This is Token Anxiety: the slow creep of realizing that verbose AI output is not just annoying — it is expensive, it erodes your context window, and in latency-sensitive workflows, it measurably slows you down.
Enter <a href="https://github.com/juliusbrussee/caveman" target="_blank" rel="noopener nofollow">Caveman</a> — a skill/plugin for AI coding agents that compresses output to its bare functional minimum. The project’s tagline is its own product demo: “Why use many token when few token do trick.”
With 71.9k GitHub stars, 4k forks, and 38 contributors as of June 2026, this is not a toy. This article breaks down whether the compression actually holds up under technical scrutiny.
<a href="https://yisusvii.github.io/posts/caveman-token-efficiency-analysis/" target="_blank" rel="noopener nofollow">https://yisusvii.github.io/posts/caveman-token-efficiency-analysis/</a>

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Caveman is a plugin for AI coding agents that compresses LLM output to minimize token usage, reducing costs, preserving context window space, and improving latency. With nearly 72k GitHub stars and 4k forks, it has significant traction. The post evaluates whether its compression approach holds up under technical scrutiny, framing the problem as 'Token Anxiety' — the real cost of verbose AI responses in production workflows.

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