End-to-end AI/ML workflows on DataRobot: train AutoML models, engineer features, deploy to production with CI/CD, monitor drift, explain predictions, and instrument external agents with OpenTelemetry observability.
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, 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. Covers the full workflow: agent design, agent_spec.md authoring, dress-rehearsal simulation via the DataRobot LLM Gateway, template-based coding, and deployment.
Guidance for setting up CI/CD pipelines for DataRobot application templates using GitLab, GitHub Actions, and Pulumi for infrastructure as code. Use when setting up CI/CD pipelines, configuring deployments, or managing infrastructure for DataRobot application templates.
Tools and guidance for data upload, dataset management, data validation, and preparing data for DataRobot projects. Use when uploading datasets, managing data, or validating data for DataRobot.
Instrument any external AI agent with OpenTelemetry to send traces, logs, and metrics to DataRobot for monitoring, observability, and governance. Use when adding observability to external agents or sending telemetry data to DataRobot.
Guidance for feature engineering, feature discovery, feature importance analysis, and understanding DataRobot's automated feature engineering capabilities. Use when working with feature engineering, feature discovery, or analyzing feature importance in DataRobot.
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DataRobot Agent Skills
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Agentic skills for DataRobot enterprise AI and agent workflows.
npx ai-agent-skills install datarobot-oss/datarobot-agent-skills
Agentic skills are modular, task-specific capability packages that help an AI agent move from general reasoning to reliable execution. Each skill bundles instructions, examples, and supporting resources so that the agent can load only what it needs for the current task, reducing context overload and improving tool use within a given workflow.
DataRobot skills are Agent Context Protocol (ACP) definitions for enterprise AI and agent workflows, including building, deploying, and governing agents, as well as AI/ML tasks such as model training, deployment, predictions, feature engineering, and monitoring. They work with major coding agents, including OpenAI Codex, Anthropic Claude Code, Google Gemini CLI, Cursor, and VS Code Copilot.
[!NOTE] "Skills" is an Anthropic term used in Claude AI and Claude Code, but the concept applies more broadly. OpenAI Codex uses
AGENTS.mdto define agent instructions, and Gemini usesgemini-extension.jsonfor extensions. This repository is compatible with all of them, and more.
[!NOTE] Supported agents for DataRobot skills include: Claude Code, Cursor, Codex, Amp, VS Code Copilot (GitHub Copilot), Gemini CLI, Goose, Letta, Kilo Code, and OpenCode.
Install all DataRobot skills, or only the ones you need, for all your AI agents with one command by using the universal skills installer.
For all skills:
npx ai-agent-skills install datarobot-oss/datarobot-agent-skills
For a specific skill:
npx ai-agent-skills install datarobot-oss/datarobot-agent-skills/skills/datarobot-predictions
For a specific agent:
npx ai-agent-skills install datarobot-oss/datarobot-agent-skills --agent cursor
npx ai-agent-skills install datarobot-oss/datarobot-agent-skills --agent claude
[!NOTE] By default, the installer copies skills to all supported agents at the same time. No configuration is required. For agent-specific installation methods, see the Installation to your coding agent section below.
Skills are self-contained folders that package instructions, scripts, and resources for a specific use case. Each folder includes a SKILL.md file with YAML frontmatter (name and description), followed by the guidance your coding agent uses while the skill is active.
[!NOTE] All DataRobot skills follow the naming convention
datarobot-<category>, where<category>describes the skill's focus area. This provides clear identification of DataRobot-specific skills, consistent naming across the skill library, and easy discovery and organization.
DataRobot skills are compatible with Claude Code, Codex, Gemini CLI, Cursor, and VS Code Copilot. Support for Windsurf and Continue is planned. Click on the section that corresponds to your coding agent to see the installation instructions.
Register the repository as a plugin marketplace:
/plugin marketplace add datarobot-oss/datarobot-agent-skills
To install a skill, run:
/plugin install <skill-folder>@datarobot-skills
For example:
/plugin install datarobot-model-training@datarobot-skills
Codex identifies the skills through the AGENTS.md file. You can verify that the instructions are loaded by running:
codex --ask-for-approval never "Summarize the current instructions."
npx claudepluginhub datarobot-oss/datarobot-agent-skills --plugin datarobot-agent-skillsBuild AutoML pipelines
Agents for data engineering, machine learning, and AI development
Skills to support Machine Learning experimentation using the Python ecosystem.
Automate ML workflows with Airflow, Kubeflow, MLflow. Use for reproducible pipelines, retraining schedules, MLOps, or encountering task failures, dependency errors, experiment tracking issues.
Agent and skill evaluation harness with MLflow integration
Skills for tracing, evaluating, and improving AI agents with MLflow. Supports the full agent improvement loop: instrument → trace → evaluate → iterate → validate.