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datarobot-agent-assist

Design, simulate, and deploy DataRobot AI agents from an agent_spec.md workflow.

24|22|Updated Dec 14, 2025
Search Tags:#deployment#agent design#LLM gateway#dress rehearsal#DataRobot#custom applications#template coding
One-click install
npx skills add https://github.com/datarobot-oss/datarobot-agent-skills --skill datarobot-agent-assist
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: datarobot-agent-assist
Source: https://github.com/datarobot-oss/datarobot-agent-skills/tree/main/skills/datarobot-agent-assist
Command: npx skills add https://github.com/datarobot-oss/datarobot-agent-skills --skill datarobot-agent-assist

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

Teams need a repeatable way to design DataRobot agent specifications, validate them before writing code, and then move from an implemented spec to a deployed DataRobot custom application with fewer configuration mistakes.

Core Features & Use Cases

  • Agent design assistance with spec generation: Clarifies requirements (including prompts, tools, and optional frontend needs) and iteratively writes a valid agent_spec.md in YAML.
  • Optional dress-rehearsal simulation before coding: Runs an end-to-end rehearsal loop via the DataRobot LLM Gateway to test tool calls and system prompt behavior before implementation.
  • Workflow-guided coding + deployment readiness: Drives users through prerequisites (Git, Python 3.11+, DataRobot CLI), template preparation, framework selection, dependency checks, and then points to AGENTS.md for deployment.

Use case example: You want to build a DataRobot agent for “agent spec first, then implement,” including a “multi-page/custom frontend” requirement, and you want to simulate tool interactions to catch spec issues early.

Quick Start

Use the datarobot-agent-assist skill to design and validate your DataRobot agent by starting from your agent_spec idea and selecting Design an AI agent in the skill menu.

Frequently Asked Questions about datarobot-agent-assist

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I design and deploy a custom AI agent on DataRobot?▼

To design and deploy a DataRobot agent, you start by generating an agent_spec.md in YAML, optionally run a dress-rehearsal simulation via the LLM Gateway, and then use template-based coding with framework selection before following deployment instructions in AGENTS.md.

Can I test DataRobot agent tool calls and system prompts before writing code?▼

Yes, you can test DataRobot agent tool calls and system prompts before coding by running an optional dress-rehearsal simulation loop through the DataRobot LLM Gateway to validate spec behavior and catch issues early.

What prerequisites do I need to implement a DataRobot agent specification?▼

To implement a DataRobot agent specification, you need Git, Python 3.11+, DataRobot CLI authentication and setup, and must execute helper scripts for model discovery, template cloning, and framework selection.

How does an agent_spec.md workflow help build enterprise AI applications?▼

An agent_spec.md workflow provides a repeatable way to clarify requirements including prompts, tools, and frontend needs, validate them through rehearsal, and transition to a deployed custom application with fewer configuration mistakes.

Does the DataRobot LLM Gateway support simulating multi-page custom frontend requirements?▼

The DataRobot LLM Gateway supports simulating tool interactions and system prompt behavior for agent specifications, including those with multi-page or custom frontend requirements, before you begin template-based implementation.

What is the best way to avoid configuration mistakes when deploying DataRobot custom applications?▼

The best way to avoid configuration mistakes when deploying DataRobot custom applications is to follow a workflow-guided process: draft an agent_spec.md, run dress-rehearsal tests, select a framework via scripts, and use AGENTS.md for deployment instructions.

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