new-app

name: new-app description: Scaffold a new Atomic Agents project from scratch — create the directory, pyproject.toml, env file, first agent, and a runnable entry point. Use when the user asks to start a new atomic-agents project from scratch, says "scaffold" / "new project" / "start from zero", or runs /atomic-agents:new-app. disable-model-invocation: true argument-hint: [project-name]

New Atomic Agents Project

Scaffold a fresh Atomic Agents project. The result is a single-package Python project with one working agent, one schema pair, a provider-wrapped client, and a runnable main.py.

This skill is opinionated. Produce a complete, tested skeleton the user can run immediately.

Phase 1 — Interrogate

Ask these questions in one message, not one-at-a-time. Skip any the user already answered (including via $ARGUMENTS).

1. Project name — used as both directory name and package name. Default from $ARGUMENTS if provided. Normalize to kebab-case for the directory and snake_case for the package.
2. LLM provider — OpenAI / Anthropic / Groq / Ollama / Gemini / OpenRouter / MiniMax. Default: OpenAI.
3. Agent type — a rough one-liner. Shapes the default SystemPromptGenerator content and the starter schema pair. Defaults to a generic chat agent.
4. Tooling — uv (default, because the repo uses uv) or pip + venv.

Do not ask about project layout, Python version, or dependency list. Pick them.

Phase 2 — Confirm the plan

State the plan in one short block and wait for a yes. Include:

• Directory: <project-name>/
• Package: <project_name>/
• Python: >=3.12 (Atomic Agents uses PEP 695 generics)
• Dependencies: atomic-agents>=2.7, instructor[<provider-extra>]>=1.14, python-dotenv, rich
• Dev dependencies: pytest, pytest-asyncio, ruff
• First agent: <agent-type> — uses BasicChatInputSchema/BasicChatOutputSchema unless the agent type calls for custom schemas
• Default model for the chosen provider (see framework/references/providers.md)
• Entry point: main.py with a REPL

Phase 3 — Scaffold

Create files in this order. Verify each step before proceeding.

Directory and package

<project-name>/
├── pyproject.toml
├── .env.example
├── .gitignore
├── README.md
└── <project_name>/
    ├── __init__.py
    └── main.py

pyproject.toml

Use the template from framework/references/project-structure.md, substituting the chosen provider extra and project name.

.env.example

Include the provider's API-key variable with a placeholder. Never the real key.

.gitignore

Use the template from framework/references/project-structure.md.

<project_name>/main.py

Produce a runnable REPL. Load .env, instantiate the provider client per framework/references/providers.md, build an agent, wire a ChatHistory with a seed assistant message, loop on console.input(...).

For the agent itself, follow the workflow from the atomic-agents:create-atomic-agent skill — same canonical imports, same per-provider mode matrix, same SystemPromptGenerator shape.

When a custom agent type was requested, build custom InputSchema / OutputSchema subclasses with field description= populated, following the atomic-agents:create-atomic-schema skill. Otherwise use BasicChatInputSchema / BasicChatOutputSchema.

Always use the canonical imports:

from atomic_agents import (
    AtomicAgent, AgentConfig,
    BasicChatInputSchema, BasicChatOutputSchema,
)
from atomic_agents.context import ChatHistory, SystemPromptGenerator
from instructor import Mode

Per-provider AgentConfig knobs — match the Instructor factory mode on AgentConfig.mode:

• OpenAI: defaults work. Omit mode (or set Mode.TOOLS).
• Anthropic: mode=Mode.TOOLS; include max_tokens in model_api_parameters.
• Groq / Ollama / MiniMax: mode=Mode.JSON (Instructor factory also uses Mode.JSON).
• Gemini: assistant_role="model" and mode=Mode.GENAI_TOOLS (Instructor factory uses Mode.GENAI_TOOLS).
• OpenRouter: mode=Mode.TOOLS.

README.md

Short. Include: what the project is, how to install (uv sync or pip install -e .[dev]), how to set the API key (cp .env.example .env and edit), how to run (uv run python -m <project_name>.main or equivalent).

Phase 4 — Install and smoke-test

Execute the install step:

• uv: uv sync
• pip: python -m venv .venv && .venv/bin/pip install -e ".[dev]" (Windows: .venv\Scripts\pip)

Verify imports without a live API key:

uv run python -c "from <project_name>.main import agent; print('ok')"

If that works, the scaffold is sound. Tell the user to drop their key into .env and run the REPL.

Phase 5 — Hand off

After scaffolding, tell the user:

1. How to set their key (cp .env.example .env).
2. How to run (uv run python -m <project_name>.main).
3. Next steps, picked from:
   • Replace the starter schemas with domain-specific ones — use the atomic-agents:create-atomic-schema skill.
   • Add another agent — use the atomic-agents:create-atomic-agent skill.
   • Add a tool — use the atomic-agents:create-atomic-tool skill.
   • Add a context provider (time, user, RAG, session) — use the atomic-agents:create-atomic-context-provider skill.
   • Split into multiple agents — see framework/references/orchestration.md.
4. A pointer to framework (auto-triggered) and review (auto-triggered before commit).

Constraints

• Never commit .env. Only .env.example.
• Never install anything globally. Use the project venv.
• Never pick an old model. Default to current generation: OpenAI gpt-5-mini, Anthropic claude-haiku-4-5, Groq llama-3.3-70b-versatile, Ollama llama3.1, Gemini gemini-2.5-flash.
• Never hand-roll what framework/references/project-structure.md already templates.