DataRobot - OSS

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.

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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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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.

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Use when the user wants to design, build, code, simulate, or deploy an AI agent (not a predictive model) to DataRobot; mentions agent_spec.yaml, 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. Covers the full workflow: agent design, agent_spec.yaml authoring, dress-rehearsal simulation via the DataRobot LLM Gateway, adversarial swarm simulation, template-based coding, and deployment.

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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 into .env.

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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.

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Query, explore, and update databases through existing DataRobot data connections (datastores) with the connection's stored credentials: no connection strings, no app scaffolding. Use when the user wants to list DataRobot data connections, browse schemas, tables, or columns, preview a table, or run a SQL SELECT against any database connected in DataRobot; or run INSERT/UPDATE/DELETE/MERGE/CREATE (DML/DDL) through a Snowflake, Databricks, BigQuery, SAP Datasphere, or Trino connection. Examples: "query my Snowflake data connection", "show 10 rows from SALES.ORDERS in datastore <id>", "insert yesterday's totals into daily_summary in my Databricks connection". With only a JDBC URL and credentials, use datarobot-connectivity-jdbc-sql. Creating a connection happens in the DataRobot UI.

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Run SQL against a database through DataRobot with a JDBC URL (or host, port, and database) and credentials you supply; no DataRobot data connection needed. Also use it to point an existing app such as Talk to My Data at DataRobot's low-latency /jdbcDataPreview/ route (/jdbcExecution/ only for user-approved writes). Use when the user wants to test a connection to PostgreSQL, MySQL, SQL Server, Snowflake, Databricks, Trino, SAP HANA or Datasphere, Redshift, or BigQuery, list tables or columns, run read queries, or run DML/DDL, e.g. "run this SQL on jdbc:postgresql://… through DataRobot". If the database is already a DataRobot data connection, use datarobot-connectivity-datastore-sql.

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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.

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Instrument any external or existing AI agent with OpenTelemetry to send traces, logs, and metrics to DataRobot for monitoring, observability, and governance. Use when the user says "add tracing/observability/monitoring to my agent", wants to instrument an existing agent project in their IDE, wants to send agent traces, logs, or metrics to DataRobot, or wants to record and replay agent trajectories (Tensile) from DataRobot traces.

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Use when the user wants to assess whether an AI agent repository (DataRobot-built or not) is enterprise-ready, run a gap analysis / readiness scan against security, governance, reliability, or regulatory standards, score an agent against EU AI Act requirements, or find and fix gaps (secrets, unpinned models, missing CI, missing guardrails, over-permissioned identity, etc.) before deploying an agent to production. Works on any repository, not only ones built with DataRobot.

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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.

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

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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.

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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.

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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, OR when any DataRobot task fails due to missing or invalid credentials. Covers first-time setup, re-authentication, and credential recovery.

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Use when the user wants to create, configure, scale, debug, observe, or roll out container workloads on DataRobot's Workload API. Triggers: deploy a container as a managed service; list/start/stop workloads; change replica counts or autoscaling; pick CPU/GPU compute bundles; inject DataRobot credentials as env vars; diagnose stuck/errored/crash-looping workloads (CrashLoopBackOff, ImagePullBackOff, OOMKilled, probe failures, exec format error); pull application logs/OpenTelemetry traces/metrics/request stats; create or iterate container artifacts; build images server-side; lock artifacts for production; zero-downtime rolling artifact replacement.

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

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Use when an agent assist user wants to deploy their agent, put it on DataRobot, get a link to it, or share it with others, the agent can run as a single container, and the project has no `infra/Pulumi.yaml`. Not for agents built from the agent template, which deploy with Pulumi through datarobot-agent-assist. Users usually just say "deploy" or "deploy to DataRobot" and won't mention containers, workloads or the CLI.

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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.

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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.

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