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 SparkEngineAI · Codex Skill · ★ 116
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is QuantClaw-plugin safe to install? View the security audit →
QuantClaw: Precision Where It Matters for OpenClaw 中文文档 QuantClaw is a plug-and-play task-type routing quantization plugin for OpenClaw. It classifies each incoming request, maps it to a precision tier (, , or ), and routes the request to the right model target so you can balance quality, latency, and cost without asking users to choose precision manually. 🔍 About QuantClaw QuantClaw is built from quantization studies on OpenClaw workloads rather than from fixed intuition. We evaluate quantized and high-precision models across 24 task types, 104 tasks, 6 models, and scales from 9B to 744B. Result
| Stars | 116 |
| Forks | 1 |
| Language | TypeScript |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 66.6611550711523/100 |
| Last Updated | 2026-04-27 |
| Created | 2026-04-22 |
| Platforms | claude-code, codex, node |
| Est. Tokens | ~197k |
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QuantClaw-plugin is QuantClaw is a plug-and-play task-type routing quantization plugin for OpenClaw.. It is categorized as a Codex Skill with 116 GitHub stars.
QuantClaw-plugin is primarily written in TypeScript. It covers topics such as agents, claude, codex.
You can find installation instructions and usage details in the QuantClaw-plugin GitHub repository at github.com/SparkEngineAI/QuantClaw-plugin. The project has 116 stars and 1 forks, indicating an active community.
QuantClaw-plugin is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to QuantClaw-plugin on Agent Skills Hub include aeon, modsearch, agnix. 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: