Agent Assist skill¶

The Agent Assist skill (datarobot-agent-assist) packages the same agent design, coding, and deployment workflows as the dr assist terminal assistant for use inside third-party coding agents. Instead of running Agent Assist in a dedicated terminal session, you install the skill into your preferred agent to drive the workflow through natural language in that environment.

The skill can be found in the DataRobot Agentic Skills repository alongside other DataRobot skills. It guides you through designing an agent, scaffolding from the Agentic Starter application template, implementing tools and code, optionally testing the implementation with Adversarial Swarm Evaluation, and deploying to DataRobot.

Install the skill¶

Install all DataRobot skills (including Agent Assist and the required datarobot-setup skill) with the universal skills installer:

npx ai-agent-skills install datarobot-oss/datarobot-agent-skills

By default, the installer copies skills to all supported coding agents on your machine. To target a specific agent, add the --agent flag:

npx ai-agent-skills install datarobot-oss/datarobot-agent-skills --agent cursor
npx ai-agent-skills install datarobot-oss/datarobot-agent-skills --agent claude

You can also install from agent-specific marketplaces or extension catalogs:

For agent-specific installation details and the full list of supported agents, see DataRobot agentic skills.

Verify installation¶

After installing, ask your coding agent What DataRobot skills do I have available?.

You should see datarobot-setup and datarobot-agent-assist listed. The skill source and helper scripts are in skills/datarobot-agent-assist/ in the repository.

Configure your environment¶

Before designing or coding an agent, run the datarobot-setup skill once per workspace. It checks Python and other dependencies, configures your DataRobot API token, and prepares a project directory.

There are two ways to trigger setup:

• Type: Run datarobot-setup
• Or use the slash command: ./datarobot-setup

Accept the prompts to complete environment configuration. If setup runs again in a later session, allow it to ensure the prerequisites are current.

Use Agent Assist in your coding agent¶

Start the Agent Assist workflow with either:

• Run datarobot-agent-assist
• ./datarobot-agent-assist

The skill presents the same three options as dr assist:

1. Design an AI agent: Clarify requirements and produce agent_spec.md.
2. Code an AI agent: Scaffold from the agent template and implement the spec.
3. Deploy an AI agent: Deploy the implemented agent to DataRobot.

Typical workflow¶

The following sequence takes you from idea to deployed agent in one session:

1. Design: Describe what you want to build in plain language. Agent Assist asks clarifying questions and writes an agent_spec.md blueprint before any code is written.
2. Rehearse (optional): Run a simulation to chat with your agent concept as an end user. When satisfied, type Done to move to coding. This design rehearsal is separate from Adversarial Swarm Evaluation, which runs after the agent is implemented.

3. Test locally: Say Let's code it. The skill handles dependencies, tools, and project structure. Watch for Lint clean and Tests passed in the output. Run the agent locally with:

   dr run dev
   

   Open the localhost URL shown in the terminal to verify the agent is running.

4. Test (optional): After coding, choose the option to test your agent. Adversarial Swarm Evaluation runs attack, behavior, and persistence tracks against the implementation, proposes patches for breaches, and writes eval_report.md. See Adversarial Swarm Evaluation.

5. Deploy: Enter Deploy my agent to publish it to DataRobot. Deployment typically takes 10 to 30 minutes; keep the session open while it runs. Ask What's the URL for my deployed agent? to retrieve the live URL.

For field definitions and examples of agent_spec.md, see Agent specification reference. For environment variables and configuration used during coding and deployment, see Environment and commands reference.

Adversarial Swarm Evaluation¶

After your agent is coded, Agent Assist can automatically test the implementation before you deploy. The coding agent offers it as a next step after implementation; use this section when you want more detail on what it tests and how the run works.

The swarm runs three tracks against your implemented agent:

When a scenario breaches, the skill proposes a minimal fix (usually a system-prompt addition, sometimes a code guard), applies approved changes, and retests. A typical full swarm run takes up to 5 minutes before any fix rounds.

Prerequisites¶

You need both of the following in the project:

• An agent_spec.md with a system_prompt.
• Implementation code (for example agent.py, myagent.py, tools.py, or app.py).

If either is missing, finish the Design and Code steps first, then return to battle-testing.

Run a swarm evaluation¶

1. Build an agent with Agent Assist in your coding tool (for example DataRobot OpenCode, Claude Code, or Cursor).
2. After the coding step, choose the option to battle-test your agent, or ask in natural language (for example, "Battle-test my agent" or "Run adversarial swarm evaluation").
3. Answer the configuration prompts. The skill asks for a user persona, optional grounding context (sample queries or real requests), how many fixing rounds to allow (default: 3), evaluation mode (standard pass/fail or scored by severity), and which model to use. If the spec includes read-only tools, you can optionally let the swarm perform actual calls for those tools instead of simulating their returns.
4. Review the generated scenarios by track. You can add or remove scenarios, ask the skill to explain any of them, then confirm to start the run.
5. Watch the swarm narrate pass, breach, and error outcomes. For each breach, approve or reject the proposed patch; approved prompt changes are applied to both agent_spec.md and the matching system prompt in the implementation so they stay in sync.
6. When convergence finishes, review eval_report.md.

The report includes pass/fail outcomes, unresolved or exhausted scenarios, readiness to deploy, and a Changes Applied list of patches made during the run. Intermediate swarm artifacts are written under .datarobot/swarm/ and removed when the report step completes. Keep eval_report.md (and evaluation_criteria.md if you want a record of the scenarios) for review before deployment.

After the report¶

From the post-run menu you can:

• Review eval_report.md for outcomes and unresolved scenarios.
• Re-run the simulation after further changes.
• Test locally with the project's usual local-run command.
• Deploy the hardened agent to DataRobot.

If any scenario is marked exhausted (could not be resolved within the fixing-round limit), address those cases before deploying. The report calls them out explicitly.

Agent Assist skill vs. dr assist¶

Both paths use the same agent_spec.md format, agent template, and deployment model. Choose the implementation that fits your workflow. Adversarial Swarm Evaluation is available through the Agent Assist skill experience in supported coding agents.