--- name: datarobot-agent-assist description: >- Use when the user wants to design, build, code, simulate, or deploy an AI agent (not a predictive model) to DataRobot; mentions agent_spec.md, dr-assist, datarobot-agent-assist, dress rehearsal, or the DataRobot agent template; wants to scaffold a LangGraph, CrewAI, LlamaIndex, NAT, or Base agent targeting DataRobot; wants to add an MCP server, backend API, or React frontend to a DataRobot agent application; or uses the DataRobot CLI (dr) to build or deploy an agentic custom application. Covers the full workflow: agent design, agent_spec.md authoring, dress-rehearsal simulation via the DataRobot LLM Gateway, template-based coding, and deployment. --- # 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 ``` 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. --- ## 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. --- ## 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 /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 ...` --- ## 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 ```bash 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: ```bash 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`. --- ## 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. 4. 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. --- ## 3. Deploying an AI Agent - Read `AGENTS.md` for deployment instructions - Follow the instructions **strictly** - Do not deviate without user confirmation --- ## 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: ```bash 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): ```bash python /clone_template.py ``` Clone to a specific directory: ```bash python /clone_template.py \ --target-dir ./my-project ``` ### setup_template.py Sets up a template repository for initializing a new agent project. ```bash 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. ```bash 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 --- ## 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. ```yaml 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. --- ## 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 --- ## 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](#dress-rehearsal) + `/rehearsal.py` in this skill directory | --- ## 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 --- ## DataRobot CLI Setup The DataRobot CLI (`dr`) is required for managing DataRobot custom applications. ### Verify Installation Check if the CLI is installed: ```bash dr --version ``` Expected output: `DataRobot CLI version: v0.2.66` (or similar) ### Install DataRobot CLI If not installed, run: **macOS/Linux:** ```bash curl https://cli.datarobot.com/install | sh ``` **Windows:** ```powershell irm https://cli.datarobot.com/winstall | iex ``` ### Upgrade CLI If the CLI version is too old, run to upgrade: ```bash dr self update --force ``` ### Check Authentication Status Verify the CLI is authenticated: ```bash dr auth check ``` ### Authenticate If not authenticated, run: ```bash dr auth login ``` This will guide the user through the authentication process interactively.