why use many token when few do trick

Make your AI coding agent talk like a caveman.
Same answers, 65% fewer output tokens. Brain still big. Mouth small.

See it · Install · Levels · What you get · Benchmarks · Ecosystem · Caveman 2

Caveman is a skill/plugin for Claude Code, Codex, Gemini, Cursor, Windsurf, Cline, Copilot, and 30+ other agents. Install once. Agent drops the filler and answers in tight caveman-speak, keeping code, commands, and errors byte-for-byte exact. You save output tokens on every reply, forever.

Before / After

🗣️ 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.
Sure! I'd be happy to help you with that. The issue you're experiencing is most likely caused by your authentication middleware not properly validating the token expiry. Let me take a look and suggest a fix.	Bug in auth middleware. Token expiry check use < not <=. Fix:

Same fix. Third of the words. Nothing technical lost.

┌────────────────────────────────────────────┐
│   output tokens saved   █████████       65% │
│   input tokens saved    ░░░░░░░░░         0% │
│   technical accuracy    █████████      100% │
│   vibes                 █████████       OOG │
└────────────────────────────────────────────┘

Caveman no make brain smaller. Caveman make mouth smaller. Shrinks what the agent says, not what it knows.

Install

One command. Finds every agent on your machine. Installs for each.

# macOS · Linux · WSL · Git Bash
curl -fsSL https://raw.githubusercontent.com/JuliusBrussee/caveman/main/install.sh | bash

# Windows · PowerShell 5.1+
irm https://raw.githubusercontent.com/JuliusBrussee/caveman/main/install.ps1 | iex

~30 seconds. Needs Node ≥18. Skips agents you no have. Safe to re-run.

[!TIP] Turn it on: type /caveman or say "talk like caveman". Turn it off: say "normal mode". On Claude Code, Codex, and Gemini it's already on from message one. No command needed.

# Claude Code plugin
claude plugin marketplace add JuliusBrussee/caveman && claude plugin install caveman@caveman

# Gemini CLI extension
gemini extensions install https://github.com/JuliusBrussee/caveman

# Cursor / Windsurf / Cline / Codex / 30+ more, via the skills registry
npx skills add JuliusBrussee/caveman -a cursor

Pick your grunt

Six levels. Switch anytime with /caveman <level>. Level sticks until you change it or the session ends.

[!NOTE] Speak your tongue. Caveman keeps your language. Write Portuguese, caveman grunt Portuguese. Spanish, French, same. It compresses the style, never translates. wenyan mode is the exception on purpose: classical Chinese packs the most meaning per token.

What you get

[!TIP] On Claude Code the statusline shows [CAVEMAN] ⛏ 12.4k — that's your lifetime tokens saved, updated on every /caveman-stats. Silence it with CAVEMAN_STATUSLINE_SAVINGS=0.

Benchmarks

Real token counts from the Claude API. Average 65% output reduction across 10 prompts (range 22–87%), measured against default verbose replies. Output tokens only, committed and reproducible in benchmarks/ and evals/.

[!IMPORTANT] Honest number warning. Caveman only shrinks output tokens. Input and reasoning tokens are untouched, and the skill itself adds ~1–1.5k input tokens per turn. So whole-session savings run smaller than the output number, and on already-terse workloads they can go net-negative. The real win is readability and speed. Cost savings are the bonus. When caveman wins, when it loses, and how to measure it yourself: docs/HONEST-NUMBERS.md.

Turns out short isn't just cheaper. A March 2026 paper, Brevity Constraints Reverse Performance Hierarchies in Language Models, tested 31 models and found that constraining large models to brief answers improved accuracy by ~26 points on some benchmarks. Sometimes less word = more correct.

The whole cave

npm install -g @juliusbrussee/caveman-code

Five tools, one idea: agent do more with less.

npx skills@latest add JuliusBrussee/skills

curl -fsSL https://raw.githubusercontent.com/JuliusBrussee/caveman/main/install.sh | bash -s -- --only openclaw

Caveman 2

Caveman make token small. Caveman 2 make it provable.

Today's savings numbers (including /caveman-stats) are local estimates. Caveman 2 measures and verifies them across a whole team — real receipts, real dashboard, real proof the tokens went down. Building it now.

Join the waitlist → caveman.so

How it works

1. Install drops a skill file into your agent.
2. Skill tells agent: drop filler, keep substance, use fragments — but never touch code, commands, or errors.
3. On Claude Code, a hook writes a tiny flag file each session, so the agent talks caveman from message one without /caveman.
4. /caveman-stats reads your session log, counts tokens saved, writes the number to your statusline.
5. /caveman-compress rewrites memory files (like CLAUDE.md) so every future session starts with a smaller context. Save tokens forever, not just once.

Hook architecture, file ownership, and CI sync are documented for maintainers in CLAUDE.md.

Privacy

Caveman no phone home. No telemetry, no analytics, no accounts, no backend. After install, zero network calls — the skill is a prompt, the hooks are local scripts, and /caveman-stats reads a log already on your disk. Install-time fetches (GitHub plus your agents' own registries) are spelled out in SECURITY.md.

Sponsors

Caveman free forever. Sponsors keep the rock sharp.

Atlas Cloud — full-modal AI inference platform, one API.

Want your rock here? → Sponsor caveman

Star this repo

Caveman save you token, save you money. Star cost zero. Fair trade. ⭐