Metrics definer

Skill ComeOnOliver/skillshub/skills/aakashg/pm-claude-code-setup/metrics-definer

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Install

npx -y skills add ComeOnOliver/skillshub --skill metrics-definer

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

SKILL.md

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Metrics Definer

Trigger

Activate on "define metrics", "what should I measure", "success metrics for [feature]", "KPIs for [initiative]".

Behavior

Step 1: Get Context

Ask:

What feature or initiative?
What's the goal?
What can we currently measure?

Step 2: Define Metrics

Primary Metric

Name, exact definition, measurement method, target, timeframe

Secondary Metrics (2-3)

Name, definition, why it matters

Guardrail Metrics (2-3)

What should NOT get worse. Current baseline and acceptable range.

Leading Indicators

What to measure in week 1 that predicts long-term success

Anti-Metrics

What metric going UP would actually be bad

Example

Bad metrics (vague, unmeasurable):

Primary Metric: Engagement
Secondary: User satisfaction
Guardrail: Performance

Good metrics (precise, measurable, useful):

Primary Metric:
- Name: 7-day feature activation rate
- Definition: % of users who complete at least one [action] within
  7 days of first exposure to the feature
- Measurement: Event tracking via Mixpanel. Event: "feature_action_completed"
- Baseline: N/A (new feature)
- Target: 30% within 90 days of launch
- Timeframe: Measured weekly, evaluated at 90 days

Guardrail Metrics:
- Overall page load time stays under 2s (p95). Currently: 1.4s.
  Acceptable range: up to 2.0s. Beyond 2.0s = performance regression, pause rollout.
- Support ticket volume for this feature area stays below 50/week.

Anti-Metric:
- Daily active usage going UP could be bad if it means users are
  confused and returning to retry failed actions. Cross-reference
  with task completion rate — high DAU + low completion = friction.

Rules

Every metric needs a precise definition. "Engagement" without defining what counts is not a metric.
Flag metrics requiring new instrumentation with [NEEDS INSTRUMENTATION]
Always specify the data source. No metric exists without a measurement method.
Anti-metrics are mandatory. If you cannot identify one, you have not thought hard enough about perverse incentives.