databricksInstall Databricks Mlflow Evaluation
Pick your agent. Every command is ready to paste.
npx skills add databricks/databricks-agent-skills --skill databricks-mlflow-evaluationThe open skills CLI detects your agents (Claude Code, Cursor, Codex and 70+ more) and installs to the right folder. Add -g to install globally.
01
Install it
Run the command above, or upload the folder in Claude.
02
Ask naturally
Describe the task. Your agent loads databricks-mlflow-evaluation when the request matches its description.
03
Name it if needed
If it does not trigger, say “use the databricks-mlflow-evaluation skill”. In Codex, type $databricks-mlflow-evaluation.
Databricks MLflow evaluation
Read databricks-core for authentication and workspace discovery. For tracing,
trace retrieval, observability, and trace-based debugging, use the separate
databricks-mlflow-tracing skill. Verify tracing when the selected evaluation mode or scorers require it; reuse
precomputed outputs or existing traces without rerunning the agent.
The guides below are references, not separately installed skills. When a guide names an upstream MLflow skill, use the matching guide here or the tracing skill.
| Request | Read this guide |
|---|---|
| Define quality criteria or scorers | Scorer design |
| Create a dataset and evaluate an agent | MLflow evaluation |
| Change agent behavior and verify improvement | Improve an agent |
For MLflow API details, consult the existing databricks-docs skill and
https://mlflow.org/docs/latest/llms.txt.
For Databricks judge models, prefer databricks:/system.ai.<model> with a
mlflow[databricks]>=3.16.0. A 404 commonly indicates an older client that
does not understand the system model URI; a 403 on a legacy endpoint name can
mean legacy serving endpoints are disabled. Verify the installed client and
available system.ai model before changing credentials or permissions.
For a new evaluation, establish a baseline with a fixed dataset and scorers,
change one thing, then run the same evaluation again. Use
databricks-mlflow-tracing first if traces are missing or a failure needs a
trace-grounded diagnosis.
Tips for Databricks Mlflow Evaluation
- • Start a fresh session after installing so your agent picks up the new skill.
- • It bundles reference files. The agent reads them only when needed, so they cost no context until then.
- • It is part of a pack with 30 skills. Install only the ones you need with --skill to keep triggers precise.
- • If two skills overlap, mention the one you want by name.
Questions about Databricks Mlflow Evaluation
What does the Databricks Mlflow Evaluation skill do?
MLflow GenAI evaluation and agent improvement on Databricks. Use when running mlflow.genai.evaluate(), building scorers or datasets, aligning judges with MemAlign, optimizing prompts, or monitoring production quality.
How do I install Databricks Mlflow Evaluation?
Run "npx skills add databricks/databricks-agent-skills --skill databricks-mlflow-evaluation" in your project. The skills CLI installs it for Claude Code, Cursor, Codex and 70+ other agents. In Claude on the web, upload the skill folder as a ZIP under Customize > Skills.
Does Databricks Mlflow Evaluation work in Cursor and Codex?
Yes. Databricks Mlflow Evaluation uses the open SKILL.md format, so it works in Claude Code, Claude, Cursor, OpenAI Codex, Gemini CLI, GitHub Copilot and other agents that support agent skills.
Is Databricks Mlflow Evaluation free?
Databricks Mlflow Evaluation is open source on GitHub. It is free to install; check the license before commercial use.
Who made Databricks Mlflow Evaluation?
Databricks Mlflow Evaluation is published by databricks in the databricks/databricks-agent-skills repository, together with 29 other skills.
