AI & Agents
Plugin
atlas-agents
atlas-agents is a Claude Code plugin with 13 hand-picked skills, indexed on Flowy in the AI & Agents category.
Plugin Signal59/100Fair
maintained52
capable55
documented75
adopted59
Install
$ npx -y skills add agulli/atlas-agents --agent claude-code
What's inside
Skill(13 skills)
/api-designDesign or review REST and GraphQL API interfaces. Use when asked to design an API, review endpoint structure, define request/response schemas, or improve API ergonomics./code-reviewPerform a structured security and quality audit on source code. Use when asked to review code, audit a pull request, check for vulnerabilities, or assess code quality./data-pipelineDesign, build, or debug data processing pipelines. Use when asked to process a dataset, transform data, build an ETL pipeline, schedule batch jobs, or fix data quality issues.
README
Atlas Agents — Hands-on AI Agents in Production
This repository contains the source code, examples, and project implementations for the book "Hands-on AI Agents" — a practical, code-first guide to building AI agents and, more importantly, putting them into production.
🚀 Book Overview
Where Agentic Design Patterns taught how to write agents, this book is about how to run them: the harnesses, guardrails, evals, loops, and deployment machinery that turn a demo into a system you can trust unattended. One project — Atlas, an autonomous engineering assistant — grows chapter by chapter from a fifty-line ReAct loop into a self-correcting, self-improving production system.
Key Technologies
- Claude API / OpenAI / Gemini: multi-provider agent cores with structured outputs.
- LangGraph & CrewAI: stateful agent graphs and role-based multi-agent orchestration.
- MCP & A2A: universal tool connectivity and agent-to-agent discovery.
- Claude Code & Antigravity: agentic coding harnesses and loop primitives.
- Agent Skills: declarative, progressively-disclosed expertise (
SKILL.md). - E2B / Docker sandboxes: safe code execution boundaries.
- Managed Agents: server-run sessions, outcomes, and scheduled deployments.
- LiteLLM, DSPy, Ollama: model portability, routing, and local inference.
📁 Repository Structure
Each chapter folder holds the chapter's Atlas project. Extended examples that go beyond the printed text live in each chapter's online/ subfolder.
| Folder | Chapter |
|---|---|
ch01_react_from_scratch/ | Anatomy of an Agent — the minimal ReAct loop |
ch02_prompt_architecture/ | Prompt Architecture for Agents |
ch03_tools_and_skills/ | Tools, Skills, and Structured Outputs |
ch04_handoffs/ | Handoffs and Routines — the support triage router |
ch05_state_graphs/ | Stateful Agent Graphs — LangGraph persistence and HITL |
ch06_multi_agent/ | Multi-Agent Collaboration — CrewAI and debate protocols |
ch07_model_portability/ | One Agent, Many Models — LiteLLM, Ollama, DSPy |
ch08_mcp_a2a/ | Open Protocols — MCP servers and A2A discovery |
ch09_agent_skills/ | Agent Skills — the production skill library |
ch10_claude_code_antigravity/ | Claude Code and Antigravity |
ch11_memory/ | Memory and Agentic RAG |
ch12_sandboxes/ | Code Execution and Sandbox Agents |
ch13_multimodal/ | Multimodal and Voice Agents |
ch14_guardrails/ | Guardrails and Agent Safety |
ch15_agent_harness/ | Agent Harness Engineering |
ch16_always_on_agents/ | Always-On Agents — daemons, watchdogs, recovery |
ch17_managed_agents/ | Managed Agents — let the platform run it |
ch18_evaluation/ | Evaluation and Observability |
ch19_deployment/ | Deployment, Async Agents, and Security |
ch20_loop_engineering/ | Loop Engineering — the self-correcting fix loop |
ch21_harness_engineer/ | The Harness Engineer — /learn, adversarial pairs, prose verifiers |
ch22_capstone/ | Capstone: Atlas — the Autonomous Engineering Assistant |
ch23_future/ | What's Next — scaffold optimization and reasoning benchmarks |
shared/ | Global config and declarative skill models used across chapters |
🛠️ Prerequisites
- Python 3.10+
- API keys as needed per chapter: Anthropic, OpenAI, Google Gemini (see
shared/config.py— keys load from a.envat the repo root) pip install -r requirements.txt(per-chapter extras are noted in each file's header)- Basic understanding of LLM prompting and Python
▶️ Running the Examples
Every script is self-contained and documents its own usage and dependencies in its module docstring:
cd ch20_loop_engineering
python fix_loop.py --repo ./orders-service --goal "pytest green, ruff clean"
File tree
FAQ
atlas-agents is a Claude Code plugin with 13 hand-picked skills, indexed on Flowy in the AI & Agents category. Install it with the command on its page. It includes api-design, code-review, data-pipeline. Its skills do not fire on their own yet. Request auto-invocation to have Flowy route them as you prompt. Free and open source.
- What is atlas-agents?
- This repository contains the source code, examples, and project implementations for the book "Hands-on AI Agents" — a practical, code-first guide to building AI agents and, more importantly, putting them into production.
- How do I install atlas-agents?
- Run these in Claude Code: npx -y skills add agulli/atlas-agents --agent claude-code. Then prompt normally.
- Does atlas-agents auto-invoke its skills?
- Not yet. It is indexed on Flowy as a plain plugin. Request auto-invocation on its page and Flowy will route its skills for you as you prompt.
- Is atlas-agents free?
- Yes. Flowy is free and open source, with nothing gated. You can read every skill in full before you install.
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