Datarobot Agent Assist

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.

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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

Show this menu first. After the user selects an option (1, 2, or 3), run the [Pre-requisite Check](#pre-requisite-check) and then the [Script Path Resolution](#script-path-resolution) before doing anything else for that option.

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Script Path Resolution

Before invoking any helper script, resolve `` once for the session:

• ` is the scripts/ subdirectory of the directory containing this SKILL.md` file.
• Confirm it exists with ls /scripts/. If the directory is missing, tell the user the skill installation is incomplete and stop.
• Use the resolved absolute path for every /... reference in this skill.

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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

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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 /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.
• If none of those preferred families appear in the catalog, pick the highest-capability available model by name — prefer ones containing large, pro, opus, or sonnet over mini, haiku, or flash.
• 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)

Before transitioning to coding, ask the user (exact wording):

> "Would you like to run a dress rehearsal simulation first? (recommended)"

Wait for their reply. If they say yes, follow [Dress Rehearsal](#dress-rehearsal) end to end: initialize with rehearsal.py --init, drive turns with --session, handle NOTE: / DONE, and produce the feedback report. Do not substitute improvised role-play or manual mock tool traces. If they decline or skip, proceed directly to coding — do not simulate without explicit confirmation.

Script path: python /rehearsal.py ...

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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: /rehearsal.py (relative to repository root).

Step 1 — Initialize the session

python /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=
output=

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: — 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 /rehearsal.py --session {session_dir} "{user_message}"

The script prints output=. 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.

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2. Coding an AI Agent

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

Before Coding Begins

Verify agent_spec.md contains at minimum:

• model — a valid LLM Gateway model ID
• system_prompt — non-empty
• tools — at least one tool defined (or explicit confirmation from the user that no tools are needed)
• frontend.type — set

If agent_spec.md does not exist, inform the user and offer to run the Design phase (option 1) first. If any required field above is missing, surface the gap and update the spec before continuing. Do not start coding against an incomplete spec.

Pre-coding Checklist

1. Read agent_spec.md — it must exist (see gate above).
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 user and ask them to clear it before proceeding. b. Move agent_spec.md aside if present — if the file exists in the working directory, move it to a temp location (e.g. /tmp/agent_spec.md.bak) before cloning so it isn't overwritten. Restore it after cloning completes. c. Clone the template: Run the helper script: `` python /clone_template.py `` d. Select the agentic framework:

STOP. Do NOT proceed until the user has replied with their framework choice.

Ask the user (exact message): > Which agentic framework would you like to use? > 1. LangGraph > 2. CrewAI > 3. LlamaIndex > 4. NeMo Agent Toolkit (NAT) > 5. Base

Wait for the user's reply. Do not assume or default to any framework. If their next message is not a framework choice (silence, unrelated text), re-display the options and wait again — do not proceed with any other coding step. Once the user replies, map their choice to the corresponding value (langgraph, crewai, llamaindex, nat, base) and run: `` python /select_framework.py \ --target-dir . \ --framework ``

e. Validate the template: Run dr dependency check. Treat any non-zero exit as a hard error — do not attempt to resolve it automatically. Return the full output to the user and stop. f. Setup the template: Run the helper script. Use the model field from agent_spec.md as --llm-model; if absent, use the model selected during the design phase. `` python /setup_template.py \ --llm-model \ --target-dir . ``

CRITICAL: In case any of the above scripts fail due to any reason, do not proceed with coding. Instead, return the error message to the user and ask how they want to proceed.

g. Re-read AGENTS.md now that the template is ready.

1. Recreate the TODO list based on agent_spec.md — break down the implementation into discrete steps and add them to the TodoWrite tool.

Coding Rules

• Implement by adapting the template code — do not write from scratch
• Modify files only inside the current directory and its subdirectories
• Do not view .env files (.env.template files are OK)
• Do not add code comments unless asked
• Do not mock tool implementations unless they would be complex to implement
• For tasks with 3+ steps, use the TodoWrite tool to manage your work
• Keep text responses concise (1–3 sentences) while coding — skip preamble and postamble

File Write/Edit Discipline

• Always explain why the change is needed (purpose and impact) in 1–2 sentences before writing or editing a file
• Invoke at most one shell command per response — wait for the result before invoking another

After Coding

1. Read AGENTS.md to find the local test command.
2. Display the command in a code block.
3. Tell the user: "Run this command in a new terminal in the current directory to test the agent locally."
4. Do not run the command yourself.
5. Present next steps: revise the implementation, or deploy to DataRobot.

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3. Deploying an AI Agent

• Read AGENTS.md for deployment instructions
• Follow the instructions strictly
• Do not deviate without user confirmation

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Helper Scripts

The following are the examples of helper scripts used in the skill. They are located in the scripts directory and are designed to assist with various tasks.

listllmmodels.py

Lists available LLM models from DataRobot LLM Gateway.

Fetches and displays active models from the DataRobot LLM Gateway catalog:

python /list_llm_models.py \
  --json

Requires env vars: DATAROBOT_API_TOKEN, DATAROBOT_ENDPOINT

clone_template.py

Clones the DataRobot agent application template repository.

Clones the template to the current directory (repository URL and branch are hardcoded):

python /clone_template.py

Clone to a specific directory:

python /clone_template.py \
  --target-dir ./my-project

setup_template.py

Sets up a template repository for initializing a new agent project.

python /setup_template.py \
  --llm-model  \
  --target-dir .

select_framework.py

Saves the chosen agentic framework to .datarobot/answers/agent-agent.yml (field agent_template_framework). Preserves all other fields in the file.

python /select_framework.py \
  --framework langgraph \
  --target-dir .

Valid --framework values: langgraph, crewai, llamaindex, nat, base

Error Handling

• If a tool returns an error, read the error message carefully before responding
• For template-prep warnings: try to resolve yourself
• For template-prep errors: return the message to the user and ask how to proceed
• On unexpected errors, ask the user if they want to retry

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agent_spec.md Schema

Write specs in YAML to agent_spec.md in the working directory. Fields are optional when the spec is still evolving.

model: "anthropic/claude-sonnet-4-5-20250929"   # DataRobot LLM Gateway model ID
system_prompt: "Your agent's instructions..."
tools:
  - function_name: tool_name
    inputs:
      - arg_name: input_arg
        type: str         # one of: str, int, float, bool, list, dict
        object_schema: "(optional: schema of dict/list contents)"
    out:
      - arg_name: output_arg
        type: str
    auth_spec:
      service_name: "External API Service"
      auth_method: api_key   # api_key | oauth2 | basic_auth | bearer_token | service_account | other
examples:
  - "Example user query 1"
  - "Example user query 2"
frontend:
  type: "chat"              # chat | multi-page | custom
  pages:
    - "Analytics - shows search history and top topics"
  requirements: "(optional additional UI requirements)"

When tools require external service auth, note that credentials must be configured as runtime parameters in the infrastructure code (see AGENTS.md for the pattern).

See [references/agent-spec-examples.md](references/agent-spec-examples.md) for complete working examples.

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Tool/Helper Scripts Timeouts

• Allow up to 10 minutes for any helper script to complete before timing out and returning an error
• Allow up to 5 minutes for any tool to return a response before timing out and returning an error
• Allow up to 30 minutes for deployment-related shell commands to complete before timing out and returning an error

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Tool Mapping

Claude's built-in tools replace the plugin's custom Python tools:

| Plugin Tool | Claude Tool | |---|---| | read_file | Read | | write_file | Write | | edit_file | Edit | | shell | Bash | | list_dir | Glob or Bash (ls) | | grep_files | Grep | | glob | Glob | | web_search | WebSearch | | get_web_page | WebFetch | | write_todos

…

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: datarobot-oss
• Source: datarobot-oss/datarobot-agent-skills
• License: Apache-2.0
• Homepage: https://datarobot.com

Install and usage instructions live in the source repository linked above.