RepositoryJuliusBrussee/cavemanTool
Because LLM APIs charge per token, chatty agent responses create unnecessary expense and increase round-trip latency. Caveman tackles this directly at the output prompt level with minimal operational overhead.
Caveman addresses the verbosity of coding assistants like Claude Code. In standard outputs, models often spend dozens of tokens framing a diagnosis before delivering the actual fix; Caveman strips away everything except essential context and code. Built primarily with Go for its proxy and wrapper utilities alongside structured prompt skills, it is aimed at software developers and engineering teams operating automated coding agents at scale. Despite its meme-inspired branding, its maturity is evident from over 100,000 GitHub stars, 6,000 forks, dedicated documentation, and support across dozens of agent environments.
Summary generated by AI (Gemini 3.8 Flash, 27 September 2026). It may contain errors – the original source is authoritative.
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