Compress natural language prompts by removing stop words while preserving semantic content.
npx skills add https://github.com/jwiegley/claude-prompts --skill caveman
Please help me install this Agent Skill. Skill: caveman Source: https://github.com/jwiegley/claude-prompts/tree/main/skills/caveman Command: npx skills add https://github.com/jwiegley/claude-prompts --skill caveman
This Skill compresses prompts by aggressively removing stop words and scaffolding while preserving semantic content, reducing token usage and improving efficiency.
Compress the sentence: This pipeline will minimize extraneous words but preserve core meaning.
High-intent search queries and answers about installing and using this skill.
Prompt compression removes non-semantic tokens like articles, auxiliary verbs, and common prepositions while preserving nouns, main verbs, adjectives, numbers, and key relationships. This reduces token usage without losing essential content or instructions.
Yes. Aggressive stop-word removal shrinks prompt length across user queries, system messages, and task instructions, lowering token consumption and improving LLM throughput while maintaining semantic fidelity.
Compression removes articles, auxiliary verbs, redundant grammar, and scaffolding. It preserves content words, domain terms, negations, time expressions, numbers, and structural relationships that carry meaning.
Use compression when token efficiency matters: long instructions, high-volume query processing, or context-limited workflows. Semantic preservation ensures model behavior remains unchanged despite reduced input length.
Yes. The technique applies across all prompt types—user queries, system messages, and task instructions—maintaining meaning while aggressively reducing extraneous words and improving processing efficiency.
shuff57Compress natural language files into caveman format to reduce input tokens.
MantisWareCache and compress prompts to reduce token consumption in LLM sessions.
devendrapratapsinghOptimize AI and LLM operations for token usage, cost, and inference.
fabis94Compress LLM instruction sets using a tiered compression workflow.
