caveman

Name: caveman
Availability: InStock
Author: tiankong0101-byte

Compress assistant responses into brief, high-density text while preserving code and error messages.

Updated May 17, 2026

Search Tags:#technical communication #token efficiency #brevity #response compression #concise response #smart caveman

Ready to run

npx skills add https://github.com/tiankong0101-byte/skills-registry --skill caveman-tiankong0101-byte

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: caveman
Source: https://github.com/tiankong0101-byte/skills-registry/tree/main/skills/caveman
Command: npx skills add https://github.com/tiankong0101-byte/skills-registry --skill caveman-tiankong0101-byte

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill helps you compress assistant responses into short, direct, high-signal language while preserving technical accuracy. It is useful when you want fewer filler words, faster reading, and lower output token usage without losing important meaning.

Core Features & Use Cases

Brevity transformation: Rewrites explanations in a smart caveman style by removing articles, pleasantries, hedging, and filler.
Technical fidelity: Keeps code blocks, error messages, and domain terms exact so the answer stays correct and actionable.
Controlled response style: Supports explicit triggers like caveman mode and stop commands like normal mode for easy switching.
Use case: A developer asks why React is re-rendering and gets a concise, high-clarity explanation instead of a long verbal preamble.

Quick Start

Ask the assistant to answer in caveman mode and keep the explanation short, direct, and technically precise.

Frequently Asked Questions about caveman

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

FAQPage Schema

How do I make ChatGPT responses shorter while keeping technical accuracy?▼

To make responses shorter while keeping technical accuracy, apply a brevity transformation that removes filler, articles, and pleasantries while preserving exact code blocks and domain terms. This ensures high-density communication without losing actionable meaning.

What is response compression for token efficiency in technical communication?▼

Response compression for token efficiency is rewriting explanations into high-signal language by stripping hedging and verbal preamble. This lowers output token usage and accelerates reading while maintaining the exact technical fidelity required for debugging.

How do I get concise explanations for code review commentary without losing context?▼

To get concise explanations for code review commentary, trigger a controlled response style that enforces brevity. This rewrites verbose feedback into direct, high-clarity statements while keeping domain terms and code snippets unchanged for accurate reviews.

Does concise response mode preserve exact error text and code blocks?▼

Yes, concise response mode preserves exact error text and code blocks. While it removes filler words and articles to achieve brevity, it strictly keeps domain terms and code unchanged so the technical answer remains correct and actionable.

When should I not use brevity transformations for assistant responses?▼

You should not use brevity transformations when the workflow requires conversational tone, detailed verbal preamble, or pleasantries. Because this response compression removes articles and hedging, it is unsuitable for contexts demanding standard conversational flow.