Does this skill require Gemini or Codex access?

While it is optimized for a multi-agent ensemble including Gemini and Codex for web research and deep code analysis, the core reasoning and orchestration are handled by Claude.

How does semantic log analysis differ from standard grep?

Unlike keyword matching, semantic analysis uses LLMs to understand the context and meaning of logs, allowing it to cluster related errors and identify anomalies human-written regex might miss.

What is a multi-agent council in debugging?

It is a workflow where different AI models take on specialized roles—such as Code Reviewer or Log Analyst—to independently investigate a bug and then debate their findings to reach a high-confidence consensus.

Can Multi-AI Debugging prevent future incidents?

Yes, part of its core workflow includes generating prevention recommendations and automated regression tests that ensure the specific bug cannot reappear in your codebase.

Multi-AI Debugging

Name: Multi-AI Debugging
Author: adaptationio

byadaptationio

분석 및 모니터링

Orchestrates multiple AI agents to perform systematic root cause analysis, semantic log classification, and automated fix generation for complex system failures.

소개

Multi-AI Debugging is an advanced ensemble framework for Claude Code that utilizes specialized agents—including Claude, Gemini, and Codex—to solve the most challenging production incidents. By moving beyond simple error lookup, this skill implements a multi-agent council to debate root causes, perform deep semantic log analysis across distributed services, and execute evaluator-critic loops for high-confidence code remediation. It is an essential tool for engineering teams looking to reduce MTTR by automating the heavy lifting of incident investigation, from initial diagnosis to the generation of verified regression tests.

주요 기능

Automated RCA report generation with incident timelines and prevention steps
Multi-Agent Council architecture for cross-validated root cause analysis
0 GitHub stars
Evaluator-Critic loops for generating and verifying automated bug fixes
Semantic log clustering and classification to identify unique error patterns
Cross-stack correlation connecting frontend, backend, and infrastructure logs

사용 사례

Investigating complex production incidents and cascading multi-service failures
Performing deep root cause analysis (RCA) on non-obvious performance regressions
Analyzing and prioritizing large volumes of error logs during system outages

소개

주요 기능

Automated RCA report generation with incident timelines and prevention steps
Multi-Agent Council architecture for cross-validated root cause analysis
0 GitHub stars
Evaluator-Critic loops for generating and verifying automated bug fixes
Semantic log clustering and classification to identify unique error patterns
Cross-stack correlation connecting frontend, backend, and infrastructure logs

사용 사례

Investigating complex production incidents and cascading multi-service failures
Performing deep root cause analysis (RCA) on non-obvious performance regressions
Analyzing and prioritizing large volumes of error logs during system outages