Caveman is a skill/plugin for [Claude Code](https://docs.anthropic.com/en/docs/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.
> 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.
> 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.
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%](#independently-measured-jetbrains-86-tasks). Same skill, different workload — mechanism below.
> **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 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.
| *normal agent* | You should wrap the object in `useMemo`, since a new reference is created on every render. |
| `lite` | Wrap object in `useMemo`. New ref created every render. |
| `full`*(default)* | New ref each render. Wrap object in `useMemo`. |
| `ultra` | New ref/render. `useMemo` it. |
| `wenyan` | New ref every render, so wrap in `useMemo` — rendered in classical Chinese, shorter still. |
> [!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
| Command | What it does |
|---|---|
| `/caveman [lite\|full\|ultra\|wenyan]` | Compress every reply. Level sticks for the session. |
> 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`.
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/`](./benchmarks/) and [`evals/`](./evals/). This is one-question-one-answer, not a full agentic coding run — for that number, see [JetBrains](#independently-measured-jetbrains-86-tasks) 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](./docs/HONEST-NUMBERS.md)**.
JetBrains ran the skill against [86 tasks from SkillsBench](https://blog.jetbrains.com/ai/2026/07/speak-to-ai-agents-like-cavemen-tosave-tokens/) 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](#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*](https://arxiv.org/abs/2604.00025), 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.
<details>
<summary><strong>caveman-compress receipts</strong> — real memory files, cutting input tokens forever</summary>
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.
| [**grill-me**](https://github.com/JuliusBrussee/skills/tree/main/skills/grill-me) | Agent grills your plan *before* you build the wrong thing. |
| [**interface-kit**](https://github.com/JuliusBrussee/skills/tree/main/skills/interface-kit) | Build UI that looks good, loads fast, works for everyone. |
<summary><strong>🦞 Teach the lobster brevity — OpenClaw integration</strong></summary>
<br>
[**OpenClaw**](https://openclaw.ai) 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.
Two things happen, no more: a caveman skill lands in the workspace, and a tiny marker-fenced block is appended to `SOUL.md` (OpenClaw injects it every turn, so the lobster is terse from message one — no `/caveman` per session). Custom path? `OPENCLAW_WORKSPACE=/your/path`. Uninstall with the same line plus `--uninstall`; your other workspace content stays untouched. Lobster claw still sharp. Lobster mouth now small.
</details>
## 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](#independently-measured-jetbrains-86-tasks) 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**](https://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](./CLAUDE.md).
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](./SECURITY.md#privacy--telemetry).
