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.
Borrowing it
Nothing to install: this file belongs to Eigenwise/atomic-agents. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Eigenwise/atomic-agents/main/AGENTS.mdgit 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/instructions/eigenwise/atomic-agents/agents-md)<a href="https://agentmods.dev/instructions/eigenwise/atomic-agents/agents-md"><img src="https://agentmods.dev/badge/instructions/eigenwise/atomic-agents/agents-md/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/instructions/eigenwise/atomic-agents/agents-md"><img src="https://agentmods.dev/badge/instructions/eigenwise/atomic-agents/agents-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.03355 | $0.03355 |
| Opus 5.5 | $0.01342 | $0.01342 |
| Sonnet 5.5 | $0.00671 | $0.00671 |
| Haiku 4.5 | $0.00335 | $0.00335 |
Grade B, and why
atomic-agents AGENTS.md scanned grade B with 1 finding 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 7d 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.
Enumerates other installed skillsmediumAgent snooping
Other skills' SKILL.md files reveal prompts, capabilities and secrets that should be invisible to peers.
- Skill convention: frontmatter `name` + `description` (optional `allowed-tools`); one skill per dir at `.claude/skills/<name>/SKILL.md` How it starts
The opening of the file, as written. The whole thing — 418 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Atomic Agents Monorepo
What is this Repository?
atomic-monorepo is the development repository for Atomic Agents, a lightweight and modular Python framework for building Agentic AI applications. The framework is built around the principle of atomicity - creating single-purpose, reusable, and composable components for AI pipelines.
Core Philosophy
Atomic Agents bridges the gap between flexibility and reliability in production AI applications by providing:
- Predictable AI Behavior: Controlled, schema-driven agent construction vs. autonomous but unpredictable multi-agent systems
- Modular Development: Build AI applications using familiar software engineering principles (LEGO-like composability)
- Type Safety: Consistent input/output contracts through Pydantic schemas
- Developer Control: Full visibility and control over AI behavior with no hidden abstractions
Built on top of Instructor (for structured LLM outputs) and Pydantic (for data validation).
Monorepo Structure
This repository contains four main packages/projects:
atomic-monorepo/
├── atomic-agents/ # Core framework library (main package)
├── atomic-assembler/ # CLI tool for managing components
├── atomic-examples/ # Example projects and use cases
├── atomic-forge/ # Collection of downloadable tools
├── docs/ # Sphinx documentation
├── guides/ # Development guides
├── .github/workflows/ # CI/CD pipelines
├── pyproject.toml # Project configuration
└── README.md # Main documentation
Package Details
1. atomic-agents/ - Core Framework
Published as: atomic-agents on PyPI (version lives in the root pyproject.toml)
Purpose: Main Python package containing all core framework components
Key Components:
agents/- AtomicAgent class and agent configurationbase/- Base abstractions (BaseIOSchema, BaseTool, BaseToolConfig)context/- ChatHistory, SystemPromptGenerator, dynamic context providersconnectors/- External integrations (MCP support)utils/- Shared utilities (token counting, formatting)
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.
- 7d ago Changed · +1 lines · +30 tokens per session 33680d955b44
- 8d ago Changed · +1 lines · +25 tokens per session e09260170a7e
- 21d ago Changed · +1 lines · +22 tokens per session da8340fe75b0
- 1mo ago First seen · 415 lines · 3,278 tokens per session scan B 2d7538b6b60b
atomic-agents AGENTS.md is an instructions file published in the GitHub repository Eigenwise/atomic-agents (6,287 stars, last pushed today), licensed MIT. It adds 3,355 tokens to every session, about $0.0134 per session on Opus 5.5. A static security scan graded it B with 1 finding (enumerates other installed skills). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other instructions, from other repositories
vscode buildNext.instructions.md
Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
next.js AGENTS.md
AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, cache eligibility and rollout and applying the notice (cutover).
langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.
vscode copilot-instructions.md
Copilot instructions for microsoft/vscode, covering vs code copilot instructions, project overview, root folders, core architecture (src/ folder) and built-in extensions (extensions/ folder).
deepseek-harness AGENTS.md
AGENTS.md instructions for deepseek-ai/deepseek-harness, covering agents.md, pre-stable apis and released session data, repository layout, commands and host sandbox failures.