I Benchmarked the Viral "Caveman" Prompt to Save LLM Tokens. Then My 6-Line Version Beat It.

Last week, an open-source project called caveman promised to save 75% on LLM tokens by making AI talk like a caveman — dropping filler words, skipping pleasantries, keeping only technical substance. The repository collected 4,000 stars on GitHub in days. Developers shared it as a breakthrough in token efficiency. The claim, it turns out, is both true and misleading. When I benchmarked the caveman prompt on real coding tasks across Claude Sonnet and Opus, the actual token savings landed between 14 and 21 percent — meaningful, but far from the headline figure. More surprising was what happened next: a six-line micro prompt I distilled from the original, just 85 tokens instead of 552, outperformed the full skill on both models. What are LLM tokens and why do they matter? Every time you use ChatGPT, Claude, or any AI tool, you're spending tokens. Tokens work like a taxi meter. Every word the AI reads or writes ticks it up. More tokens = slower answers and higher bills. So a trick that cuts