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
├── AGENTS.md
├── CLAUDE.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).

AGENTS.md and CLAUDE.md

Every scaffolded project ships agent instructions so any coding assistant (Cursor, Codex, Copilot, Gemini CLI, ...) knows the framework conventions from the first commit. CLAUDE.md contains exactly one line — @AGENTS.md — so Claude Code reads the same file without duplication.

AGENTS.md template (substitute project specifics):

# <Project Name>

<One-line description from the agent-type answer.>

Built with [Atomic Agents](https://github.com/eigenwise/atomic-agents) — a schema-driven
framework on Instructor + Pydantic. Docs for LLMs:
https://eigenwise.github.io/atomic-agents/llms.txt

## Framework conventions

- Import from the top-level package: `from atomic_agents import AtomicAgent, AgentConfig,
  BaseIOSchema, BaseTool`; context pieces from `atomic_agents.context`.
- Agents are `AtomicAgent[InputSchema, OutputSchema](config=AgentConfig(...))` — the type
  parameters carry runtime information, keep them accurate.
- The LLM client must be wrapped with Instructor before it goes into `AgentConfig.client`.
- Every `BaseIOSchema` subclass needs a non-empty docstring and `Field(..., description=...)`
  on each field — both flow into the LLM prompt.
- Provider knobs (`temperature`, `max_tokens`, ...) go in `AgentConfig.model_api_parameters`.
- Provider: <chosen provider>. <Provider-specific line from the matrix below, if any.>

## Commands

- Install: `uv sync` (or the pip equivalent chosen at scaffold time)
 Run: 
 Test: 

Provider-specific lines for the template: Anthropic → "Requires max_tokens in model_api_parameters; mode=Mode.TOOLS." Gemini → "assistant_role='model' and mode=Mode.GENAI_TOOLS." Groq/Ollama/MiniMax → "mode=Mode.JSON on both the Instructor factory and AgentConfig." OpenAI/OpenRouter → omit.

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.