Skill Test
databricks-solutions/ai-dev-kit
Testing framework for evaluating Databricks skills. An agent skill from databricks-solutions/ai-dev-kit.
MLflow 3 GenAI agent evaluation. An agent skill from databricks/databricks-agent-skills.
Install Databricks Mlflow Evaluation
$ npx skills add databricks/databricks-agent-skills --skill databricks-mlflow-evaluation -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install databricks/databricks-agent-skills databricks-mlflow-evaluation --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/databricks/databricks-agent-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/databricks-mlflow-evaluation .claude/skills/databricks-mlflow-evaluation && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "databricks-mlflow-evaluation" agent skill from https://github.com/databricks/databricks-agent-skills/tree/main/skills/databricks-mlflow-evaluation into .claude/skills/databricks-mlflow-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "databricks-mlflow-evaluation", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/databricks/databricks-agent-skills/tree/main/skills/databricks-mlflow-evaluationType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add databricks/databricks-agent-skills --skill databricks-mlflow-evaluation -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install databricks/databricks-agent-skills databricks-mlflow-evaluation --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/databricks/databricks-agent-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/databricks-mlflow-evaluation .agents/skills/databricks-mlflow-evaluation && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "databricks-mlflow-evaluation" agent skill from https://github.com/databricks/databricks-agent-skills/tree/main/skills/databricks-mlflow-evaluation into .agents/skills/databricks-mlflow-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "databricks-mlflow-evaluation", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add databricks/databricks-agent-skills --skill databricks-mlflow-evaluation -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install databricks/databricks-agent-skills databricks-mlflow-evaluation --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/databricks/databricks-agent-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/databricks-mlflow-evaluation .cursor/skills/databricks-mlflow-evaluation && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "databricks-mlflow-evaluation" agent skill from https://github.com/databricks/databricks-agent-skills/tree/main/skills/databricks-mlflow-evaluation into .cursor/skills/databricks-mlflow-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "databricks-mlflow-evaluation", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/databricks/databricks-agent-skills.git --path skills/databricks-mlflow-evaluation--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add databricks/databricks-agent-skills --skill databricks-mlflow-evaluation -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install databricks/databricks-agent-skills databricks-mlflow-evaluation --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/databricks/databricks-agent-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/databricks-mlflow-evaluation .gemini/skills/databricks-mlflow-evaluation && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "databricks-mlflow-evaluation" agent skill from https://github.com/databricks/databricks-agent-skills/tree/main/skills/databricks-mlflow-evaluation into .gemini/skills/databricks-mlflow-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "databricks-mlflow-evaluation", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install databricks/databricks-agent-skills databricks-mlflow-evaluationInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add databricks/databricks-agent-skills --skill databricks-mlflow-evaluation -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/databricks/databricks-agent-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/databricks-mlflow-evaluation .github/skills/databricks-mlflow-evaluation && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "databricks-mlflow-evaluation" agent skill from https://github.com/databricks/databricks-agent-skills/tree/main/skills/databricks-mlflow-evaluation into .github/skills/databricks-mlflow-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "databricks-mlflow-evaluation", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add databricks/databricks-agent-skills --skill databricks-mlflow-evaluation -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install databricks/databricks-agent-skills databricks-mlflow-evaluation --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/databricks/databricks-agent-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/databricks-mlflow-evaluation .opencode/skills/databricks-mlflow-evaluation && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "databricks-mlflow-evaluation" agent skill from https://github.com/databricks/databricks-agent-skills/tree/main/skills/databricks-mlflow-evaluation into .opencode/skills/databricks-mlflow-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "databricks-mlflow-evaluation", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
databricks-mlflow-evaluation- GitHub stars
- 345
- Token cost
- ~2.7k tokens
- SKILL.md length
- 1,046 words
- Files
- 15 (incl. references, assets)
- Skills in repo
- 32
- Repo updated
- First seen
- Licence
- Custom licence
At a glance
MLflow 3 GenAI agent evaluation. An agent skill from databricks/databricks-agent-skills.
- Works in 2 steps: Read GOTCHAS.md - 15+ common mistakes… → Read CRITICAL-interfaces.md - Exact API…
- Writing mlflow.genai.evaluate() code
- SKILL.md covers Scope vs upstream mlflow/skills, Before Writing Any Code, End-to-End Workflows and Reference Files Quick Lookup, plus 2 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Databricks Mlflow Evaluation is an agent skill from databricks/databricks-agent-skills, published by the product's own GitHub organization. MLflow 3 GenAI agent evaluation. Use when writing mlflow.genai.evaluate() code, creating @scorer functions, using built-in scorers (Guidelines, Correctness, Safety, RetrievalGroundedness), building eval datasets from traces, setting up trace ingestion and production monitoring, aligning judges with MemAlign from domain expert feedback, or running optimizeprompts() with GEPA for automated prompt improvement.
Its SKILL.md is about 2.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 17 other files, including reference files and assets (for example `agents/openai.yaml`, `references/CRITICAL-interfaces.md` and `references/GOTCHAS.md`). Compatibility notes: Requires databricks CLI (= v1.0.0)
It sits in Agent Workflows, covering Agent evaluation and testing. It works with MLflow and Databricks. The repository describes itself as: Databricks AI Tools: skills and plugins for building on Databricks with Claude Code, Cursor, Codex, GitHub Copilot, and other AI coding agents.
When your agent uses it
- Writing mlflow.genai.evaluate() code
- Creating @scorer functions
- Using built-in scorers (Guidelines
- RetrievalGroundedness)
Example prompts
- “/databricks-mlflow-evaluation”
Requirements
- Compatibility (from SKILL.md): Requires databricks CLI (>= v1.0.0)
Workflow steps
2 steps, taken from the first numbered list in SKILL.md.
- Read GOTCHAS.md - 15+ common mistakes that cause failures
- Read CRITICAL-interfaces.md - Exact API signatures and data schemas
What it can do on your machine
Read from SKILL.md and the folder at commit f4fcec5. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
Links to these hosts (documentation or services it may open):
github.com
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Compatibility
Requires databricks CLI (>= v1.0.0)
From compatibility in the SKILL.md frontmatter.
Context cost
Databricks Mlflow Evaluation loads about 2.7k tokens when it runs, and up to ~54k if it reads all its reference files. Until then it costs about 110 tokens; SKILL.md has 1,046 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
Its licence (Custom licence) doesn't allow us to republish the file, so here is its outline and opening line. It has 1,046 words (~2,685 tokens).
“The OSS mlflow/skills repo ships agent-evaluation and related skills (instrumenting-with-mlflow-tracing, analyze-mlflow-trace, retrieving-mlflow-traces, querying-mlflow-metrics) that cover the generic MLflow GenAI evaluation workflow — mlflow.genai.evaluate(), scorers/judges, datasets, tracing setup, and the 5-step evaluation loop.”
- name
- databricks-mlflow-evaluation
- compatibility
- Requires databricks CLI (>= v1.0.0)
- metadata.version
- 0.1.0
- parent
- databricks-core
Files
SKILL.md and 14 other files (references, assets) in skills/databricks-mlflow-evaluation of databricks/databricks-agent-skills.
- SKILL.md
- agents/openai.yaml
- assets/databricks.png
- assets/databricks.svg
- references/CRITICAL-interfaces.md
- references/GOTCHAS.md
- references/patterns-context-optimization.md
- references/patterns-datasets.md
- references/patterns-evaluation.md
- references/patterns-judge-alignment.md
- references/patterns-prompt-optimization.md
- references/patterns-scorers.md
- references/patterns-trace-analysis.md
- references/patterns-trace-ingestion.md
- references/user-journeys.md
Open the folder on GitHubat commit f4fcec5
Compare with similar skills
Databricks Mlflow Evaluation next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Databricks Mlflow Evaluation this skilldatabricks/databricks-agent-skills | 345 | — | ~2.7k | Automated safety check: Pass | Custom licence | |
| Skill Testdatabricks-solutions/ai-dev-kit | 1.9k | — | ~1.9k | Automated safety check: Pass | Custom licence | |
| Azure Machine LearningMicrosoftDocs/Agent-Skills | 775 | 1 repos | ~19k | Automated safety check: Pass | CC-BY-4.0 | |
| Azure Data Science VmMicrosoftDocs/Agent-Skills | 775 | — | ~1.8k | Automated safety check: Pass | CC-BY-4.0 | |
| MCP Server Builderanthropics/skills | 180k | 62 repos | ~2.3k | Automated safety check: Pass | Apache-2.0 | |
| Diagnosing Superpowers Sessionsobra/superpowers | 296k | 3 repos | ~1.7k | Automated safety check: Pass | MIT |
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Works with
Categories
Questions about Databricks Mlflow Evaluation
What does Databricks Mlflow Evaluation do?
MLflow 3 GenAI agent evaluation. An agent skill from databricks/databricks-agent-skills. Databricks Mlflow Evaluation is an agent skill from databricks/databricks-agent-skills, published by the product's own GitHub organization. MLflow 3 GenAI agent evaluation.
When should I use Databricks Mlflow Evaluation?
Databricks Mlflow Evaluation fits situations like: writing mlflow.genai.evaluate() code; creating @scorer functions; using built-in scorers (Guidelines; retrievalGroundedness).
How do I install Databricks Mlflow Evaluation in Claude Code?
Run `npx skills add databricks/databricks-agent-skills --skill databricks-mlflow-evaluation -a claude-code`. Or copy the skill folder (skills/databricks-mlflow-evaluation in databricks/databricks-agent-skills) into .claude/skills/databricks-mlflow-evaluation in your project. Claude Code loads it when a task matches its description.
How do I install Databricks Mlflow Evaluation in Codex?
Run `npx skills add databricks/databricks-agent-skills --skill databricks-mlflow-evaluation -a codex`. Or copy the skill folder (skills/databricks-mlflow-evaluation in databricks/databricks-agent-skills) into .agents/skills/databricks-mlflow-evaluation in your project. Codex loads it when a task matches its description.
Can I use Databricks Mlflow Evaluation in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add databricks/databricks-agent-skills --skill databricks-mlflow-evaluation -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/databricks-mlflow-evaluation, .gemini/skills/databricks-mlflow-evaluation, .github/skills/databricks-mlflow-evaluation and .opencode/skills/databricks-mlflow-evaluation in your project.
What does Databricks Mlflow Evaluation need to run?
SKILL.md names no scripts, command-line tools or credentials: Databricks Mlflow Evaluation is instructions for the agent only. Compatibility (from SKILL.md): Requires databricks CLI (>= v1.0.0).
Does Databricks Mlflow Evaluation access the network?
SKILL.md names 1 domain. As links in the text: github.com. This is read from the text; nothing was executed.
Is Databricks Mlflow Evaluation safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Databricks Mlflow Evaluation use?
Databricks Mlflow Evaluation has a licence file (the repository's licence) that doesn't match a standard licence. Read it on GitHub before reusing the skill.
How many tokens does Databricks Mlflow Evaluation use?
About 2.7k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 52k tokens, read only when the agent opens those files.
What are the alternatives to Databricks Mlflow Evaluation?
Skills that share tags, products or a category with Databricks Mlflow Evaluation: Skill Test (databricks-solutions/ai-dev-kit, 1.9k stars), Azure Machine Learning (MicrosoftDocs/Agent-Skills, 775 stars), Azure Data Science Vm (MicrosoftDocs/Agent-Skills, 775 stars) and MCP Server Builder (anthropics/skills, 180k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Databricks Mlflow Evaluation?
databricks (a GitHub organization, an official publisher) maintains it in databricks/databricks-agent-skills, which has 345 GitHub stars. The repository holds 32 skills in this directory. The repository was last updated on October 6, 2026.
Source: databricks/databricks-agent-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.