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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Use agent to learn agent - A skeleton course on how to design, build, and operate production AI agents
| Date | Stars |
|---|---|
| 2026-07-24 | 494 |
| 2026-07-25 | 494 |
| 2026-07-28 | 494 |
| 2026-07-30 | 494 |
| 2026-07-31 | 551 |
| 2026-08-06 | 551 |
Today
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This week
+57 stars this week
This month
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Momentum
55.9
growth rate 11.54%/day
# Agentic System Course - Use Agent to Learn Agent > English · [中文](README_zh.md) **Join the [discord channel](https://discord.gg/dWSnHAFdpb) if you want to learn and build together!** --- This is a 22-chapter skeleton course on how to design, build, and operate production AI agents — written to be read with your own AI partner at your side. **An agentic system** is an AI system that can autonomously pursue goals by planning, making decisions, using tools, adapting based on feedback, having memory, etc — instead of only responding to a single prompt. Similar to [Andrej Karpathy's idea file on LLM-wiki](https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f), **this course is giving you the skeleton and your agent will help you put the muscles on it**. **This course is**: - A *skeleton* — load-bearing topics, patterns, and decisions, with trade-offs. - Written to age slowly. Framework specifics rot fast; architectural patterns do not. - A file pair (course + AGENTS.md) designed for AI consumption as much as human reading. **This course is not**: - A step-by-step tutorial. There is no walked-through project. - Tied to one stack. The course never says "use LangChain" or "use Pydantic AI." Your AI partner suggests the stack that fits your project. - A reference manual. When you need an exact API signature, ask your AI or read the docs. --- ## How to start Clone the repo, open it in your usual IDE to view course content. At the same time, point your AI agent (Claude code/Codex) at the project root, and try one of these prompts when you study a chapter: - *"Give me three real-world examples of where this matters."* - *"Suppose you are interviewing me, quiz me on this topic with five follow-up questions, easy to hard."* - *"What's a question I should be asking that I haven't?"* - *"I just read about [pattern X]. I am building [your project]. Translate the pattern into the smallest version that works in whatever language and tools fit, and explain each piece as you write it."* - *"Forget my project for a moment — show me how OpenCode (or Hermes Agent, or any leading coding agent) handles this, and what we should borrow from it."* You can also just point your agent at Ch.22's design canvas and walk through it with your specific project in mind — that's the fastest path from "I have an idea" to "I have a spec." --- ## Built-in skills ### `agentic-system-reviewer` Reviews PRDs, design docs, implementation plans, or agent code against the course. You can run this skill on **any agentic system** to get course-grounded feedback — your own project, an open-source agent you're studying, a PRD before any code exists, or a coworker's repo you want a second opinion on. The skill calibrates scope first (hobby / team tool / customer-facing), picks the chapters that matter for your archetype, reads them, and produces a findings-first report with severity, evidence, course citations, and concrete fixes — not a generic "looks good" or "add safety" review. In Claude Code, just describe what you want — *"review this against the course"*, *"is this agent design good?"*, *"what chapters does this miss?"* — while the agent is pointed at the target repo or doc. The skill auto-loads when its description matches your intent. **Codex users:** use Codex's official `skill-creator` skill to port this skill over. --- ## Course structure | Chapters | Theme | |---|---| | **Ch.00** | How to use this course with your AI partner | | **Ch.01–04** | Foundations: one tool call → the loop → tools as contract → prompts & cache | | **Ch.05–08** | Memory and state: short-term → long-term → writing & curation → persistence | | **Ch.09–11** | Coordination: planning → multi-agent delegation → the harness | | **Ch.12–14** | External surface: human-in-the-loop → connectors/MCP → skills/MCP/subagents | | **Ch.15–17** | Production scale: backend → observability → cost, latency, model strategy | | **Ch.18–19** | Quality and ops: safety/adversarial i
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:b4499fe0a94f3bac, topic:ai-agents, topic:agentic-ai, topic:agentic-workflow
matched fp:b4499fe0a94f3bac, topic:tutorial, topic:course, readme:tutorial
