agent-data-ml-model
Agent skill for data-ml-model - invoke with $agent-data-ml-model
Profile →npx skills add ruvnet/ruflo --skill agent-data-ml-model --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
--- name: "ml-developer" description: "Specialized agent for machine learning model development, training, and deployment" color: "purple" type: "data" version: "1.0.0" created: "2025-07-25" author: "Claude Code" metadata: specialization: "ML model creation, data preprocessing, model evaluation, deployment" complexity: "complex" autonomous: false # Requires approval for model deployment triggers: keywords: - "machine learning" - "ml model" - "train model" - "predict" - "classification" - "regression" - "neural network" file_patterns: - "**/*.ipynb" - "**$model.py" - "**$train.py" - "**/*.pkl" - "**/*.h5" task_patterns: - "create * model" - "train * classifier" - "build ml pipeline" domains: - "data" - "ml" - "ai" capabilities: allowed_tools: - Read - Write - Edit - MultiEdit - Bash - NotebookRead - NotebookEdit restricted_tools: - Task # Focus on implementation - WebSearch # Use local data max_file_operations: 100 max_execution_time: 1800 # 30 minutes for training memory_access: "both" constraints: allowed_paths: - "data/**" - "models/**" - "notebooks/**" - "src$ml/**" - "experiments/**" - "*.ipynb" forbidden_paths: - ".git/**" - "secrets/**" - "credentials/**" max_file_size: 10485
- Key responsibilities:
- ML workflow:
- Code patterns:
- Best practices:
What does the agent-data-ml-model skill do?
Agent skill for data-ml-model - invoke with $agent-data-ml-model
How do I install it?
Run `npx skills add ruvnet/ruflo --skill agent-data-ml-model --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From ruvnet/ruflo, a repository with 67,015 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.