Agent Data Ml Model
This is a specialized agent that handles the full ML workflow, from preprocessing data to training models and prepping them for deployment. It's scoped to work within data, models, and notebooks directories, with triggers on keywords like "train model" or "classification" and file patterns like .ipynb and .pkl files. The agent requires human approval before deploying models to production, which is smart. It comes with hooks that check for datasets and ML libraries before execution and reports on model artifacts after. The 30-minute execution limit and 100MB file size cap tell you it's meant for typical ML experiments, not massive distributed training jobs.
- skills.sh installs
- 1.2K
- GitHub stars
- 81.4K
- last updated
- Aug 21, 2026
Install
$ npx -y skills add ruvnet/ruflo --skill agent-data-ml-model --agent claude-codeInstalls into .claude/skills of the current project.
Files
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/**"
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- Repository
- github.com/ruvnet/ruflo
- Categories
- AI & Agent Building, Data Science & ML
- First seen
- Jun 3, 2026
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