caveman

why use many token when few do trick

Before/After • Install • Levels • Skills • Benchmarks • Evals

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A Claude Code skill/plugin and Codex plugin that makes agent talk like caveman — cutting ~75% of output tokens while keeping full technical accuracy. Now with 文言文 mode, terse commits, one-line code reviews, and a compression tool that cuts ~45% of input tokens every session.

Based on the viral observation that caveman-speak dramatically reduces LLM token usage without losing technical substance. So we made it a one-line install.

Before / After

🗣️ Normal Claude (69 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."	🪨 Caveman Claude (19 tokens) "New object ref each render. Inline object prop = new ref = re-render. Wrap in useMemo."
🗣️ Normal Claude "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."	🪨 Caveman Claude "Bug in auth middleware. Token expiry check use < not <=. Fix:"

Same fix. 75% less word. Brain still big.

Pick your level of grunt:

🪶 Lite "Your component re-renders because you create a new object reference each render. Inline object props fail shallow comparison every time. Wrap it in useMemo."

🪨 Full "New object ref each render. Inline object prop = new ref = re-render. Wrap in useMemo."

🔥 Ultra "Inline obj prop → new ref → re-render. useMemo."

📜 文言文 "物出新參照，致重繪。useMemo Wrap之。"

Same answer. You pick how many word.

┌─────────────────────────────────────┐
│  TOKENS SAVED          ████████ 75% │
│  TECHNICAL ACCURACY    ████████ 100%│
│  SPEED INCREASE        ████████ ~3x │
│  VIBES                 ████████ OOG │
└─────────────────────────────────────┘

• Faster response — less token to generate = speed go brrr
• Easier to read — no wall of text, just the answer
• Same accuracy — all technical info kept, only fluff removed (science say so)
• Save money — ~71% less output token = less cost
• Fun — every code review become comedy

Install

Claude Code (recommended)

Install as a plugin — includes skills + auto-loading hooks (caveman activates every session, mode badge tracks /caveman ultra etc.):

claude plugin marketplace add JuliusBrussee/caveman
claude plugin install caveman@caveman

Any agent (Claude Code, Cursor, Copilot, Windsurf, Cline, Codex)

npx skills add JuliusBrussee/caveman

For a specific agent: npx skills add JuliusBrussee/caveman -a cursor

Note

npx skills installs skills only (no hooks). For Claude Code auto-loading hooks, use the plugin install above or run bash hooks/install.sh.

Codex

1. Clone repo → Open Codex in repo → /plugins → Search Caveman → Install

Note

Windows Codex users: Clone repo → VS Code → Codex Settings → Plugins → find Caveman under local marketplace → Install → Reload Window. Also enable git config core.symlinks true before cloning (requires developer mode or admin).

Install once. Use in all sessions after that. One rock. That it.

Optional: Statusline Badge

Add a [CAVEMAN:ULTRA] badge to your statusline showing which mode is active. See hooks/README.md for the snippet.

Usage

Trigger with:

• /caveman or Codex $caveman
• "talk like caveman"
• "caveman mode"
• "less tokens please"

Stop with: "stop caveman" or "normal mode"

Intensity Levels

Level	Trigger	What it do
Lite	/caveman lite	Drop filler, keep grammar. Professional but no fluff
Full	/caveman full	Default caveman. Drop articles, fragments, full grunt
Ultra	/caveman ultra	Maximum compression. Telegraphic. Abbreviate everything

文言文 (Wenyan) Mode

Classical Chinese literary compression — same technical accuracy, but in the most token-efficient written language humans ever invented.

Level	Trigger	What it do
Wenyan-Lite	/caveman wenyan-lite	Semi-classical. Grammar intact, filler gone
Wenyan-Full	/caveman wenyan	Full 文言文. Maximum classical terseness
Wenyan-Ultra	/caveman wenyan-ultra	Extreme. Ancient scholar on a budget

Level stick until you change it or session end.

Caveman Skills

Skill	What it do	Trigger
caveman-commit	Terse commit messages. Conventional Commits. ≤50 char subject. Why over what.	/caveman-commit
caveman-review	One-line PR comments: L42: 🔴 bug: user null. Add guard. No throat-clearing.	/caveman-review

caveman-compress

Caveman make Claude speak with fewer tokens. Compress make Claude read fewer tokens.

Your CLAUDE.md loads on every session start. Caveman Compress rewrites memory files into caveman-speak so Claude reads less — without you losing the human-readable original.

/caveman:compress CLAUDE.md

CLAUDE.md          ← compressed (Claude reads this every session — fewer tokens)
CLAUDE.original.md ← human-readable backup (you read and edit this)

File	Original	Compressed	Saved
claude-md-preferences.md	706	285	59.6%
project-notes.md	1145	535	53.3%
claude-md-project.md	1122	687	38.8%
todo-list.md	627	388	38.1%
mixed-with-code.md	888	574	35.4%
Average	898	494	45%

Code blocks, URLs, file paths, commands, headings, dates, version numbers — anything technical passes through untouched. Only prose gets compressed. See the full caveman-compress README for details. Security note: Snyk flags this as High Risk due to subprocess/file patterns — it's a false positive.

Benchmarks

Real token counts from the Claude API (reproduce it yourself):

Task	Normal (tokens)	Caveman (tokens)	Saved
Explain React re-render bug	1180	159	87%
Fix auth middleware token expiry	704	121	83%
Set up PostgreSQL connection pool	2347	380	84%
Explain git rebase vs merge	702	292	58%
Refactor callback to async/await	387	301	22%
Architecture: microservices vs monolith	446	310	30%
Review PR for security issues	678	398	41%
Docker multi-stage build	1042	290	72%
Debug PostgreSQL race condition	1200	232	81%
Implement React error boundary	3454	456	87%
Average	1214	294	65%

Range: 22%–87% savings across prompts.

Important

Caveman only affects output tokens — thinking/reasoning tokens are untouched. Caveman no make brain smaller. Caveman make mouth smaller. Biggest win is readability and speed, cost savings are a bonus.

A March 2026 paper "Brevity Constraints Reverse Performance Hierarchies in Language Models" found that constraining large models to brief responses improved accuracy by 26 percentage points on certain benchmarks and completely reversed performance hierarchies. Verbose not always better. Sometimes less word = more correct.

Evals

Caveman not just claim 75%. Caveman prove it.

The evals/ directory has a three-arm eval harness that measures real token compression against a proper control — not just "verbose vs skill" but "terse vs skill". Because comparing caveman to verbose Claude conflate the skill with generic terseness. That cheating. Caveman not cheat.

# Run the eval (needs claude CLI)
uv run python evals/llm_run.py

# Read results (no API key, runs offline)
uv run --with tiktoken python evals/measure.py

Snapshots committed to git. CI runs free. Every number change reviewable as diff. Add a skill, add a prompt — harness pick it up automatically.

Star This Repo

If caveman save you mass token, mass money — leave mass star. ⭐

Also by Julius Brussee

• Cavekit — specification-driven development for Claude Code. Caveman language → specs → parallel builds → working software.
• Revu — local-first macOS study app with FSRS spaced repetition, decks, exams, and study guides. revu.cards

License

MIT — free like mass mammoth on open plain.