Install
npx skills add mlflow/skills --skill mlflow-agent
Runs the open skills CLI and installs the skill for your agent (Claude Code, Codex, Cursor …). Alternatively copy the skill folder into .claude/skills/ or upload it in Claude.ai under Settings → Skills.
As a Claude Code plugin
/plugin marketplace add anthropics/claude-plugins-official
/plugin install mlflow@mlflow
This skill is part of the plugin “mlflow”. In Claude Code the plugin installs it together with the plugin’s other skills, commands and hooks.
What does the mlflow-agent skill do?
Description from the SKILL.md in the source repository.
Questions about mlflow-agent
What does the mlflow-agent skill do?
Master dispatcher for all MLflow workflows. Use this skill when the user wants to do anything with MLflow — tracing, evaluating, debugging, or improving an agent. Routes to the right MLflow sub-skill automatically. Triggers on: "use mlflow", "help with mlflow", "mlflow agent", "add mlflow to my project", "trace my agent", "evaluate my agent", or any MLflow task without a specific skill in mind.
How do I install mlflow-agent?
In a terminal: npx skills add mlflow/skills --skill mlflow-agent. The skills CLI asks which agents to install it for. In Claude.ai you can upload the skill folder as a ZIP under Settings → Skills.
Which agents can use mlflow-agent?
Skills use the open SKILL.md format, which Claude Code, Claude.ai, the Claude API, OpenAI Codex, Cursor, Gemini CLI and other agents support.
Is mlflow-agent safe to use?
Our automatic check found no risky patterns (piped shell scripts, hidden characters, secret access, prompt overrides). It is not a full audit – read the SKILL.md and any scripts before installing.
More skills from mlflow/skills
Fix or change an AI agent's behavior using MLflow traces and regression tests. Use for wrong answers, missing tool calls, new business rules, or context and token-cost optimization. Diagnose first, prove a regression fails, implement the smallest fix, and verify it passes without breaking existing behavior.
Searches and retrieves MLflow documentation from the official docs site. Use when the user asks about MLflow features, APIs, integrations (LangGraph, LangChain, OpenAI, etc.), tracing, tracking, or requests to look up MLflow documentation. Triggers on "how do I use MLflow with X", "find MLflow docs for Y", "MLflow API for Z".
Design MLflow scorers and run GenAI evaluation on an agent, dataset, precomputed outputs, or traces. Use for quality criteria, judges, MemAlign, datasets, evaluation runs, and results analysis.
Onboards users to MLflow by determining their use case (GenAI agents/apps or traditional ML/deep learning) and guiding them through relevant quickstart tutorials and initial integration. If an experiment ID is available, it should be supplied as input to help determine the use case. Use when the user asks to get started with MLflow, set up tracking, add observability, or integrate MLflow into their project. Triggers on "get started with MLflow", "set up MLflow", "onboard to MLflow", "add MLflow to my project", "how do I use MLflow".
Connect to SageMaker Managed MLflow (mlflow-app or mlflow-tracking-server ARN) as an MLflow backend, then hand off to the other MLflow skills. Triggers on a SageMaker MLflow ARN (arn:aws:sagemaker:...:mlflow-app/... or arn:aws:sagemaker:...:mlflow-tracking-server/...) or "SageMaker Managed MLflow".
Analyze MLflow traces, agent failures, and multi-turn chat sessions. Explain errors, unexpected output, quality issues, latency, or cross-turn propagation without editing application code.
Add or repair MLflow tracing in Python or TypeScript applications, including tracing required for evaluation. Covers framework autologging, custom spans, backend setup, and persisted trace verification.
Query aggregate MLflow trace, span and assessment metrics for latency, tokens, counts and quality trends. Use for operational summaries rather than individual-trace diagnosis.
Related skills in Agents & Workflows
Helps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express interest in extending capabilities. This skill should be used when the user is looking for functionality that might exist as an installable skill.
Configure this repo for the engineering skills: set up its issue tracker, triage label vocabulary, and domain doc layout. Run once before first use of the other engineering skills.
Teach the user a new skill or concept, within this workspace.
No description in the SKILL.md.
No description in the SKILL.md.
Ask which skill or flow fits your situation. A router over the skills in this repo.
No description in the SKILL.md.
Plan a huge chunk of work (more than one agent session can hold) as a shared map of decision tickets on your issue tracker, and resolve them one at a time until the way to the destination is clear.