Use when you need to track Service Level Objectives (SLOs) and ensure service reliability. Monitors SLO compliance, calculates error budgets, generates burn rate alerts, and provides reliability reporting for critical services across cloud environments.
SkillScore91Maintained
Automatic
◆DevOps & SRE pack
Last verified 8/4/2026
What it changes
Passed 20 of 22 test cases with the skill, and 7 of 22 without it.
Without skill32%
With skill91%
Checks it passed that the model alone missed
Sets the dryRun parameter to true
Sets the cloudProvider parameter to all
Identifies operation as a required input parameter
and 12 more in the full report
14 fixed6 passed either way1 still failing1 broke
Pass-rate lift
+59 pp
Tokens
+21%
Agent turns
0%
Response time
-50%
Model gemini-3.6-flashMeasured 8/4/2026v120 of 22 cases passed with skill
Tokens, turns and time describe resource use, not a dollar price.
See it in action
One of 14 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 14
Prompt
Which specific automation script coordinates operations across multiple cloud platforms in the core scripts directory?
What was checked, without and with the skill
Checked
Without skill
With skill
Identifies core/scripts/automation/multi_cloud_orchestrator.py for cross-provider coordination
Missed
Passed
case-13 · gemini-3.6-flash · 8/4/2026 · answers are capped and redacted
What's inside
The instructions your model reads when the skill loads.
SKILL.md113 linessingle file
Slo Monitor — Multi-Cloud Enterprise Automation
Purpose
Enterprise-grade automation solution for slo monitor operations across AWS, Azure, GCP, and on-premise environments to maximize operational efficiency while maintaining security and compliance standards.
When to Use
SLO Tracking: When monitoring service reliability against defined objectives
Error Budget Management: When tracking remaining error budget and burn rates
Reliability Reporting: When generating SLO compliance reports for stakeholders
Service Reviews: When preparing for service reliability reviews or post-mortems
Capacity Planning: When SLO trends indicate need for capacity improvements
Incident Prevention: When proactively monitoring for SLO degradation patterns
Compliance Requirements: When meeting contractual reliability obligations
Gotchas
Common Pitfalls
SLO Definition: Poorly defined SLOs lead to misleading reliability metrics
Measurement Windows: Short measurement windows can cause SLO volatility
Service Dependencies: SLOs may be affected by dependencies outside your control
Data Quality: Bad monitoring data leads to inaccurate SLO calculations
Edge Cases
New Services: New services lack historical data for meaningful SLOs
Seasonal Traffic: Usage patterns may affect SLO calculations predictably
Planned Maintenance: Maintenance windows should be excluded from SLO calculations
Multi-Region Services: Different regions may have different reliability characteristics
Performance Issues
Metric Volume: High-volume services can generate millions of data points
Calculation Complexity: Complex SLO calculations can be computationally expensive
Storage Requirements: Long-term SLO history requires significant storage
AI-powered multi-cloud cost optimization skill with ML-based recommendations, predictive spending analysis, and automated cost-saving measures. Use when cloud costs spike unexpectedly or when you need to optimize resource allocation across providers with advanced AI capabilities.