caveman

Strip filler and hedging from AI responses while preserving technical accuracy.

15|5|Updated May 17, 2026
One-click install
npx skills add https://github.com/cropsgg/skills --skill caveman-cropsgg
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: caveman
Source: https://github.com/cropsgg/skills/tree/main/skills/productivity/caveman
Command: npx skills add https://github.com/cropsgg/skills --skill caveman-cropsgg

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Ultra-compressed communication mode that drops filler, hedging, and meta-commentary while preserving full technical accuracy. Reduces token usage ~75% in tight-context scenarios, enabling rapid iteration without sacrificing precision.

Core Features & Use Cases

  • Strip filler, hedging, and meta-commentary from outputs to produce crisp, signal-focused results.
  • Enforce imperative voice and concise structure (headlines, bullet lists, and explicit file/path references).
  • Use in handoffs, runbooks, technical specs, or any context with restricted tokens where raw signal is required.

Quick Start

Enable caveman mode and respond with ultra-compact, technically precise output for the given prompt.

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 LLM outputs while preserving technical accuracy?▼

Reduce token usage by stripping filler, hedging, and meta-commentary from LLM outputs. This rule-based compression enforces imperative voice and concise formatting, cutting token consumption by roughly 75% in tight-context workflows without sacrificing precision.

What is the best way to format technical runbooks for rapid iteration cycles?▼

Format technical runbooks for rapid iteration by enforcing imperative voice, concise structure, and explicit file/path references. Stripping meta-commentary and filler ensures raw signal delivery, which is critical when operating under strict token limits.

Can I use ultra-compact prompt design for tight-context technical specs?▼

Yes, ultra-compact prompt design works for tight-context technical specs. It applies rule-based stripping of filler and hedging to deliver crisp, signal-focused results, enabling rapid iteration and handoffs without exceeding token limits.

Does stripping meta-commentary from AI responses affect path-exact references?▼

Stripping meta-commentary does not affect path-exact references. The compression process preserves full technical accuracy by enforcing imperative voice and explicit file/path references while removing only filler and hedging from the output.

When should I avoid using conciseness-focused prompt compression?▼

Avoid conciseness-focused prompt compression when outputs require conversational tone, detailed explanations, or extensive hedging. The tool is designed for raw signal delivery in technical specs and runbooks, stripping meta-commentary entirely.