github.com/JuliusBrussee/caveman @v1.10.0@v1.9.1@v1.9.0

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 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.

[!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.

Install for one agent, or any of 30+ others

Every agent has its own path (plugin, extension, rule file, or npx skills add). The full per-agent matrix, all flags, dry-run, and uninstall live in INSTALL.md. A few common ones:

# 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

Install broke? Open your agent in this repo and say: "Read CLAUDE.md and INSTALL.md, install caveman for me." Agent read repo, agent fix own brain. Snake eat tail.

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 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.

[!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.

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.

caveman-compress receipts — real memory files, cutting input tokens forever

Every session after, that file loads ~46% smaller. Input tokens saved forever, not just one reply.

The whole cave

### Want the whole agent, not just its mouth? → caveman-code This skill shrinks what an agent **says**. **[caveman-code](https://github.com/JuliusBrussee/caveman-code)** shrinks **everything** — a full terminal coding agent, caveman top to bottom. **~2× fewer tokens than Codex** on identical tasks. 20+ providers, plan mode, autopilot goal loop, MIT. npm install -g @juliusbrussee/caveman-code [**▶ Try caveman-code →**](https://github.com/JuliusBrussee/caveman-code)

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

Also: five sibling skills, one install

JuliusBrussee/skills — works in Claude Code, Cursor, Gemini, Cline, Copilot, 40+ agents:

npx skills@latest add JuliusBrussee/skills

🦞 Teach the lobster brevity — OpenClaw integration

OpenClaw is a self-host gateway: one box, many agents inside, wired to Slack / Discord / iMessage / Telegram. Lobster strong. Lobster smart. Lobster also talk a lot.

Same installer, scoped to one agent:

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

Two things happen, no more: a caveman skill lands in the workspace,