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Automation
Skill

/usage-health-scorecard

Framework for monitoring activation, engagement, and monetization guardrails.

From plugin
gtm-agents
368200 skills200 agents199 commands
Install
$ npx -y skills add gtmagents/gtm-agents --skill usage-health-scorecard --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/usage-health-scorecard

Context preview

The summary Claude sees to decide when to auto-load this skill.

Framework for monitoring activation, engagement, and monetization guardrails.

SKILL.md

usage-health-scorecard.SKILL.md
name: usage-health-scorecard
description: Framework for monitoring activation, engagement, and monetization guardrails.

Usage Health Scorecard Skill

When to Use

  • Tracking activation/retention metrics for self-serve and hybrid customer cohorts.
  • Monitoring the impact of PLG experiments on core product health.
  • Sharing health snapshots with product, growth, and CS leads.

Framework

1. **Metric Groups** – activation, engagement, collaboration, monetization, support load. 2. **Segmentation** – persona, plan, cohort, region, product area. 3. **Thresholds** – traffic light ranges for each metric with warning/critical bands. 4. **Alerting** – notification rules for drops, anomalies, or experiment impacts. 5. **Action Registry** – log remediation steps, owners, due dates, and results.

Templates

  • Scorecard layout (metric, current, target, delta, owner).
  • Dashboard wireframe with spark lines + annotations.
  • Action tracker for follow-up tasks linked to health changes.

Tips

  • Pair with `launch-in-app-experiments` to watch guardrails post-launch.
  • Include qualitative signals (support tags, NPS) to contextualize telemetry.
  • Archive snapshots to tell longitudinal stories in QBRs.

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