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claude-code-decompiled

MCP Tool

alanisme/claude-code-decompiled

πŸ” Deep research reports based on Claude Code source analysis: architecture, agent loop, tool system, system prompts, telemetry, permissions, MCP integration, context management, persistence, and hidden features.

Install

$ npx loaditout add alanisme/claude-code-decompiled

Platform-specific configuration:

.claude/settings.json
{
  "mcpServers": {
    "claude-code-decompiled": {
      "command": "npx",
      "args": [
        "-y",
        "claude-code-decompiled"
      ]
    }
  }
}

Add the config above to .claude/settings.json under the mcpServers key.

About

Claude Code Research Reports

[](https://www.npmjs.com/package/@anthropic-ai/claude-code) [](docs/)

Independent research reports on Claude Code v2.1.88, focused on architecture, agent behavior, permission design, prompt assembly, MCP integration, and context management.

This repository is intentionally documentation-only. It collects original analysis and commentary about a publicly distributed software package. It is not presented as a runnable source release or supported CLI build.

Language: English | δΈ­ζ–‡ | ζ—₯本θͺž | ν•œκ΅­μ–΄ | EspaΓ±ol

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Why This Repo Exists

Claude Code is one of the most sophisticated production AI coding agents available today. These reports aim to make its architecture and behavior easier to study:

  • Agent developers can study production patterns for tool orchestration, context management, and recovery logic.
  • Security researchers can inspect telemetry, remote control mechanisms, and permission boundaries.
  • AI researchers can examine prompt assembly, model routing, and agent loop design.
  • Engineers can use the reports as references when building CLI-based agent systems.

These reports are based on technical analysis of a publicly distributed npm package and are presented here as research commentary.

---

What This Repository Covers

This repository is designed to answer a fairly specific set of questions about Claude Code:

  • How is Claude Code architected internally?
  • How does the main agent loop actually work?
  • How are tools registered, validated, permission-checked, and executed?
  • How is the system prompt assembled from static and dynamic parts?
  • What telemetry and privacy-relevant data paths exist?
  • How does the permission model work in practice?
  • How do

Tags

ai-agentsanthropicclaude-codecode-analysismcpreverse-engineeringsecurity-researchsource-analysissource-codesystem-promptstelemetry

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Quality Signals

2
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Last updated18 days ago
Security: AREADME

Safety

Risk Levelmedium
Data Access
read
Network Accessnone

Details

Sourcegithub-crawl
Last commit4/1/2026
View on GitHub→

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