Claude Skills by datarobot-oss

Use when the user wants to design, build, code, or deploy an AI agent on DataRobot; mentions agent_spec.md, dress rehearsal, the DataRobot agent template, LangGraph, CrewAI, LlamaIndex, NAT, Base agents, MCP servers, backend APIs, custom frontends, or the DataRobot CLI.

Use when the user wants to adversarially test, evaluate, or harden an implemented AI agent before deployment; mentions swarm simulation, attack testing, persistence testing, evaluation criteria, or eval_report.md.

Use when the user wants to configure LLM integration for a DataRobot agent application. This skill helps to change LLM model, switch between the LLM integrations the project supports (LLM Gateway, a DataRobot-deployed LLM, an external provider, an LLM Blueprint, and any others its config declares), or set up provider credentials. The skill reads the project's .datarobot/cli/llm.yml for the real options, interviews the user, then runs sync_llm_env.py with the chosen values as CLI args to merge...

Use when the user wants to find DataRobot capabilities — skills, MCP servers, agents, or platform resources — for a task. Fetches the live DataRobot catalog directly so results are always current, regardless of third-party search index lag. Also checks the user's own DataRobot instance if DATAROBOT_ENDPOINT is set.

Use when the user wants to create, configure, scale, debug, observe, or roll out container workloads on DataRobot's Workload API. Triggers include: deploying a container as a managed service, listing/starting/stopping workloads, changing replica counts or autoscaling, picking CPU/GPU compute bundles, injecting DataRobot credentials as env vars, diagnosing workloads that are stuck / errored / crash-looping (CrashLoopBackOff, ImagePullBackOff, OOMKilled, probe failures, exec format error), pull...

Refactor large DataRobot skill files by moving detailed content into directly linked reference files while preserving meaning. Use when a skill triggers context-window warnings, needs progressive disclosure, or should be chunked without changing guidance.

Unified DataRobot agent workflow — design (agent_spec.md), optional dress-rehearsal simulation via built-in rehearsal engine, template-based coding, and deployment. Combines agent design guidance with interactive pre-code simulation (DataRobot LLM Gateway, feedback report). Use when the user wants to design, build, code, or deploy an AI agent for DataRobot, run a spec simulation before coding, mentions "agent spec", "datarobot-agent-assist", "dr-assist", "dress rehearsal", or is creating agen...

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.

Tools and guidance for deploying DataRobot models, managing deployments, configuring prediction environments, and deployment operations. Use when deploying models, creating or updating deployments, or configuring prediction environments.

Tools and guidance for model explainability, prediction explanations, feature impact analysis, SHAP values, and model diagnostics. Use when analyzing model explanations, feature impact, SHAP values, or diagnosing model behavior.

Tools and guidance for monitoring model performance, tracking data drift, managing model health, and detecting prediction anomalies. Use when monitoring deployed models, tracking drift, or investigating prediction anomalies.

Comprehensive guidance for training models in DataRobot, including project creation, AutoML configuration, feature engineering, and model selection. Use when training models, creating AutoML projects, or selecting models in DataRobot.

Tools and guidance for making predictions with DataRobot deployments, including real-time predictions, batch scoring, prediction dataset generation, and prediction explanations (SHAP/XEMP). Use when making predictions, running batch scoring, generating prediction datasets, or explaining individual predictions from a deployment.

Sets up DataRobot for local development including Python SDK, dr-cli, Agent Assist, and all required dependencies. Use when the user has not yet worked with DataRobot on this machine and wants to deploy agents to DataRobot, build an agent from scratch, or connect to DataRobot's APIs from a new project.