원클릭으로
Compress and simplify prompts to preserve meaning while reducing use of context
npx skills add https://github.com/jwiegley/claude-prompts --skill caveman이 명령을 Claude Code에 복사하여 붙여넣어 스킬을 설치하세요
Compress and simplify prompts to preserve meaning while reducing use of context
npx skills add https://github.com/jwiegley/claude-prompts --skill caveman이 명령을 Claude Code에 복사하여 붙여넣어 스킬을 설치하세요
Multi-phase, multi-model deep analysis workflow for complex problems. This skill should be used when the user wants rigorous, multi-model collaborative analysis: deep research with Opus and PAL MCP consensus (GPT-5.4-Pro + Gemini 3 Pro), strategic planning, Sonnet execution with tests, comprehensive review, and adversarial devil's advocate critique. Invoke explicitly with /forge.
Resolve NixOS issues using research and sequential thinking
Edit, analyze, and create Node-RED flows by working with flows.json files, understanding node types, and applying Node-RED best practices. Use when the user mentions Node-RED, flows.json, flow development, or needs to modify Node-RED configurations.
Translate English language text into high quality, accurate Persian (Farsi) text using a team of specialist reviewers
Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations.
Write, review, or improve SwiftUI code following best practices for state management, view composition, performance, modern APIs, Swift concurrency, and iOS 26+ Liquid Glass adoption. Use when building new SwiftUI features, refactoring existing views, reviewing code quality, or adopting modern SwiftUI patterns.
| name | caveman |
| description | Compress and simplify prompts to preserve meaning while reducing use of context |
You are a caveman compression expert. Aggressively remove all stop words and grammatical scaffolding while preserving meaning.
CORE STRATEGY: Remove articles, auxiliary verbs, and redundant words. Keep only content words that carry semantic meaning.
ALWAYS REMOVE:
ALWAYS KEEP:
BE SMART ABOUT:
EXAMPLES:
"Caveman Compression is a semantic compression method for LLM contexts" → "Caveman Compression semantic compression method LLM contexts." (Remove: is, a, for)
"It removes predictable grammar while preserving the unpredictable content" → "Removes predictable grammar preserving unpredictable content." (Remove: It, the, while → keep main meaning)
"The system was designed to process data efficiently" → "System designed process data efficiently." (Remove: The, was, to)
"There were at least 20 people" → "At least 20 people." (Keep: at least - quantifier matters)
"Made from wood and metal" → "Made from wood and metal." (Keep: from - shows material relationship)
Output ONLY the caveman compressed text, nothing else.
TEXT TO COMPRESS: {text}