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 →
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ljg-skill-the-one 本质提取器 — 一个 Claude Code Skill，用第一性原理从任何书籍、概念或想法中提炼出一个极简公式（The One），并通过证伪找到边界条件，生成可视化 HTML 卡片。 功能 解构→升维→形式化：从表象中剥离噪音，识别核心矛盾与动力机制，提炼不超过 5 个变量的本质公式 证伪与边界：为每个公式找到真实、尖锐的边界条件（至少 2-3 个），而非泛泛的"极端情况" 情感自适应配色：根据内容情感基调（警示/成长/智慧/财富/平衡/力量/沉稳）自动匹配主题色系 Bento Grid 可视化卡片：生成包含来源、表象、公式、核心逻辑、Mermaid 思维路径图、边界条件的完整 HTML 卡片 安装 使用 输出示例 生成的 HTML 卡片包含 6 个 Bento 区块： 卡片保存至 ，自动在浏览器中打开。 设计原则 极简：公式变量不超过 5 个，每个变量有明确现实对应物 诚实：边界条件必须尖锐真实，不说"极端情况下不成立"这种废话 美观：暗色 Bento Grid 布局，7 套情感色系自适应，hover 发光交互 License MIT
| Stars | 50 |
| Forks | 8 |
| Language | HTML |
| Category | AI Tool |
| License | MIT |
| Quality Score | 53.1738458202749/100 |
| Last Updated | 2026-02-26 |
| Created | 2026-02-26 |
| Est. Tokens | ~1k |
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ljg-skill-the-one is 本质提取器 — 用第一性原理提炼极简公式，生成可视化 HTML 卡片. It is categorized as a AI Tool with 50 GitHub stars.
ljg-skill-the-one is primarily written in HTML.
You can find installation instructions and usage details in the ljg-skill-the-one GitHub repository at github.com/lijigang/ljg-skill-the-one. The project has 50 stars and 8 forks, indicating an active community.
ljg-skill-the-one is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to ljg-skill-the-one on Agent Skills Hub include claude-code-voice-skill, maui-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.
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