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learn-claude-code

by shareAI-lab

Pending

Bash is all You need - Write a nano Claude Code 0 - 1

16,201stars
3,523forks
Python
Added 12/31/2025
AI Agentsagentagent-developmentai-agentclaudeclaude-codeeducationalllmpythonteachingtutorial
Installation
# Add to your Claude Code skills
git clone https://github.com/shareAI-lab/learn-claude-code
README.md

Learn Claude Code - Bash is all you & agent need

Python 3.10+ Tests License: MIT

Disclaimer: This is an independent educational project by shareAI Lab. It is not affiliated with, endorsed by, or sponsored by Anthropic. "Claude Code" is a trademark of Anthropic.

Learn how modern AI agents work by building one from scratch.

Chinese / 中文 | Japanese / 日本語


Why This Repository?

We created this repository out of admiration for Claude Code - what we believe to be the most capable AI coding agent in the world. Initially, we attempted to reverse-engineer its design through behavioral observation and speculation. The analysis we published was riddled with inaccuracies, unfounded guesses, and technical errors. We deeply apologize to the Claude Code team and anyone who was misled by that content.

Over the past six months, through building and iterating on real agent systems, our understanding of "what makes a true AI agent" has been fundamentally reshaped. We'd like to share these insights with you. All previous speculative content has been removed and replaced with original educational material.


Works with Kode CLI, Claude Code, Cursor, and any agent supporting the Agent Skills Spec.

<img height="400" alt="demo" src="https://github.com/user-attachments/assets/0e1e31f8-064f-4908-92ce-121e2eb8d453" />

What You'll Learn

After completing this tutorial, you will understand:

  • The Agent Loop - The surprisingly simple pattern behind all AI coding agents
  • Tool Design - How to give AI models the ability to interact with the real world
  • Explicit Planning - Using constraints to make AI behavior predictable
  • Context Management - Keeping agent memory clean through subagent isolation
  • Knowledge Injection - Loading domain expertise on-demand without retraining

Learning Path

Start Here
    |
    v
[v0: Bash Agent] -----> "One tool is enough"
    |                    16-50 lines
    v
[v1: Basic Agent] ----> "The complete agent pattern"
    |                    4 tools, ~200 lines
    v
[v2: Todo Agent] -----> "Make plans explicit"
    |                    +TodoManager, ~300 lines
    v
[v3: Subagent] -------> "Divide and conquer"
    |                    +Task tool, ~450 lines
    v
[v4: Skills Agent] ---> "Domain expertise on-demand"
                         +Skill tool, ~550 lines

Recommended approach:

  1. Read and run v0 first - understand the core loop
  2. Compare v0 and v1 - see how tools evolve
  3. Study v2 for plannin...