs: Draft North Star Narrative (template in references/TEMPLATES.md).
  • Checks: Narrative can act as a decision tie-breaker (“if we do X, does it move the North Star?”).

3) Generate 3–5 candidate North Star metrics (customer POV)

  • Inputs: North Star Narrative + value moment.
  • Actions: Propose metrics that measure delivered customer value (not internal activity). Include at least one “friction/absence of pain” option when relevant.
  • Outputs: Candidate list with definitions.
  • Checks: Each candidate is measurable, understandable, and not trivially gameable.

4) Stress-test and pick the North Star metric

  • Inputs: Candidate metrics.
  • Actions: Evaluate with references/CHECKLISTS.md and references/RUBRIC.md. Explicitly test:
    • Leading vs lagging (avoid “retention as the only goal”; pair lagging outcomes with controllable inputs)
    • Controllability within a quarter (proxy/input metrics you can move)
    • Ecosystem impact (what breaks if you optimize this?)
  • Outputs: Selection table + chosen metric + why others lost.
  • Checks: A cross-functional leader could agree/disagree based on definitions and evidence.

5) Write the metric spec (make it unambiguous)

  • Inputs: Chosen metric.
  • Actions: Define formula, unit, window, inclusion rules, segmentation, owner, source, latency, and example calculation.
  • Outputs: North Star Metric Spec.
  • Checks: Two analysts would compute the same number.

6) Build the driver tree (inputs + guardrails)

  • Inputs: Metric spec + product levers.
  • Actions: Decompose into 3–7 drivers; identify leading input/proxy metrics you can move in weeks/months; add guardrails to prevent gaming/harm.
  • Outputs: Driver tree table + guardrails list.
  • Checks: Every driver has at least 1 realistic lever (initiative/experiment) and 1 measurement.

7) Define validation + rollout

  • Inputs: Driver tree + constraints.
  • Actions: Plan validation (sanity checks, correlation to outcomes) and operationalization (dashboards, cadence, owners, decision rules).
  • Outputs: Validation & Rollout Plan.
  • Checks: Plan includes “who does what, when” and works with current instrumentation.

8) Quality gate + finalize pack

  • Inputs: All drafts.
  • Actions: Run references/CHECKLISTS.md and score with references/RUBRIC.md. Add Risks/Open questions/Next steps.
  • Outputs: Final North Star Metric Pack.
  • Checks: Pack is shareable as-is; key decisions and caveats are explicit.

Quality gate (required)

Examples

Example 1 (B2B SaaS): “Define a North Star metric for a team collaboration tool.”
Expected: a pack that chooses a customer-value metric (e.g., weekly active teams completing the core value moment), plus a driver tree (activation → collaboration depth) and guardrails.

Example 2 (Marketplace): “Refresh North Star metric for a local services marketplace.”
Expected: a pack that measures delivered value (e.g., successful jobs completed with quality), plus input metrics for supply/demand balance and quality guardrails.

Boundary example: “Our North Star should be retention.”
Response: keep retention as an outcome/validation metric, and propose controllable input/proxy metrics (time-to-first-value, weekly value moments, repeat value delivery) as the operating focus.

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Skill Information

Category:Skill
Last Updated:1/23/2026