Multi-AI Debugging Council

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Multi-AI Debugging is a sophisticated troubleshooting framework that leverages a multi-agent architecture to resolve complex production incidents and cascading failures. By coordinating specialized agents for code analysis, log synthesis, and web research, the skill provides a 'council' approach to root cause analysis (RCA) that minimizes bias and increases diagnostic accuracy. It moves beyond simple error messages to perform semantic log analysis, cross-stack correlation, and critic-verified auto-remediation, significantly reducing the mean time to resolution (MTTR) for distributed systems and multi-service architectures.

Características Principales

• Semantic log analysis and automated error classification
• Automated remediation playbooks and self-healing patterns
• Cross-stack correlation across frontend, backend, and infrastructure
• 0 GitHub stars
• Multi-Agent Council for consensus-based root cause analysis
• Evaluator/Critic loops for verified bug fix generation

Casos de Uso

• Diagnosing complex production incidents and cascading failures
• Performing deep-dive Root Cause Analysis (RCA) for multi-service systems
• Automating log classification and priority-based error reporting