This article examines how Claude Code constructs its system prompts by analyzing recently leaked source code. It highlights the dynamic nature of these prompts, which are built from various components based on conditional statements. The author also outlines additional elements that contribute to context, like tool definitions and conversation history.

• Claude Code's system prompt isn't static text—it's dynamically assembled at runtime from conditional components, revealed by a recent source code leak.
• The prompt mixes always-included elements (like core instructions) with conditional ones (like tool-specific guidance) that only appear based on context.
• Beyond the system prompt itself, the full context sent to the model includes tool definitions, conversation history, attachments, and skills—all engineered together to shape responses.

The article breaks down the recently leaked source code of Anthropic's Claude Code CLI. It highlights the system's architecture, design choices, and differences from OpenAI's Codex, particularly in handling context overflow and user interactions. Key features like compaction strategies and internal versus external user instructions are explored.

• Claude Code uses a four-tiered compaction strategy (proactive token monitoring, reactive fallback, and a "snip compaction" mode for headless sessions) versus Codex's simpler diff-based approach that just minimizes data sent per turn
• The system prompt uses a boundary marker to cache roughly 3,000 tokens of static instructions across users for performance gains
• Internal users get specialized instructions specifically designed to stop the model from misrepresenting test results or giving misleading status updates
• The codebase includes feature flags and build-time checks specifically to prevent sensitive information from leaking into public builds