---
title: "Testing and Refining Claude Code Skills with MLflow"
url: https://daily.dev/posts/testing-and-refining-claude-code-skills-with-mlflow-0inp55bns
source_url: https://mlflow.org/blog/evaluating-skills-mlflow
type: article
source: "mlflow"
published: 2026-04-20T13:19:22.302Z
updated: 2026-04-20T13:21:27.787Z
tags: ["machine-learning", "ai-agents", "claude-code"]
reading_time: 10
upvotes: 1
comments: 0
language: en
---

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# Testing and Refining Claude Code Skills with MLflow

**[mlflow](https://daily.dev/sources/MLflow)** · 10 min read · 1 upvotes · 0 comments

## Summary

A methodology for testing and automatically refining Claude Code skills (SKILL.md files) using MLflow tracing and LLM-based judges. The approach traces every tool call Claude makes during skill execution, then runs judges — both LLM-based and rule-based — to verify correct behavior. When judges fail, the failing trace and rationale are fed back to Claude Code, which edits the skill file itself. Two real examples from the agent-evaluation skill illustrate how this loop caught Claude bypassing MLflow APIs entirely and a missing skill dependency in the description field. Key lessons: write judges before polishing the skill, use both judge types, and ensure judge rationale is detailed enough for automated refinement.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://mlflow.org/blog/evaluating-skills-mlflow>

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---

Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#ai-agents](https://daily.dev/tags/ai-agents), [#claude-code](https://daily.dev/tags/claude-code)

[View this post on daily.dev](https://daily.dev/posts/testing-and-refining-claude-code-skills-with-mlflow-0inp55bns)