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Comprehensive debugging skill for agents, Temporal workflows, and Kubernetes infrastructure in distributed environments
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Based on SOC occupation classification
Fully autonomous decision engine with trial-and-error learning capabilities. Executes infrastructure operations without human intervention while leveraging reconciliation engines as safety nets. Learns from outcomes to improve future decisions through pattern recognition and adaptive optimization.
Monitor Kubernetes cluster health and generate alerts for issues. Use when you need automated health monitoring, resource utilization tracking, or proactive issue detection in Kubernetes environments.
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AI-powered automated scaling policies skill with intelligent resource optimization, predictive scaling decisions, and adaptive policy management. Use when implementing automated scaling with advanced AI capabilities for proactive resource management and cost optimization.
Enterprise-grade advanced monitoring with AI-powered analytics, real-time anomaly detection, predictive insights, and intelligent alerting. Use when implementing sophisticated monitoring with machine learning, time series analysis, and automated incident response.
Advanced deep learning automation for complex infrastructure pattern recognition, intelligent optimization, and self-healing systems. Use when implementing sophisticated AI capabilities with neural networks, deep learning models, and advanced pattern recognition.
| name | debug |
| description | Comprehensive debugging skill for agents, Temporal workflows, and Kubernetes infrastructure in distributed environments |
| version | 1.0.0 |
| risk_level | low |
| autonomy | fully_auto |
| layer | temporal |
| action_name | debug_system |
| input_schema | {"type":"object","properties":{"target_component":{"type":"string","enum":["agents","workflows","infrastructure","all"],"description":"Component to debug"},"issue_type":{"type":"string","enum":["performance","errors","timeouts","connectivity","resource","behavior"],"description":"Type of issue to investigate"},"time_range":{"type":"string","pattern":"^\\d+[mhd]$","description":"Time range for analysis (e.g., 30m, 2h, 1d)"},"namespace":{"type":"string","default":"temporal","description":"Kubernetes namespace to investigate"},"verbose":{"type":"boolean","default":false,"description":"Enable verbose logging"},"auto_fix":{"type":"boolean","default":false,"description":"Attempt automatic fixes for common issues"}},"required":["target_component","issue_type"]} |
| output_schema | {"type":"object","properties":{"debug_session_id":{"type":"string","description":"Unique identifier for this debug session"},"findings":{"type":"array","items":{"type":"object","properties":{"component":{"type":"string"},"severity":{"type":"string","enum":["critical","warning","info"]},"issue":{"type":"string"},"root_cause":{"type":"string"},"evidence":{"type":"array","items":{"type":"string"}},"recommendations":{"type":"array","items":{"type":"string"}}}}},"metrics_summary":{"type":"object","properties":{"total_issues":{"type":"integer"},"critical_issues":{"type":"integer"},"warnings":{"type":"integer"},"auto_fixes_applied":{"type":"integer"}}},"execution_time":{"type":"number","description":"Debug session duration in seconds"},"next_steps":{"type":"array","items":{"type":"string"}}},"required":["debug_session_id","findings","metrics_summary","execution_time"]} |
| human_gate | {"required":false,"description":"No human approval required for read-only debugging operations. Auto-fix requires review for critical components."} |
| dependencies | ["kubectl","curl","jq","python3","temporal-cli (optional)"] |
| environment_variables | ["KUBECONFIG","TEMPORAL_HOST","METRICS_ENDPOINT"] |
| tags | ["debugging","monitoring","troubleshooting","distributed-systems","agents","kubernetes","temporal"] |
This skill provides comprehensive debugging capabilities for agents running in distributed Kubernetes environments with Temporal orchestration. It systematically diagnoses issues across the entire stack - from individual agent behavior to infrastructure health.
python main.py debug \
--target-component agents \
--issue-type errors \
--time-range 1h \
--verbose
python main.py debug \
--target-component infrastructure \
--issue-type resource \
--time-range 30m \
--auto-fix
python main.py debug \
--target-component all \
--issue-type performance \
--time-range 2h \
--namespace temporal \
--verbose \
--auto-fix
/monitoring/metrics/monitoring/alerts/health/audit/eventsThe skill produces structured debugging reports with:
When auto_fix is enabled, the skill can:
This skill is designed for distributed environments and includes:
The skill helps prevent regressions by: