JuliusBrussee/caveman

⭐ 56,983  ·  #10  ·  Python

🪨 why use many token when few token do trick — Claude Code skill that cuts 65% of tokens by talking like caveman

Python ai anthropic caveman Skill

项目分析

🎯 定位	Agent 能力增强
💡 核心价值	为 AI 编码 Agent 提供标准化的 Skills 和 Prompt 模板，覆盖特定场景（代码审查、调试、架构设计等），让 Agent 在这些场景下输出质量更高
👥 适合谁	使用 Claude Code/Cursor/Codex 等 Agent 工具的开发者，想提升 Agent 在特定任务上的表现

为什么值得关注

56,983 Stars 说明这是一个经过大量用户验证的成熟工具。使用 Python 开发。核心特色："The reason your React component is re-rendering is likely because you're creating a new object reference on each render cycle. When you pass an inline object as a prop, React's shallow comparison sees it as a different object every time, which triggers a re-render. I'd recommend using useMemo to memoize the object."。

AI 深度分析报告

Summary

Compresses LLM output tokens by 75% using a primitive language style.

Core Features

1. Multi-Level Language Compression

Offers three compression levels: Lite / Full / Ultra, along with a Classical Chinese Mode, allowing users to switch freely based on context. In Ultra mode, routine technical responses can be compressed to fewer than 20 tokens.

2. End-to-End Token Savings

Not only compresses output (~75%), but also provides an input compression tool, saving approximately 46% of input tokens per session. Additionally supports terse commits, one-line reviews, and lifetime stats.

3. Multi-Platform Plugins

Natively supports the Claude Code and Codex plugin ecosystems. Takes effect automatically upon installation with no additional configuration required. Has formed the caveman / cavemem / cavekit toolchain.

4. Benchmarking and Evaluation

Provides quantitative data on token savings and accuracy, citing an arXiv paper (2604.00025) as scientific evidence that technical accuracy remains unaffected.

Technical Architecture

• Language: Python
• Design Philosophy: Prompt Engineering + System Instruction Injection. Alters output style by modifying the LLM's System Prompt without changing model weights or inference logic.
• Code Structure: Lightweight plugin-style architecture. Core logic is a single-line System Prompt template supporting intensity parameters and mode switching. Installation scripts automatically inject into Claude Code / Codex configurations.

Quick Start Guide

# Install into Claude Code
claude code install skill JuliusBrussee/caveman

# Or clone manually
git clone https://github.com/JuliusBrussee/caveman.git
cd caveman
# Configure system prompt as per README

After installation, Claude Code automatically runs in Caveman mode without requiring additional commands.

Pros, Cons, and Use Cases

Advantages

• Significant Token Savings: Reduces output tokens by 75% and input tokens by 46%, directly lowering API costs.
• Faster Response Times: Reduced token generation leads to approximately 3x faster response times.
• Enhanced Readability: Eliminates redundant pleasantries, delivering technical answers directly.
• Zero Intrusion: Purely a Prompt-level modification that does not affect model capabilities.

Disadvantages

• Not Suitable for Non-Technical Scenarios: Inappropriate for customer-facing interactions, documentation writing, team communication, and other contexts requiring polite phrasing.
• High Barrier for Classical Chinese Mode: Difficult to understand for non-native Chinese speakers or those with weak classical Chinese foundations.
• Platform Dependent: Only supports Claude Code and Codex; not universally applicable.

Use Cases

• Individual Developers: Daily coding and debugging with a focus on efficiency.
• Small Technical Teams: Internal technical discussions and code reviews.
• API Cost-Sensitive Projects: Teams using Claude Code at scale.

Community and Popularity

• Stars: 56,983 (as of 2026-05-09), growing rapidly, demonstrating that meme-driven projects have high viral potential in the developer community.
• Last Updated: 2026-05-09, recently active with ongoing maintenance.
• Ecosystem Expansion: Has spawned sub-projects cavemem (memory enhancement) and cavekit (build tools), forming a toolchain.
• Topic Tags: Covers prompt-engineering, llm, meme, tokens, etc., blending entertainment with practicality.

This project is essentially an extreme experiment in Prompt Engineering, pushing the "less is more" philosophy to its limits. It serves both as a practical tool and a cultural satire of LLMs' tendency toward verbose output. Suitable for efficiency-obsessed geek developers.

技术信息

• 💻 语言: Python
• 📂 Topics: ai, anthropic, caveman, claude, claude-code
• 🕐 更新: 2026-05-09
• 🔗 访问 GitHub 仓库

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数据更新于 2026-05-09 · Stars 数以 GitHub 实际数据为准