No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by JuliusBrussee · Agent Tool · ★ 109.3k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is caveman safe to install? View the security audit →
caveman why use many token when few do trick Install • Benchmarks • Before/After • Intensity Levels • Why A Claude Code skill/plugin and Codex plugin that makes agent talk like caveman — cutting 75% of tokens while keeping full technical accuracy. 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
| Stars | 109,327 |
| Forks | 6,324 |
| Language | Go |
| Category | Agent Tool |
| License | Apache-2.0 |
| Quality Score | 70.6628666014407/100 |
| Open Issues | 144 |
| Last Updated | 2026-10-03 |
| Created | 2026-04-04 |
| Platforms | claude-code, go |
| Est. Tokens | ~17k |
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caveman is 🪨 why use many token when few token do trick. Viral skill + proxy for coding agents that cuts 65% of tokens by talking like a caveman.. It is categorized as a Agent Tool with 109.3k GitHub stars.
caveman is primarily written in Go. It covers topics such as ai, anthropic, caveman.
You can find installation instructions and usage details in the caveman GitHub repository at github.com/JuliusBrussee/caveman. The project has 109.3k stars and 6324 forks, indicating an active community.
caveman is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to caveman on Agent Skills Hub include headroom, humanizer, awesome-claude-skills. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: