datarobot-agent-skills
NewDataRobot skills for AI/ML workflows — model training, deployment, predictions, feature engineering, monitoring, explainability, data preparation, App Framework CI/CD, and external agent monitoring.
Summary
This skill integrates DataRobot's AI/ML platform into Claude Code, enabling developers to train models, deploy them, make predictions, engineer features, monitor performance, and manage explainability directly from the terminal.
- It streamlines end-to-end ML workflows and supports App Framework CI/CD and external agent monitoring, making it essential for teams building and maintaining production AI systems.
Install & Usage
mkdir -p .claude/agentsAdd the configuration to .claude/agents/datarobot-agent-skills.md
@datarobot-agent-skillsUse Cases
Usage Examples
/datarobot-agent-skills train --project my_project --dataset data.csv --target target_column
/datarobot-agent-skills predict --model my_model_id --input new_data.csv --output predictions.csv
/datarobot-agent-skills monitor --project my_project --alert-threshold 0.05
Security Audits
Frequently Asked Questions
What is datarobot-agent-skills?
This skill integrates DataRobot's AI/ML platform into Claude Code, enabling developers to train models, deploy them, make predictions, engineer features, monitor performance, and manage explainability directly from the terminal. It streamlines end-to-end ML workflows and supports App Framework CI/CD and external agent monitoring, making it essential for teams building and maintaining production AI systems.
How to install datarobot-agent-skills?
To install datarobot-agent-skills: create the agents directory (mkdir -p .claude/agents), then add the config to .claude/agents/datarobot-agent-skills.md. Finally, @datarobot-agent-skills in Claude Code.
What is datarobot-agent-skills best for?
datarobot-agent-skills is a agent categorized under Development. It is designed for: deployment, agent. Created by DataRobot.
What can I use datarobot-agent-skills for?
datarobot-agent-skills is useful for: Train a model on a dataset and automatically deploy it to a production endpoint.; Generate predictions on new data using a deployed model and retrieve explainability insights.; Set up feature engineering pipelines and monitor model drift over time.; Manage DataRobot App Framework CI/CD pipelines for deploying AI applications.; Configure external agent monitoring to track model performance and alert on anomalies.; Prepare and upload datasets to DataRobot for automated machine learning..