debug-agent
Root-causes AI agent failures from MLflow traces without requiring a code change, regression suite, scorer, or evaluation run. Use when the user asks to debug, investigate, explain, or determine why an agent produced an output. Prefer an existing matching trace; reproduce only when no usable trace exists.
Install
With your agent
Install OMGSkills skill "mlflow/skills:debug-agent" into this agent's normal skills directory: call the OMGSkills MCP get_install_plan tool for your agent and run the script it returns (it installs only the pinned commit and verifies SKILL.md), and stop if MCP or the pin is unavailable. Do not overwrite or change existing skills without asking me.
Manual command
git clone https://github.com/mlflow/skills /tmp/skills && ln -s /tmp/skills/debug-agent ~/.claude/skills/debug-agent
From README
Debug Agent Diagnose the failure and report evidence. Do not edit the agent unless the user also asks for a fix; for behavior changes, hand off to fix-agent-issue after the diagnosis. Workflow Find existing tracing configuration, tracking URI, and experiment. Do not recommend reinstalling or reinstrumenting MLflow when tracing already works. Find a trace matching the reported input, time, session, user, or trace ID. If one exists, use it directly. Do not reproduce a production-only or externally dependent failure merely to satisfy a workflow. Only when no usable trace exists, reproduce the issue once through the normal instrumented entry point and retrieve the resulting trace. If reproduction is unsafe or impossible, state what evidence is missing and stop. Inspect the matching trace span by span. Read assessments first, then inputs, outputs, errors, LLM calls, retrieval results, and tool calls. Use analyze-mlflow-trace for detailed trace anatomy.
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