Inside Claude Code’s System Prompt: Agent Swarms Turn One AI into an Entire Team

A GitHub repository (Piebald-AI/claude-code-system-prompts) extracted the System Prompt of Claude Code, showing that the prompt is not a simple text but a sophisticated engineering system.

Prompt Modular Engineering

The prompt system consists of more than 40 dynamic modules. Key modules include:

Environment Awareness : loads different prompts depending on whether the user is in a terminal, IDE, or browser.

Tool Hot‑Plug : tools such as Write (file writing) and Bash (command line) have independent specifications.

Dynamic Composition : the main program assembles the most suitable prompt at runtime like building blocks.

This demonstrates that Prompt Engineering has evolved into Prompt Systems Engineering.

Agent Swarms Tactics

The leaked prompts introduce a new concept called Agent Swarms . A special tool named TeammateTool provides the following commands:

spawn : the main agent can create multiple sub‑agents based on task difficulty.

assignTask : breaks a complex task into subtasks and distributes them to different sub‑agents.

claimTask : sub‑agents can voluntarily claim tasks.

shutdown : sub‑agents dissolve after completing their work.

For example, when asked to refactor a payment module, Claude Code could split into three agents:

Agent A reads the existing code and maps the logic.

Agent B writes new test cases.

Agent C implements the code changes.

The agents communicate and review each other's work, resembling an "automatic software outsourcing team".

Memory Awakening

The new Session Search Assistant can search historical sessions using tags, titles, or Git branch names. A dialogue example illustrates the improvement:

You: "Add verification to this new page like the last login page." Previous AI : "Which login page? Please paste the code." Latest Claude : "Got it. Searching relevant sessions... Found the login-feature branch modification. I have loaded the previous style guidelines and will apply them to the new page."

This continuous memory enables AI to act as a true team collaborator.

Tool Explosion

Anthropic's Model Context Protocol (MCP) standard, released last year, is now leveraged extensively. The leaked prompts mention MCP Search and MCP through CLI . Benefits include:

Dynamic Loading : the system can discover and load required tools at runtime.

CLI Invocation : to reduce token usage, the AI can call tools via a command‑line interface instead of stuffing all data into the prompt context.

For instance, the AI may notice the user is accessing AWS S3, automatically load the AWS MCP tool, check bucket permissions, and report a security issue.

Security Review

A dedicated Security Review Agent is embedded in the prompt. Unlike a static linter, this agent behaves like a security expert, scanning code before merge for risks such as SQL injection, XSS, and permission leaks, effectively providing a senior security‑expert code review.

From Coder to Commander

The analysis concludes that programming difficulty is not reduced but shifted. Junior developers may find their niche shrinking as Agent Swarms execute tasks faster. Senior developers will become AI team commanders, focusing on task definition, acceptance criteria, and managing tireless AI workers rather than writing low‑level APIs.

These capabilities remain at the System Prompt level; whether they become reality depends on Anthropic's implementation. Nonetheless, the author predicts that by 2026 "no one will fight alone".