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

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

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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 <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 ...

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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: <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.

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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 user and ask them to clear it before proceeding b. Clone the template: Run the helper script:

   python <skill_scripts_dir>/clone_template.py
   

   c. 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. Once the user replies, map their choice to the corresponding value (langgraph, crewai, llamaindex, nat, base) and run:

   python <skill_scripts_dir>/select_framework.py \
     --target-dir . \
     --framework <value>
   

   d. Validate the template: Run dr dependency check. If it fails, return the error message to the user DO NOT proceed. If it succeeds, continue to the next step. e. Setup the template: Run the helper script:

   python <skill_scripts_dir>/setup_template.py \
     --llm-model <model-name> \
     --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.

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

4. Recreate the TODO list based on the 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. Proactively test the agent code locally — read AGENTS.md and follow its local testing instructions to validate the implementation. Attempt this before suggesting next steps.

2. If the user asks for help testing locally:

   • Read AGENTS.md to find the exact shell command(s) required to run the agent locally
   • Display the command(s) in a code block
   • Tell the user to run the command in a new terminal in the current directory
   • Do not run the command yourself

3. After completing code generation and testing, present next steps:

   • Testing the agent locally
   • Revising aspects of the implementation
   • Deploying 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.

list_llm_models.py

Lists available LLM models from DataRobot LLM Gateway.

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

python <scripts_dir>/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 <scripts_dir>/clone_template.py

Clone to a specific directory:

python <scripts_dir>/clone_template.py \
  --target-dir ./my-project

setup_template.py

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

python <scripts_dir>/setup_template.py \
  --llm-model <model-name> \
  --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 <scripts_dir>/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 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 / read_todos	TodoWrite
show_agent_spec	Write to agent_spec.md + display as YAML
prepare_to_code	Bash (git clone + dr start)
list_available_models	WebFetch (DataRobot API)
code_research	Agent (Explore subagent)
Agent simulation (dress rehearsal)	Dress Rehearsal + <skill_scripts_dir>/rehearsal.py in this skill directory

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Behavioral Rules

• If it is unclear whether the request falls into one of the three categories, ask a clarifying question
• If the user insists on a task outside these three categories, politely decline
• If a user asks to code before designing, strongly encourage designing first
• During coding: keep responses to 1–3 sentences; no introductions or conclusions
• During design: be conversational and thorough

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DataRobot CLI Setup

The DataRobot CLI (dr) is required for managing DataRobot custom applications.

Verify Installation

Check if the CLI is installed:

dr --version

Expected output: DataRobot CLI version: v0.2.66 (or similar)

Install DataRobot CLI

If not installed, run:

macOS/Linux:

curl https://cli.datarobot.com/install | sh

Windows:

irm https://cli.datarobot.com/winstall | iex

Upgrade CLI

If the CLI version is too old, run to upgrade:

dr self update --force

Check Authentication Status

Verify the CLI is authenticated:

dr auth check

Authenticate

If not authenticated, run:

dr auth login

This will guide the user through the authentication process interactively.