why use many token when few do trick Original skill made agents say less. Caveman 2 makes them read less too. 33.2% fewer provider reported input tokens in a pinned Claude Code benchmark. benchmark counterfactual Keep your agent. Brain big. Context small. See it · Install · Learn · Proxy · Pixel · Wrap · Docs · License See it 🗣️ Normal agent — 69 tokens Caveman agent — 19 tokens The reason your React component is re rendering is likely because you're creating a new object reference on each render cycle. When you pass an inline object as a prop, React's shallow comparison sees it as a different object every time, which triggers a re render. I'd recommend using useMemo to memoize the object. New object ref each render. Inline object prop = new ref = re render. Wrap in useMemo . Install Two products. Pick one or both. 1 · Save input — Caveman Proxy shrinks what your agent reads before every provider call, with byte exact recovery. BSL 1.1 runtime, MIT CLI. [安装/使用命令见下方] 2 · Save output — the skill, the original. Your agent answers in tight caveman speak while code, commands, and errors stay exact. MIT, 30+ agents. [安装/使用命令见下方] Other ways in — full installer with hooks, Windows, one agent only The full installer also wires the Claude Code hooks and statusline, finds every supported agent on your machine, and is safe to rerun (Node.js 18+): [安装/使用命令见下方] Windows (PowerShell 5.1+): [安装/使用命令见下方] One agent only: [安装/使用命令见下方] Full 30+ agent matrix, dry run, flags, verification, and uninstall: INSTALL.md. Where your tokens go You have months of agent history on disk. caveman learn reads it and scores your setup. Local, read only, no account. [安装/使用命令见下方] The report shows your Cave Score, every token sink ranked by flow with a one line fix behind each row, how deep each session ran into its context window, a replay of what the fixes would have cut from your past sessions, and a list price illustration of what the ranked sinks cost over 30 days. [安装/使用命令见下方] learn implement opens