name: sla-monitor description: Set up SLA monitoring and uptime tracking for AI agents and services. Generates monitoring configs, alert rules, and incident response playbooks. Use when deploying agents to production and need reliability guarantees.

SLA Monitor Skill

Purpose

Help teams set up production-grade monitoring for AI agents and automated services. Covers uptime tracking, response time SLAs, error budgets, and incident escalation.

When to Use

• Deploying AI agents to production
• Setting up monitoring for client-facing automation
• Creating SLA documentation for service agreements
• Building incident response procedures

Monitoring Stack Options

Option 1: UptimeRobot (Free tier available)

• 50 monitors free, 5-minute intervals
• HTTP, keyword, ping, port monitors
• Email + Slack + webhook alerts

Option 2: Better Stack (Formerly Uptime.com)

• Status pages included
• Incident management built-in
• Free tier: 10 monitors

Option 3: Self-Hosted (Uptime Kuma)

docker run -d --restart=always -p 3001:3001 -v uptime-kuma:/app/data --name uptime-kuma louislam/uptime-kuma:1

SLA Tiers

Tier 1: Standard ($1,500/mo)

• 99.5% uptime guarantee (43.8h downtime/year)
• Response within 4 hours (business hours)
• Monthly performance report

Tier 2: Professional ($3,000/mo)

• 99.9% uptime guarantee (8.76h downtime/year)
• Response within 1 hour (business hours)
• Weekly performance reports
• Quarterly optimization reviews

Tier 3: Enterprise ($5,000+/mo)

• 99.95% uptime (4.38h downtime/year)
• Response within 15 minutes (24/7)
• Real-time dashboard access
• Dedicated support channel

Alert Configuration Template

monitors:
  - name: "Agent Health Check"
    type: http
    url: "https://your-agent-endpoint/health"
    interval: 300  # 5 minutes
    alerts:
      - type: email
        threshold: 1  # alert after 1 failure
      - type: slack
        webhook: "${SLACK_WEBHOOK}"
        threshold: 2  # alert after 2 consecutive failures
      - type: sms
        threshold: 3  # escalate after 3 failures

  - name: "API Response Time"
    type: http
    url: "https://your-agent-endpoint/api"
    interval: 60
    expected_response_time: 2000  # ms
    alerts:
      - type: slack
        condition: "response_time > 5000"

error_budget:
  monthly_target: 99.9
  burn_rate_alert: 2.0  # Alert if burning 2x normal rate

Incident Response Playbook

Severity 1 — Total Outage

1. Acknowledge within 5 minutes
2. Status page update within 10 minutes
3. Root cause identification within 30 minutes
4. Resolution or workaround within 2 hours

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5. Post-mortem within 24 hours

Severity 2 — Degraded Performance

1. Acknowledge within 15 minutes
2. Investigation within 30 minutes
3. Resolution within 4 hours
4. Summary report within 48 hours

Severity 3 — Minor Issue

1. Acknowledge within 1 hour
2. Resolution within 24 hours
3. Logged for next review cycle

Error Budget Calculator

Monthly minutes: 43,200 (30 days)
99.9% SLA = 43.2 minutes downtime allowed
99.5% SLA = 216 minutes downtime allowed
99.0% SLA = 432 minutes downtime allowed

Burn rate = (actual downtime / budget) × 100
If burn rate > 50% with 2+ weeks remaining → review needed
If burn rate > 80% → freeze deployments

Status Page Template

Provide clients with a public status page showing: - Current system status (operational / degraded / outage) - Component-level status (Agent A, Agent B, API, Dashboard) - Uptime percentage (30-day rolling) - Incident history with resolution notes - Scheduled maintenance windows

Next Steps

Need managed AI agents with built-in SLA monitoring? → AfrexAI handles deployment, monitoring, and maintenance for $1,500/mo → Book a call: https://calendly.com/cbeckford-afrexai/30min → Learn more: https://afrexai-cto.github.io/aaas/landing.html

这个 Skill 质量中等偏上。它全面介绍了 SLA 监控的概念、工具选择和配置方法，对新手友好。但不足之处是内容偏理论，实际可用的代码示例较少，更多像一份产品手册而非可直接使用的技能包。如果需要快速搭建监控，可能需要额外查阅其他资料。