DataRobot AI/ML workflows
Build on the DataRobot platform from your development environment. Use these skills to prepare data, train and explain models, deploy and monitor models, make predictions, automate DataRobot application delivery, operate workloads, and add observability to existing applications. More
Skills 14 in the ChatGPT listing
Datarobot agent assist Use when the user wants to design, build, code, simulate, or deploy an AI agent (not a predictive model) to DataRobot; mentions agent_spec.md, dr-assist, datarobot-agent-assist, dress rehearsal, swarm simulation, or the DataRobot agent template; wants to scaffold a LangGraph, CrewAI, LlamaIndex, NAT, or Base agent targeting DataRobot; wants to add an MCP server, backend API, or React frontend to a DataRobot agent application; or uses the DataRobot CLI (dr) to build or deploy an agentic custom application; or wants to harden, stress-test, or battle-test an agent. Datarobot agent llm selection Use when the user wants to configure LLM integration for a DataRobot agent application. Datarobot app framework cicd Guidance for setting up CI/CD pipelines for DataRobot application templates using GitLab, GitHub Actions, and Pulumi for infrastructure as code. Datarobot data preparation Tools and guidance for data upload, dataset management, data validation, and preparing data for DataRobot projects. Datarobot discover Use when the user wants to find DataRobot capabilities — skills, MCP servers, agents, or platform resources — for a task. Datarobot external agent monitoring Instrument any external or existing AI agent with OpenTelemetry to send traces, logs, and metrics to DataRobot for monitoring, observability, and governance. Datarobot feature engineering Guidance for feature engineering, feature discovery, feature importance analysis, and understanding DataRobot's automated feature engineering capabilities. Datarobot model deployment Tools and guidance for deploying DataRobot models, managing deployments, configuring prediction environments, and deployment operations. Datarobot model explainability Tools and guidance for model explainability, prediction explanations, feature impact analysis, SHAP values, SHAP distributions, anomaly assessment, and model diagnostics. Datarobot model monitoring Tools and guidance for monitoring model performance, tracking data drift, managing model health, and detecting prediction anomalies. Datarobot model training Comprehensive guidance for training models in DataRobot, including project creation, AutoML configuration, feature engineering, and model selection. Datarobot predictions Tools and guidance for making predictions with DataRobot deployments, including real-time predictions, batch scoring, prediction dataset generation, and prediction explanations (SHAP/XEMP). Datarobot setup Sets up DataRobot for local development including Python SDK, dr-cli, Agent Assist, and all required dependencies. Datarobot workload api Use when the user wants to create, configure, scale, debug, observe, or roll out container workloads on DataRobot's Workload API. 