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

Project Analysis

🎯 Positioning	Agent capability enhancement
💡 Core Value	Provides standardized Skills and Prompt templates for AI coding Agents, covering specific scenarios (code review, debugging, architecture design, etc.), enabling higher quality output in these scenarios
👥 Target Audience	Developers using Agent tools like Claude Code/Cursor/Codex, looking to improve Agent performance on specific tasks

Why It's Worth Attention

56,983 Stars indicates a mature tool validated by a large user base. Developed in Python. Core feature: "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."

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

Core Features

1. Multi-Level Language Compression

Offers Lite / Full / Ultra three compression levels, plus Classical Chinese mode, allowing users to switch freely based on the scenario. In Ultra mode, typical technical responses can be compressed to under 20 tokens.

2. Full-Chain Token Savings

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

3. Multi-Platform Plugins

Natively supports Claude Code and Codex plugin ecosystems, activating automatically after installation without additional configuration. Has formed a 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, proving technical accuracy is unaffected.

Technical Architecture

• Language: Python
• Design Philosophy: Prompt Engineering + system instruction injection, altering output style by modifying the LLM's System Prompt without changing model weights or inference logic.
• Code Structure: Lightweight plugin-style architecture, with core logic being a single-line System Prompt template supporting strength parameters and mode switching. Installation script automates injection into Claude Code / Codex configuration.

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 additional commands.

Strengths, Weaknesses, and Use Cases

Strengths

• Significant Token Savings: Output tokens reduced by 75%, input by 46%, directly lowering API costs.
• Faster Response Times: Reduced token generation leads to approximately 3x faster response times.
• Enhanced Readability: Removes redundant pleasantries, directly providing technical answers.
• Zero Intrusion: Pure Prompt-level modification, does not affect model capabilities.

Weaknesses

• Not Suitable for Non-Technical Scenarios: Scenarios requiring polite language, such as customer-facing communication, documentation writing, or team discussions.
• High Barrier for Classical Chinese Mode: Difficult for non-native Chinese speakers or those with weak classical Chinese foundation to understand.
• Platform Dependent: Only supports Claude Code and Codex, not universal.

Use Cases

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

Community and Popularity

• Stars: 56,983 (as of 2026-05-09), rapid growth, indicating high virality of meme-driven projects in the developer community.
• Last Update: 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 on the verbosity of LLM outputs. Ideal for efficiency-obsessed geek developers.

Technical Information

• 💻 Language: Python
• 📂 Topics: ai, anthropic, caveman, claude, claude-code
• 🕐 Updated: 2026-01-09
• 🔗 Visit GitHub Repository

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Data updated on 2026-05-09 · Star count based on actual GitHub data