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analysis_claude_code

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Basic Information

This repository is a read-only research archive that documents a deep reverse-engineering study of Claude Code v1.0.33. It aggregates deobfuscated source chunks, technical analysis, verification reports and reconstruction guides created while analyzing the obfuscated Claude Code agent implementation. The stated goals are to understand the system architecture and core mechanisms, restore implementation logic, validate findings by cross-checking source locations, and provide open-source reconstruction guidance and SOPs for researchers and developers. The project contains parsed code blocks, analysis results, scripts used in preprocessing and LLM-assisted analysis, and multiple detailed design documents covering agent loops, tool execution, memory management and security. The README notes the work is intended for education and research, is not guaranteed 100% accurate, and the repository was archived and made read-only by its owner.

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App URL
https://github.com/shareAI-lab/analysis_claude_code
Github Repository
https://github.com/shareAI-lab/analysis_claude_code/blob/main/README.md

App Details

Features
The repository bundles comprehensive artifacts and technical discoveries from the reverse-engineering effort. It contains deobfuscated code split into 102 chunks, code-beautifying and splitting scripts, an LLM analysis helper, merged analysis results, and structured docs including deep dives on h2A async messaging, nO agent loop, wU2 compression, Plan and MCP integrations, and sandbox security. Core technical findings highlighted include a dual-buffer zero-latency async queue, layered multi-agent architecture with main and sub agents, a 92% threshold intelligent context compressor, a six-layer permission model and sandboxed tool execution. The tree also includes Open-Claude-Code reconstruction templates, demo repositories, validation reports claiming measured coverage and accuracy metrics, and UI integration notes covering CLI, VSCode plugin and WebSocket-based web UI.
Use Cases
This archive serves as a reference and learning resource for engineers, researchers and students studying modern AI agent architectures and reverse-engineering methods. It provides concrete artifacts and step-by-step methodologies for static preprocessing, LLM-assisted code analysis, runtime validation and integration testing, plus reusable scripts and reconstruction templates to bootstrap implementations. Practically, it offers architectural patterns for high-throughput async messaging, multi-agent scheduling, context compression strategies, secure tool execution and UI integration that teams can study and adapt. The repository is explicitly framed for academic and educational use, supplies verification reports and SOPs to improve reproducibility, and warns that analyses are not perfect; the project is archived and intended primarily as a technical reference rather than a production library.

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