# analyzing-mlflow-trace

> Claude/Codex skill by [@mlflow](https://omgskills.com/library/mlflow/index.md) · 82 stars · Updated 2026-10-23

Analyzes a single MLflow trace to answer a user query about it. Use when the user provides a trace ID and asks to debug, investigate, find issues, root-cause errors, understand behavior, or analyze quality. Triggers on "analyze this trace", "what went wrong with this trace", "debug trace", "investigate trace", "why did this trace fail", "root cause this trace".

## Install

```sh
git clone https://github.com/mlflow/skills /tmp/skills && ln -s /tmp/skills/analyze-mlflow-trace ~/.claude/skills/analyzing-mlflow-trace
```

## From README

Analyzing a Single MLflow Trace Trace Structure A trace captures the full execution of an AI/ML application as a tree of spans. Each span represents one operation (LLM call, tool invocation, retrieval step, etc.) and records its inputs, outputs, timing, and status. Traces also carry assessments — feedback from humans or LLM judges about quality. It is recommended to read references/trace-structure.md before analyzing a trace — it covers the complete data model, all fields and types, analysis guidance, and OpenTelemetry compatibility notes. For a batch of traces, component bottlenecks, or recurring error patterns, read references/cross-trace-analysis.md after selecting a bounded sample with retrieving-mlflow-traces. Handling CLI Output Traces can be 100KB+ for complex agent executions. Always redirect output to a file — do not pipe mlflow traces get directly to jq, head, or other commands, as piping can silently produce no output.

## Links

- HTML page: https://omgskills.com/library/mlflow/skills/analyze-mlflow-trace/
- GitHub: https://github.com/mlflow/skills
- Author: [@mlflow](https://omgskills.com/library/mlflow/index.md)

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