Every AI agent shares the same habit: it explains before it answers, apologizes before it fixes, and pads the sentence with courtesy nobody asked for. "Sure, I'd be happy to help with that" isn't an answer, it's filler billed by the token.
This isn't a style problem. It's a financial and operational one. Every word of padding the model generates becomes an output token, and output tokens are the expensive part of the bill. Multiply that across hundreds of interactions a day, across an entire team using Claude Code or Codex, and the API invoice grows without the actual work getting any better.
Why "just be more concise" never fixed it
Asking AI to "answer more concisely" in the prompt works for one message, maybe two. Then the model drifts back to its default, because a brevity request doesn't become a persistent rule, it dissolves into the conversation.
caveman, an open source skill built by Julius Brussee, fixes this by treating concision as an installable behavior layer instead of a request you repeat every session. Once active, it rewrites how the agent speaks, without touching how it thinks.
The mechanism behind the cut
The skill targets four specific categories of excess: articles, filler words, performative courtesy ("I'd be happy to help"), and hedging, the throat-clearing an AI does before it actually gets to the point.
Tested against real Claude API data across ten technical prompts, the result was an average 65% reduction in output tokens, ranging from 22% to 87% depending on the task. Explaining a React re-render bug, for instance, dropped from 1,180 tokens down to 159 without losing the cause or the fix.
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