Edit
omegaml/omegaml
how to use the edit command properly
Train ML models on Databricks. An agent skill from databricks/databricks-agent-skills.
Install Databricks ML Training
$ npx skills add databricks/databricks-agent-skills --skill databricks-ml-training -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install databricks/databricks-agent-skills databricks-ml-training --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-ml-training .claude/skills/databricks-ml-training && 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-ml-training" agent skill from https://github.com/databricks/databricks-agent-skills/tree/main/skills/databricks-ml-training into .claude/skills/databricks-ml-training/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "databricks-ml-training", 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-ml-trainingType 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-ml-training -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install databricks/databricks-agent-skills databricks-ml-training --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-ml-training .agents/skills/databricks-ml-training && 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-ml-training" agent skill from https://github.com/databricks/databricks-agent-skills/tree/main/skills/databricks-ml-training into .agents/skills/databricks-ml-training/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "databricks-ml-training", 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-ml-training -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install databricks/databricks-agent-skills databricks-ml-training --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-ml-training .cursor/skills/databricks-ml-training && 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-ml-training" agent skill from https://github.com/databricks/databricks-agent-skills/tree/main/skills/databricks-ml-training into .cursor/skills/databricks-ml-training/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "databricks-ml-training", 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-ml-training--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-ml-training -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install databricks/databricks-agent-skills databricks-ml-training --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-ml-training .gemini/skills/databricks-ml-training && 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-ml-training" agent skill from https://github.com/databricks/databricks-agent-skills/tree/main/skills/databricks-ml-training into .gemini/skills/databricks-ml-training/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "databricks-ml-training", 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-ml-trainingInstalls 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-ml-training -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-ml-training .github/skills/databricks-ml-training && 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-ml-training" agent skill from https://github.com/databricks/databricks-agent-skills/tree/main/skills/databricks-ml-training into .github/skills/databricks-ml-training/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "databricks-ml-training", 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-ml-training -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-ml-training --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-ml-training .opencode/skills/databricks-ml-training && 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-ml-training" agent skill from https://github.com/databricks/databricks-agent-skills/tree/main/skills/databricks-ml-training into .opencode/skills/databricks-ml-training/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "databricks-ml-training", 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-ml-training- GitHub stars
- 345
- Token cost
- ~4.6k tokens
- SKILL.md length
- 1,241 words
- Files
- 8 (incl. references, assets)
- Skills in repo
- 32
- Repo updated
- First seen
- Licence
- Custom licence
At a glance
Train ML models on Databricks. An agent skill from databricks/databricks-agent-skills.
- : classification/regression/deep-learning (XGBoost
- SKILL.md covers Default Canonical flow, Train and register (the 90%…, Consume: batch scoring over… and Real-time serving (when…, plus 7 more sections
- Calls databricks and jq
- PyTorch) with Optuna
What it does
Databricks ML Training is an agent skill from databricks/databricks-agent-skills, published by the product's own GitHub organization. Train ML models on Databricks. Use for: classification/regression/deep-learning (XGBoost, scikit-learn, LightGBM, PyTorch) with Optuna, @prod/@challenger aliases, batch scoring (sparkudf for plain models, fe.scorebatch for feature-store-backed), custom PyFunc, custom ResponsesAgent (LangGraph + UC Function/Vector Search); UC feature tables + FeatureLookup + point-in-time joins + Lakebase online store; declarative Feature Views (createfeature, DeltaTableSource, RollingWindow/SlidingWindow/TumblingWindow…
Its SKILL.md is about 4.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 10 other files, including reference files and assets (for example `agents/openai.yaml`, `references/custom-pyfunc.md` and `references/feature-store.md`). Compatibility notes: Requires databricks CLI (= v0.294.0)
It sits in AI & LLM Engineering, covering Machine learning, Deep learning and LLM inference and serving. It works with Databricks, MLflow, LangGraph and PyTorch. 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
- : classification/regression/deep-learning (XGBoost
- PyTorch) with Optuna
- @prod/@challenger aliases
- Batch scoring (sparkudf for plain models
Example prompts
- “/databricks-ml-training”
Requirements
- Python 3
- Compatibility (from SKILL.md): Requires databricks CLI (>= v0.294.0)
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
Shell commands in SKILL.md call:
databricksjq
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
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 (>= v0.294.0)
From compatibility in the SKILL.md frontmatter.
Context cost
Databricks ML Training loads about 4.6k tokens when it runs, and up to ~20k if it reads all its reference files. Until then it costs about 171 tokens; SKILL.md has 1,241 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 1,241 words (~4,578 tokens).
“FIRST: Use the parent databricks-core skill for CLI basics, authentication, and profile selection.”
- name
- databricks-ml-training
- compatibility
- Requires databricks CLI (>= v0.294.0)
- metadata.version
- 0.1.0
- parent
- databricks-core
Files
SKILL.md and 7 other files (references, assets) in skills/databricks-ml-training of databricks/databricks-agent-skills.
- SKILL.md
- agents/openai.yaml
- assets/databricks.png
- assets/databricks.svg
- references/custom-pyfunc.md
- references/feature-store.md
- references/feature-views.md
- references/genai-agents.md
Open the folder on GitHubat commit f4fcec5
Compare with similar skills
Databricks ML Training 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 ML Training this skilldatabricks/databricks-agent-skills | 345 | — | ~4.6k | Automated safety check: Pass | Custom licence | |
| Editomegaml/omegaml | 107 | — | ~206 | Automated safety check: Pass | Apache-2.0 | |
| ML EngineerRightNow-AI/openfang | 18k | — | ~987 | Automated safety check: Pass | Apache-2.0 | |
| AI ML Engineertheneoai/awesome-skills | 183 | — | ~2.9k | Automated safety check: Pass | MIT | |
| ML Engineerdavila7/claude-code-templates | 32k | 9 repos | ~2.3k | Automated safety check: Pass | MIT | |
| ML Model Trainingsecondsky/claude-skills | 227 | 1 repos | ~1.7k | Automated safety check: Pass | MIT |
Similar skills
ML Engineer
RightNow-AI/openfang
Machine learning engineer expert for PyTorch, scikit-learn, model evaluation, and MLOps
AI ML Engineer
theneoai/awesome-skills
Expert AI/ML Engineer with deep MLOps expertise. An agent skill from theneoai/awesome-skills.
ML Engineer
davila7/claude-code-templates
Build production ML systems with PyTorch 2.x, TensorFlow, and modern ML frameworks.
ML Model Training
secondsky/claude-skills
Train ML models with scikit-learn, PyTorch, TensorFlow. An agent skill from secondsky/claude-skills.
Deep Learning
ericrisco/rsc-harness
A skill your agent uses when training or debugging a neural net in PyTorch — the forward/loss/backward/step loop and its silent bugs, mixed precision (AMP), AdamW/LR schedules, DDP/FSDP/ZeRO…
More from databricks/databricks-agent-skills
All 32 skills in this repo- Official
Databricks Dbsql
databricks/databricks-agent-skills
Databricks SQL (DBSQL) advanced features and SQL warehouse capabilities.
- Official
Databricks Synthetic Data Gen
databricks/databricks-agent-skills
Generate realistic synthetic data using Spark + Faker (strongly recommended).
- Official
Databricks Model Serving
databricks/databricks-agent-skills
Databricks Model Serving endpoint lifecycle and ops. An agent skill from databricks/databricks-agent-skills.
- Official
Databricks Python SDK
databricks/databricks-agent-skills
Databricks development guidance including Python SDK, Databricks Connect, CLI, and REST API.
- Official
Databricks Spark Structured Streaming
databricks/databricks-agent-skills
Comprehensive guide to Spark Structured Streaming for production workloads.
- Official
Databricks App Design
databricks/databricks-agent-skills
Design the UX of custom-code Databricks Apps (AppKit/React) data screens — KPI/overview pages, reports, charts, tables, and Genie/chat data assistants — mapped to concrete AppKit components.
Related
Categories
Questions about Databricks ML Training
What does Databricks ML Training do?
Train ML models on Databricks. An agent skill from databricks/databricks-agent-skills. Databricks ML Training is an agent skill from databricks/databricks-agent-skills, published by the product's own GitHub organization. Train ML models on Databricks.
When should I use Databricks ML Training?
Databricks ML Training fits situations like: : classification/regression/deep-learning (XGBoost; pyTorch) with Optuna; @prod/@challenger aliases; batch scoring (sparkudf for plain models.
How do I install Databricks ML Training in Claude Code?
Run `npx skills add databricks/databricks-agent-skills --skill databricks-ml-training -a claude-code`. Or copy the skill folder (skills/databricks-ml-training in databricks/databricks-agent-skills) into .claude/skills/databricks-ml-training in your project. Claude Code loads it when a task matches its description.
How do I install Databricks ML Training in Codex?
Run `npx skills add databricks/databricks-agent-skills --skill databricks-ml-training -a codex`. Or copy the skill folder (skills/databricks-ml-training in databricks/databricks-agent-skills) into .agents/skills/databricks-ml-training in your project. Codex loads it when a task matches its description.
Can I use Databricks ML Training 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-ml-training -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-ml-training, .gemini/skills/databricks-ml-training, .github/skills/databricks-ml-training and .opencode/skills/databricks-ml-training in your project.
What does Databricks ML Training need to run?
Going by SKILL.md and its folder, Databricks ML Training needs the command-line tools its instructions call (databricks and jq). Our summary lists: Python 3. Compatibility (from SKILL.md): Requires databricks CLI (>= v0.294.0).
Does Databricks ML Training access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Databricks ML Training 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 ML Training use?
Databricks ML Training 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 ML Training use?
About 4.6k tokens (SKILL.md is roughly 18k 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 15k tokens, read only when the agent opens those files.
What are the alternatives to Databricks ML Training?
Skills that share tags, products or a category with Databricks ML Training: Edit (omegaml/omegaml, 107 stars), ML Engineer (RightNow-AI/openfang, 18k stars), AI ML Engineer (theneoai/awesome-skills, 183 stars) and ML Engineer (davila7/claude-code-templates, 32k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Databricks ML Training?
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.