datarobot-agent-skills

Plugin datarobot-agent-skills from marketplace ccode-personal-plugins in mrkhachaturov/ccode-personal-plugins. Commands read from the repo's manifest — 2 days ago.

Skills (19)

• progressive-disclosure

  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.

• 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

• datarobot-agent-assist-build

  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.

• datarobot-agent-assist-simulate

  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.

• datarobot-agent-llm-selection

  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),

• 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. Use when setting up CI/CD pipelines, configuring deployments, or managing infrastructure for DataRobot application templates.

• datarobot-connectivity-datastore-sql

  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

• datarobot-connectivity-jdbc-sql

  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-a

• datarobot-data-preparation

  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.

• datarobot-discover

  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_

• 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. Use when the user says "add tracing/observability/monitoring to my agent", wants to instrument an existing agent project in their IDE, wants t

• datarobot-feature-engineering

  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.

• datarobot-model-deployment

  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.

• datarobot-model-explainability

  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 b

• datarobot-model-monitoring

  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.

• datarobot-model-training

  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.

• 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). Use when making predictions, running batch scoring, generating prediction datasets, or explaining individual p

• datarobot-setup

  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,

• datarobot-workload-api

  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 credent

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