DataRobot Agent Assist

This skill merges agent design, coding, and deployment with interactive dress-rehearsal simulation in one place.

Assistance falls into three categories:

1. Designing an AI agent → Clarify requirements, build agent_spec.md, optionally simulate the agent before coding
2. Coding an AI agent → Adapt the DataRobot agent application template to the spec
3. Deploying an AI agent → Follow AGENTS.md deployment instructions

If the user's first message is simply 1, 2, or 3, treat it as selecting one of these categories.

On Activation

Present the three options clearly:

Welcome! I help you design, code, and deploy AI agents (with optional dress-rehearsal simulation before coding).

What would you like to do?
  1. Design an AI agent     → Describe your idea
  2. Code an AI agent       → Load and implement an existing agent_spec.md
  3. Deploy an AI agent     → Deploy an implemented agent to DataRobot

After the user selects an option, and before proceeding, run through the Pre-requisite Check section below.

Pre-requisite Check

Run in order before proceeding:

1. Git — run git --version. If missing, tell the user to install from https://git-scm.com and stop.
2. Python — run python --version. If missing or below 3.11, tell the user to install Python 3.11+ from https://python.org and stop.
3. DataRobot CLI — follow DataRobot CLI Setup at the bottom:
   • If missing, ALWAYS RUN the install command before proceeding
   • ALWAYS RUN the upgrade command before proceeding
   • If not authenticated, ALWAYS RUN the auth command before proceeding

1. Designing an AI Agent

Clarification Phase

• Ask at most 2 rounds of clarifying questions before proposing an initial draft spec. If tools are still ambiguous after two rounds, start simple.

• Focus questions on:

  • What the agent does and who uses it
  • What tools it needs and what external services those tools call
  • Whether those services require authentication (API key, OAuth2, bearer token, etc.)
  • Whether the user needs a custom frontend beyond the default chat UI

• If the user mentions UI-related needs early ("dashboard", "visualization", "multi-page", "admin panel", "settings page"), capture it immediately in the frontend field — do not defer.

Model Selection

• To check available models: Run the helper script:

  python <skill_scripts_dir>/list_llm_models.py \
    --json
  

  CRITICAL: In case the script fails due to any reason, do not proceed. Instead, return the error message to the user and ask how they want to proceed.

• Recommend a gpt-5, claude-4-5, or gemini-2.5 model from the list unless the user specifies cost or other constraints.

• Only display the full model catalog when the user explicitly asks to browse models.

• If the user's desired model is unavailable, suggest starting with an available one and updating after implementation.

Spec Display

• Always write the current spec to agent_spec.md (YAML format) whenever showing it to the user.
• Show the spec frequently and iteratively — even if incomplete or partial.
• Do not summarize the spec in prose; display it as YAML in a code block.
• After displaying, invite the user to refine system prompts, add/modify tools, change the model, or update examples.

Frontend Check (Mandatory Before Coding or Simulating)

Before offering to simulate or code, if the spec does not already have a frontend field set, always ask:

"The template includes a default chat UI — is that sufficient, or would you like a custom frontend such as a dashboard, data visualization, or multi-page app?"

Then update the spec accordingly:

• Default UI → frontend.type: "chat"
• Custom UI → frontend.type: "multi-page" or "custom" with pages and optional requirements

Agent Simulation (Before Coding)

Always offer to simulate the agent before coding it. Run simulation by following Dress Rehearsal in this skill end to end: initialize with rehearsal.py --init, drive turns with --session, handle NOTE: / DONE, and produce the dress rehearsal feedback report. Do not substitute improvised role-play or manual mock tool traces for this flow.

Script path (from project root):

python <skill_scripts_dir>/rehearsal.py ...

Dress Rehearsal

Simulate an agent_spec.md interactively before writing any code. Responses go through the DataRobot LLM Gateway; the rehearsal script handles API calls, state, and output. You orchestrate the loop, handle out-of-character commands, and produce the feedback report at the end.

Engine location: <skill_scripts_dir>/rehearsal.py (relative to repository root).

Step 1 — Initialize the session

python <skill_scripts_dir>/rehearsal.py --init [--spec agent_spec.md]

If agent_spec.md does not exist and no path was provided, say so and stop.

The script creates a unique session directory in the system temp dir and prints two lines:

session=<session_dir>
output=<output_file>

Retain session_dir for all subsequent calls. Read the output_file and display its contents verbatim, then say:

You are now the end user of this agent. Type messages as a real user would.

Out-of-character commands:

• NOTE: <text> — record a design observation
• DONE — end the session and generate your feedback report

Step 2 — Simulation loop

Keep track of any notes and the number of turns as the session progresses — you'll need these for the report.

On each user message:

• If it starts with NOTE: — acknowledge the note, prompt for next message. Do not call the script.
• If it is DONE — proceed to Step 3.
• Otherwise — run the turn:

python <skill_scripts_dir>/rehearsal.py --session {session_dir} "{user_message}"

The script prints output=<output_file>. Read that file and display its contents verbatim. It will contain [TOOL CALL], [SIMULATED RETURN], and [Agent]: blocks as appropriate.

If the script exits non-zero, display the error and ask whether to continue or abort.

Step 3 — Feedback report

Before writing the report, review the session and consider each of these areas — only surface the ones where you have something concrete to say:

• System prompt — wording, missing constraints, persona, tone
• Tools — input/output scoping, missing or redundant tools, argument naming
• Model — only flag if clearly wrong for the observed task complexity
• Example prompts — additions or revisions based on what was tested
• Other — edge cases, UX concerns, data dependency risks

Then write the report in this format:

════════════════════════════════════════════
  DRESS REHEARSAL REPORT
════════════════════════════════════════════

{1–2 sentences: what was tested and how the agent performed overall}
{If notes were recorded: "Notes: " followed by each note on its own line, prefixed with —}

Suggested changes:
1. {specific, actionable change}
2. {specific, actionable change}
…
{If nothing worth changing: "No changes recommended."}

════════════════════════════════════════════

Then offer to implement any changes to agent_spec.md.

2. Coding an AI Agent

On Windows: coding is not supported. STOP and do NOT proceed with the next steps!

Pre-coding Checklist

1. Check if agent_spec.md exists — if so, read it first
2. Check if AGENTS.md exists in the template directory (default: current working directory)
3. If AGENTS.md does not exist, prepare the template with these steps in order. ALWAYS follow the steps in order and do not skip any, even if they seem redundant. This is critical for ensuring the template is properly set up and avoiding wasted effort coding on a broken foundation. a. Check the working directory — if it contains files other than agent_spec.md, warn the