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Automation
Skill

/site-performance-watch

Monitoring and alerting framework for ecommerce site speed, errors, and

From plugin
gtm-agents
368200 skills200 agents199 commands
Install
$ npx -y skills add gtmagents/gtm-agents --skill site-performance-watch --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/site-performance-watch

Context preview

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

Monitoring and alerting framework for ecommerce site speed, errors, and

SKILL.md

site-performance-watch.SKILL.md
name: site-performance-watch
description: Monitoring and alerting framework for ecommerce site speed, errors, and
  uptime.

Site Performance Watch Skill

When to Use

  • Setting up proactive monitoring during campaigns or launches.
  • Investigating conversion drops tied to latency or availability issues.
  • Communicating performance incidents to merchandising and engineering teams.

Framework

1. **KPI Stack** – FCP, LCP, CLS, TTFB, checkout API latency, error rates, uptime. 2. **Segmentation** – device, geography, browser, promotion, traffic source. 3. **Alerting Rules** – thresholds, aggregation windows, escalation paths, war-room triggers. 4. **Diagnostics** – logging, tracing, screenshot/session replay hooks. 5. **Comms Kit** – stakeholder updates, status pages, rollback plans.

Templates

  • Performance scorecard with spark lines + thresholds.
  • Incident log template with root cause, mitigation, and follow-up tasks.
  • Escalation matrix covering engineering, DevOps, merchandising, and CX leads.

Tips

  • Pair synthetic monitoring with RUM data to catch both systemic and localized issues.
  • Freeze experiment/merch changes when performance breaches critical thresholds.
  • Use alongside `diagnose-conversion-drop` to correlate experience and performance data.

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