caveman

Compress AI-generated responses across nine linguistic intensity levels.

4|2|Updated Apr 26, 2026

Search Tags:#optimization #efficiency #compression #tokens #symbolic #terse

Ready to run

npx skills add https://github.com/IsNoobgrammer/skills-for-agents --skill caveman-isnoobgrammer

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: caveman
Source: https://github.com/IsNoobgrammer/skills-for-agents/tree/main/skills/caveman
Command: npx skills add https://github.com/IsNoobgrammer/skills-for-agents --skill caveman-isnoobgrammer

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This skill solves the issue of excessive token consumption and model verbosity by enforcing a highly compressed, terse communication style that maintains technical accuracy.

Core Features & Use Cases

Multi-Level Compression: Offers nine intensity levels ranging from professional-tight to symbolic-mathematical.
Context-Aware: Automatically applies compression to reasoning, code comments, and responses while preserving critical safety and structural integrity.
Use Case: Use this when working with high-volume API tasks or complex technical debugging where every token counts and you need to minimize fluff without sacrificing precision.

Quick Start

Invoke the caveman skill by typing /caveman full to switch to the default ultra-terse communication mode.

Frequently Asked Questions about caveman

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I reduce token usage in AI API responses without losing technical accuracy?▼

Reduce token usage by applying multi-level linguistic and symbolic compression to AI-generated responses. This approach compresses technical documentation, code comments, and internal reasoning chains across nine intensity levels to maximize information density while preserving critical safety and structural integrity.

What's the best way to compress code comments and technical documentation for high-volume API tasks?▼

Compress code comments and technical documentation for high-volume API tasks by applying context-aware linguistic compression. This enforces a highly terse communication style that minimizes fluff and maximizes information density across reasoning chains without sacrificing precision.

Does ultra-terse communication mode preserve critical safety information during technical debugging?▼

Ultra-terse communication mode preserves critical safety information by enforcing strict safety-first expansion rules for critical operations. It automatically applies compression to reasoning and responses while maintaining the structural integrity required for complex technical debugging.

Can I adjust the compression intensity for symbolic mathematical responses?▼

Adjust compression intensity for symbolic mathematical responses using nine available levels ranging from professional-tight to symbolic-mathematical. These multi-level compression options allow you to strictly control information density based on specific domain constraints.

When should I avoid using token compression on internal reasoning chains?▼

Avoid using token compression on internal reasoning chains when executing critical operations that require strict safety-first expansion rules. The system preserves structural integrity and safety constraints, but extreme symbolic compression may obscure necessary context in highly sensitive technical debugging.