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mlflow — Agent Skill

by mlflow · Updated

Skills for tracing, evaluating, and improving AI agents with MLflow. Supports the full agent improvement loop: instrument → trace → evaluate → iterate → validate.

At a glance

mlflow is an Agent Skill by mlflow in the Automation & Workflow category. In Claude Code it installs with `/plugin install mlflow@claude-plugins-official` after adding the anthropics/claude-plugins-official marketplace; other assistants use its SKILL.md instructions as rules. Its source repository has 82 GitHub stars (as of Oct 28, 2026).

Source 82GitHub stars

How to install mlflow in your AI client

Setup for Claude Code, Claude Desktop, Cursor, Codex, Windsurf, Cline and VS Code — copy-paste ready.

How to install mlflow in Claude CodeAnthropic's agentic CLI

  1. Add the marketplace

    claude
    /plugin marketplace add anthropics/claude-plugins-official
  2. Install the skill

    claude
    /plugin install mlflow@claude-plugins-official
  3. Use it

    These are slash commands for a Claude Code session, not your shell. Claude auto-invokes the skill when your request matches its description — or type its slash command if it exposes one.

Documentation

Full README

MLflow Skills for Coding Agents

Turn your favorite coding agent into an LLMOps expert with MLflow skills.

Build, debug, and evaluate GenAI applications with confidence. These skills give your AI coding assistant deep knowledge of MLflow's tracing, evaluation, and observability features.

Works with any coding agent that support Skills, including Claude Code, Cursor, Codex CLI, Gemini CLI, and OpenCode.

Why MLflow Skills?

Building production-ready AI agents is hard. You need observability to understand what your agent is doing, evaluation to measure quality, and debugging tools when things go wrong. MLflow provides SDKs and best practices for all of these operations, and with skills we bring them directly into the environment where LLM agent development happens. Now you can go to your favorite coding agent and just ask:

  • *"Add tracing to my LangChain app"* → Instruments your code automatically
  • *"Why did this trace fail?"* → Analyzes spans, finds root causes, suggests fixes
  • *"Evaluate my agent's accuracy"* → Sets up datasets, scorers, and runs evaluation
  • *"Improve my agent and verify your work"* → Gives the coding agent reproducible and verifiable mechanism with MLflow eval to hill climb on quality
  • *"Show me token usage trends"* → Queries metrics and analyze trends

Available Skills

| Skill | Description | |-------|-------------| | mlflow-agent | Routes broad MLflow requests to the right skill. | | mlflow-onboarding | Configures MLflow for GenAI or traditional ML. | | mlflow-docs | Searches official MLflow documentation. | | mlflow-sagemaker | Connects to SageMaker Managed MLflow. | | mlflow-trace-instrumentation | Instruments Python and TypeScript applications. | | mlflow-trace-retrieval | Finds and filters traces. | | mlflow-trace-analysis | Analyzes traces, agent failures, and multi-turn sessions. | | mlflow-trace-metrics | Queries aggregate trace metrics. | | mlflow-evaluation | Designs scorers and runs GenAI evaluation. | | mlflow-agent-improvement | Fixes agent behavior with trace-grounded regression tests. |


Installation

Using skills installer

bash
npx skills add mlflow/skills

Excerpt — the full document is on GitHub.

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