Claude Code Prompt Architecture Reverse-Engineered for Local Models
A GitHub repository contains a complete, legally clean reimplementation of Claude Code's prompting architecture, designed for developers building coding agents on local models.
Key Details
The repository documents the full prompting architecture that Claude Code uses, originally sourced from a brief public npm release. The author studied every prompt and used Claude itself to help rewrite the entire collection from scratch. The result is 26 prompts total covering:
- System prompt structure that actually controls behavior (not just "you are a helpful assistant")
- Tool prompts that prevent the model from using shell when a dedicated tool exists
- Safety rules that gate destructive actions without being overly restrictive
- Memory compression for long sessions (critical for smaller context windows)
- Verification patterns that catch when the model is rationalizing instead of testing
The prompts are organized into categories: system, tools, agents, memory, coordination, and utilities. The prompt patterns are model-agnostic and can be adapted for any model that supports tool use.
Legal Status
Every prompt is independently authored with different wording. The author verified no verbatim copying via automated checks. The repository includes a full legal disclaimer covering nominative fair use, non-affiliation with Anthropic, and a DMCA response policy. This is described as a clean-room style reimplementation, not a copy.
The project is MIT licensed and available at https://github.com/swati510/claude-code-prompts.
This architecture is particularly useful for building agentic workflows with Ollama, llama.cpp, or vLLM.
📖 Read the full source: r/LocalLLaMA
👀 See Also
Codesight CLI reduces AI coding agent token usage by scanning codebases
Codesight is a zero-dependency CLI tool that scans TypeScript, Python, and Go projects to generate compact context files, reducing Claude Code exploration tokens by 12.3× on average according to benchmarks from real production codebases.
Codebase Memory MCP: Graph-based code exploration for Claude Code
A developer built an MCP server that indexes codebases into a persistent knowledge graph using Tree-sitter and SQLite, reducing token usage by 20x on average for structural queries like call tracing and dead code detection.
Snip: Open-source tool reduces Claude Code token usage with YAML filters
Snip is a Go-based tool that sits between Claude Code and the shell, filtering verbose command output through declarative YAML pipelines to reduce token usage by 60-90%. It includes 16 composable pipeline actions and works with multiple AI coding agents.
HolyCode: Docker Container for Persistent Claude AI Coding Environments
HolyCode is a Docker container that maintains AI coding environment state across machine switches and rebuilds. It includes 30+ preinstalled tools, browser automation with Chromium + xvfb + Playwright, and preserves context in ./data/opencode.