🪨 why use many token when few token do trick — Claude Code skill that cuts 65% of tokens by talking like caveman
⚠️ Third-Party Software Notice
This skill is third-party open-source software developed and hosted independently on GitHub. SkillsLLM is an informational directory and does not control or maintain the underlying repository.
Any security checks, ratings, or warnings displayed by SkillsLLM are automated and limited in scope. They do not constitute a security certification or guarantee that the software is safe, error-free, or free from malicious code, vulnerabilities, compromised dependencies, or prompt-injection risks.
Review the source code, permissions, dependencies, and configuration before installing or running any third-party skill. Use is at your own risk. To the maximum extent permitted by applicable law, SkillsLLM is not liable for losses arising from third-party software.
# Add to your Claude Code skills
git clone https://github.com/JuliusBrussee/cavemanGuides for using ai agents skills like caveman.
Last scanned: 4/15/2026
{
"issues": [],
"status": "PASSED",
"scannedAt": "2026-04-15T06:03:37.350Z",
"semgrepRan": false,
"npmAuditRan": true,
"pipAuditRan": true
}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.
Same fix, fewer words. That was Caveman 1. Agent mouth got smaller. Appetite did not: tool schemas, files, logs, and history still cross the wire in full, every turn. Caveman 2 shrinks that too.
Two products. Pick one or both.
Save input with Caveman Proxy, the new release. A local proxy that shrinks what your agent reads before every provider call, with byte-exact recovery. BSL-1.1 runtime, MIT CLI.
npm install -g @caveman-ai/cli && caveman setup --install
caveman claude # or codex · gemini · aider · opencode · hermes · openclaw
Save output with the skill, the original. Your agent answers in tight caveman-speak while code, commands, and errors stay byte-for-byte exact. MIT, works in 30+ agents.
npx skills add JuliusBrussee/caveman
The full installer also wires the Claude Code hooks and statusline, finds every supported agent on your machine, and is safe to rerun (Node.js 18+):
curl -fsSL https://raw.githubusercontent.com/JuliusBrussee/caveman/v2.1.0/install.sh | bash
Windows (PowerShell 5.1+):
irm https://raw.githubusercontent.com/JuliusBrussee/caveman/v2.1.0/install.ps1 | iex
One agent only:
# Claude Code
claude plugin marketplace add JuliusBrussee/caveman && claude plugin install caveman@caveman
# Gemini CLI
gemini extensions install https://github.com/JuliusBrussee/caveman
# Codex, Cursor, Windsurf, Cline, and other skills-compatible agents
npx skills add JuliusBrussee/caveman --skill '*' -a codex --yes # replace codex with your agent profile
Full 30+ agent matrix, dry run, flags, verification, and uninstall: INSTALL.md.
Prefer building the proxy from source instead of signed binaries? scripts/install-local-cli.sh (macOS/Linux) or pwsh -File scripts/install-local-cli.ps1 (Windows); needs Go and pnpm.
You have months of agent history on disk. caveman learn reads it and scores your setup. Local, read-only, no account.
caveman learn # Claude Code + Codex + Gemini CLI + opencode; aider via CAVEMAN_AIDER_ROOT
The report shows your Cave Score, every token sink ranked by flow with a one-line fix behind each row, how deep each session ran into its context window, a replay of what the fixes would have cut from your past sessions, and a list-price illustration of what the ranked sinks cost over 30 days. Sink and cost numbers are inferred; the saved-so-far card is proxy-measured. None of it is a bill.
Every sink wears its class: safe fix (a bloated CLAUDE.md, a skill you never invoke), offload (context you re-paste every session, moved to caveman memory when recall measures cheaper), habit (numbers plus a soft suggestion, never an imperative), load-bearing (config you need, counted in the score and never touched).
caveman learn implement # hand the plan to Claude Code or Codex
The analyzer never edits your files. learn implement opens your own agent with the plan and the caveman-learn skill, which instructs it to propose each fix as a diff, apply only on your yes, re-measure, and revert anything that did not lower tokens per turn. Caveman never makes your agent dumber to make it cheaper.
Fix land? caveman learn applied <sink_id> records it. Future runs say improved, unchanged, regressed, or need more data. No fake win.
One command wraps your agent and routes provider traffic through a local proxy powered by Caveman Engine. In a pinned 54-run Claude Code benchmark it used 33.2% fewer provider-reported input tokens than direct Claude Code while passing all 18 exact-answer checks. Method, per-case results, and limits. benchmark_counterfactual
No code change, no Caveman backend: the proxy forwards each request to your chosen provider, and recovery copies stay on your disk. Claude Pro/Max OAuth credentials pass through to Anthropic as-is.
| Mode | What it does | Bytes the model sees |
|---|---|---|
default stack (caveman claude) |
Structural compression routed per content type, plus JSON tool results re-encoded as TOON when measured smaller. | Changed, recoverable |
--off |
Counts tokens and cost. Changes nothing. | Byte-identical |
--pixel |
Dense text slabs rendered to PNG pages for vision models. | Changed, recoverable |
Three rules keep it safe:
caveman_retrieve. Parse problem, store failure, or larger result sends original bytes unchanged.inferred. verified requires real traffic and eval gates; offline caveman never says it.detect() types each payload, then routes it to a compressor that keeps what answers depend on:
| Detected type | Keeps | Target |
|---|---|---|
json |
keys, structure, error/message subtrees; collapses repetitive arrays | 70–90% |
log |
errors, stack traces, first/last lines; drops INFO and progress noise | 85–95% |
code |
imports, signatures, types; elides function bodies, syntax stays valid | 40–70% |
diff |
file/hunk headers and changed lines; elides repeated context | 60–80% |
search-result |
top/bottom hits plus diagnostic/security hits | 80–95% |
text / HTML |
headings, opening/closing context, important sections | 50–80% |
All targets inferred. The code compressor uses tree-sitter (Go, Python, JS/TS) under cgo, with a pure-Go fallback that handles Go only. contextwindow.Pack() additionally fits candidate context into a token budget by BM25 relevance, recency, and error signal, returned in original order so chronology survives.
The same engine powers a set of verbs:
caveman learn # scan your real agent history → score + ranked token sinks
caveman learn implement # fix the findings with your own agent, consent-gated per edit
caveman explore install # r
caveman is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by JuliusBrussee. 🪨 why use many token when few token do trick — Claude Code skill that cuts 65% of tokens by talking like caveman. It has 99,033 GitHub stars.
Yes. caveman passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/JuliusBrussee/caveman" and add it to your Claude Code skills directory (see the Installation section above).
caveman is primarily written in Go. It is open-source under JuliusBrussee on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh caveman against similar tools.
If Caveman cuts your token bill, the next step is building agents that put those savings to work. Agentic AI for Beginners covers tool use, the ReAct pattern, memory, and deployment — 41 minutes, hands-on.
No comments yet. Be the first to share your thoughts!
Based on votes and bookmarks from developers who liked this skill