This project provides a Claude Code skill designed to drastically reduce Large Language Model (LLM) token usage by adopting a simplified, "caveman-like" communication style. It targets developers and power users seeking to optimize LLM interactions for cost, speed, and readability without sacrificing technical accuracy. The core benefit is achieving significant token savings through concise, direct language.

How It Works

The Caveman skill acts as a post-processing layer for LLM outputs, specifically targeting Claude. It identifies and removes verbose filler phrases, pleasantries, and hedging language, replacing them with telegraphic, direct statements. This approach is supported by research suggesting that brevity constraints can sometimes improve LLM accuracy. The system offers adjustable "intensity levels" (Lite, Full, Ultra) to fine-tune the degree of compression, allowing users to balance conciseness with desired verbosity.

Quick Start & Requirements

• Primary install: npx skills add JuliusBrussee/caveman or via the Claude Code plugin system (claude plugin marketplace add JuliusBrussee/caveman).
• Prerequisites: Requires a compatible Claude Code environment or Codex.
• Links: No specific external documentation or demo links are provided beyond the installation commands.

Highlighted Details

• Achieves an average of 65% reduction in output tokens across various technical tasks, with savings ranging from 22% to 87%.
• Maintains 100% technical accuracy, preserving critical information and code blocks.
• Increases response speed by approximately 3x due to reduced token generation.
• Offers three intensity levels (Lite, Full, Ultra) for customizable verbosity.

Maintenance & Community

No specific details regarding maintainers, community channels (e.g., Discord, Slack), or project roadmap are present in the provided README.

Licensing & Compatibility

The project is released under the MIT License, indicating it is free for use and modification, including for commercial purposes.

Limitations & Caveats

The effectiveness of the "caveman" style can vary, with the project acknowledging that "sometimes full caveman too much," necessitating the use of intensity levels. The optimization applies only to output tokens, not the LLM's internal reasoning tokens.