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debug-agent

debug-agent

github.com/mlflowTesting & quality82232 installsUpdated 2d ago
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About debug-agent

debug-agent is a workflow skill for diagnosing failures in MLflow-traced agents. It uses trace evidence to identify a cause and likely fix, without changing code unless asked.

Key features

  1. 01Finds and inspects matching MLflow traces
  2. 02Correlates trace evidence with source code
  3. 03Reports behavior, evidence, cause, and confidence
  4. 04Avoids edits during diagnosis-only requests

Use cases

  1. 01Investigating an agent failure with an existing trace
  2. 02Reproducing an issue when no usable trace exists
  3. 03Handing a confirmed diagnosis to a fix workflow

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/mlflow/skills --skill debug-agent

After installing, type/debug-agentin Claude Code to confirm it loaded.

Bundled filesagents/openai.yaml

Details

From the project 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

  1. Find existing tracing configuration, tracking URI, and experiment. Do not recommend reinstalling or reinstrumenting MLflow when tracing already works.
  2. 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.
  3. 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.
  4. 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.
  5. Correlate trace evidence with source code as needed. Reading source is fine; do not settle on a diagnosis without confirming it against the trace.
  6. Report: observed behavior, expected behavior, trace evidence, root cause, confidence, and the smallest likely fix. Do not create tests or scorers for a diagnosis-only request.

Boundaries

  • If no trace can be produced because the app is uninstrumented, load instrumenting-with-mlflow-tracing and explain the limitation.
  • Log the user's complaint as HUMAN feedback only when a concrete trace is identified and doing so is authorized.
  • If the user asks to implement the fix, invoke fix-agent-issue with the trace ID and diagnosis so it can write regression coverage before editing.

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