Analyze MLflow experiment results. Use when the user asks about model performance or experiment history.
EnglishCompare MLflow experiment runs, rank them, and recommend next steps. Use when the user asks about results, which run is best, what improved, what hurt performance, or wants a summary of experiment history. Triggers on "compare runs", "what improved", "best model so far", "show me results", "which hyperparameters worked".
EnglishInitialize or update a project with Databricks ML workflow scaffolding, skills, and agents. Use when starting a new ML project or adding missing Databricks integration to an existing project.
EnglishAutonomous build loop — implements, tests, reviews, analyzes, and refines. Default 6 iterations (thorough) or --fast for 4 iterations (ship it quick).
EnglishResearch ML techniques, architectures, or papers relevant to the current task and summarize actionable insights. Use when the user asks "is there a better way to do X", "what are state-of-the-art methods for Y", "find papers on Z", "how do others solve this problem", or wants to understand recent advances in a technique.
EnglishSubmit a training job to the Databricks GPU cluster, wait for results, and pull MLflow metrics back locally. Use when the user wants to train on GPU, run a full experiment, or execute code on the Databricks cluster. Triggers on "run on Databricks", "train on GPU", "submit job", "full training run".
EnglishSubmit ML training to Databricks and pull results. Use when the user wants to run, train, or execute a model on Databricks.
EnglishRun a quick training experiment locally (CPU or MPS). Use when the user wants to test a model change, debug training code, run a smoke test, or validate a new architecture before submitting to Databricks. Fast iteration, small data, short epochs.
English
AGENT SKILLS ATLASIncludes Check Results on Databricks, Compare Runs, Init Databricks Ml, and other workflows.
Claude Code
Codex
Cursor
GitHub Copilot
Gemini CLISupported by the Atlas install prompt. Skill-specific requirements vary.
Pack stats
What’s included
Check Results on Databricks
Analyze MLflow experiment results. Use when the user asks about model performance or experiment history.
Compare Runs
Compare MLflow experiment runs, rank them, and recommend next steps. Use when the user asks about results, which run is best, what improved, what hurt performance, or wants a summary of experiment history. Triggers on "compare runs", "what improved", "best model so far", "show me results", "which hyperparameters worked".
Init Databricks Ml
Initialize or update a project with Databricks ML workflow scaffolding, skills, and agents. Use when starting a new ML project or adding missing Databricks integration to an existing project.
Iterate
Autonomous build loop — implements, tests, reviews, analyzes, and refines. Default 6 iterations (thorough) or --fast for 4 iterations (ship it quick).
Research Papers
Research ML techniques, architectures, or papers relevant to the current task and summarize actionable insights. Use when the user asks "is there a better way to do X", "what are state-of-the-art methods for Y", "find papers on Z", "how do others solve this problem", or wants to understand recent advances in a technique.
Run on Databricks
Submit a training job to the Databricks GPU cluster, wait for results, and pull MLflow metrics back locally. Use when the user wants to train on GPU, run a full experiment, or execute code on the Databricks cluster. Triggers on "run on Databricks", "train on GPU", "submit job", "full training run".
Run Training on Databricks
Submit ML training to Databricks and pull results. Use when the user wants to run, train, or execute a model on Databricks.
Train Local
Run a quick training experiment locally (CPU or MPS). Use when the user wants to test a model change, debug training code, run a smoke test, or validate a new architecture before submitting to Databricks. Fast iteration, small data, short epochs.
Quick start
- Choose your skills. Select individual skills above, or choose the entire pack.
- Copy the install prompt. Use Copy install prompt above the list.
- Send it to your agent. Paste into your coding agent’s chat. Your agent reviews the source and guides the setup.
Try it with your agent
After installation, start with a request like:
Use the Check Results on Databricks skill to help with my task.
Ask me for the context you need before getting started.Requirements & license
- A coding agent that can access your project files and install skills.
- Your agent checks each skill’s instructions, dependencies, and permissions during setup.
- Reported license for the skills shown: MIT. The author’s terms apply.