推荐一个我最近一直在用的开源项目caveman，77.3k star

能把Claude Code的对话token直接砍掉65%，靠的是让AI用原始人语法回话，省下的都是钱。我跑了个一周的活儿，账单愣是少了一半

不用改你的工作流，装成Claude Code的skill就能用，写代码、跑测试、查日志全兼容。省token还不降智商，代码质量基本没掉

纯文本规则文件，核心逻辑就几十行，想改口吻自己动手就行。嫌原始人不够狠，还能让它说废话文学，玩法随你折腾

做AI的兄弟直接fork，轮子别自己写了。 算笔账 重度Claude Code用户一个月token费300刀起步，用这个直接省出小200刀，一年下来够换个新显示器。 而且它是skill文件。 不是黑盒插件，扒开看就懂，想嵌进自己的自动化流程也容易。 你们平时咋省token的，有比我这个更野的路子吗？ 冷知识，

https://github.com/JuliusBrussee/caveman…

#AI #AI工具老炮

JuliusBrussee/caveman

Source: https://github.com/JuliusBrussee/caveman

why use many token when few do trick

Make your AI coding agent talk like a caveman.
Same answers. 65% fewer output tokens on prose,
8.5% on long-horizon agentic coding runs. 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.

That 65% is the prose number, measured on replies like the ones above. On a full agentic coding run, where most of the output is code and tool calls, it’s 8.5%. Same skill, different workload — mechanism below.

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.

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 --consent

# 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.

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

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 chat-style prompts (range 22–87%), measured against default verbose replies. Output tokens only, committed and reproducible in benchmarks/ and evals/. This is one-question-one-answer, not a full agentic coding run — for that number, see JetBrains below.

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.

Independently measured: JetBrains, 86 tasks

JetBrains ran the skill against 86 tasks from SkillsBench in July 2026 — real coding work, auto-graded by each task’s own tests, Claude Code on claude-sonnet-5, skill forced on for every reply.

Both numbers are real. They measure different workloads, and the gap is mechanical: caveman compresses narration and leaves code, diffs, tool calls, and error strings byte-exact. In a chat answer, narration is the whole reply. In an agentic run it’s the thin layer between tool calls, so that’s all there is to squeeze. An output-only skill has a low ceiling on work that is mostly not prose.

Pick the number that matches your workload:

• Agent writes you prose — explanations, review, docs, debugging walkthroughs → 65% territory.
• Agent works a repo unattended → single digits. Not zero, not 65%.

Quality was unaffected: across 86 auto-graded tasks the two arms were statistically indistinguishable. Small mouth, same brain — checked by someone who didn’t ship it.

Two things follow:

• Agentic bills are mostly input tokens, which an output-only skill cannot touch by construction. /caveman-compress and caveman-shrink chip at that side; the skill alone never will.
• The right number is your number. JetBrains had to run a full paid benchmark to find out what caveman does on their stack. That’s the job Caveman 2 exists to do — for yours, continuously.

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.

The JetBrains result is the argument for it. 65% and 8.5% are both correct, and neither one is your number — one harness, one model, one task set, and your stack is none of those. The fix is not a better README claim, ours or anyone’s. It’s a baseline on your own traffic and a receipt at the end of the month.

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. ⭐