Atomic Agents is a lightweight, modular framework for assembling AI agents and applications from single-purpose components such as agents, tools, and context providers. It is for developers building maintainable agentic AI pipelines with reusable, composable parts and structured outputs. The catalogue entries provide agent skills, instructions, settings, and related development workflows for the framework.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Eigenwise/atomic-agents --skill new-appgit clone --depth 1 https://github.com/Eigenwise/atomic-agentsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/eigenwise/atomic-agents/new-app)<a href="https://agentmods.dev/skills/eigenwise/atomic-agents/new-app"><img src="https://agentmods.dev/badge/skills/eigenwise/atomic-agents/new-app/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/eigenwise/atomic-agents/new-app"><img src="https://agentmods.dev/badge/skills/eigenwise/atomic-agents/new-app.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00076 | $0.01981 |
| Opus 5.5 | $0.00030 | $0.00792 |
| Sonnet 5.5 | $0.00015 | $0.00396 |
| Haiku 4.5 | $0.00008 | $0.00198 |
Grade A, and why
new-app scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 171 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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).
- Project name — used as both directory name and package name. Default from
$ARGUMENTSif provided. Normalize tokebab-casefor the directory andsnake_casefor the package. - LLM provider — OpenAI / Anthropic / Groq / Ollama / Gemini / OpenRouter / MiniMax. Default: OpenAI.
- Agent type — a rough one-liner. Shapes the default
SystemPromptGeneratorcontent and the starter schema pair. Defaults to a generic chat agent. - Tooling —
uv(default, because the repo uses uv) orpip + 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>— usesBasicChatInputSchema/BasicChatOutputSchemaunless the agent type calls for custom schemas - Default model for the chosen provider (see
framework/references/providers.md) - Entry point:
main.pywith 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.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 171 lines · 76 tokens per session scan A a425bb1fa43d
new-app is a skill published in the GitHub repository Eigenwise/atomic-agents (6,287 stars, last pushed yesterday), licensed MIT. It adds 76 tokens to every session and 1,981 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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