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 Tomsawyerhu · Codex Skill · ★ 693
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is Chinese-WebNovel-Skill safe to install? View the security audit →
Chinese-WebNovel-Skill 这是一个面向中文网文写作的 Codex skill。 它现在的核心不是一份很长的总 prompt，而是一套 的模块化设计。 知乎回答贴: 为什么很多网文作者都非常自信，认为AI在创作领域无法取代自己？ 设计思路 这套 skill 主要分四层： 只保留全局原则、流程约束和模块路由，不再把所有写作知识堆在一个文件里。 每个高频问题拆成独立模块，单独处理教程、运行规则、正例、反例。 提供本地小说语料、结构摘录和检索索引，支持“先匹配素材，再构思，再写作”。 提供检索脚本和少量模板资产，把模块判断进一步落成可执行动作。 这样做的目的很简单： 主 skill 保持短、稳、可维护 具体问题用具体模块处理 模型先诊断问题层级，再调用对应材料 避免一个巨型 prompt 同时兼顾开头、转场、对白、逻辑、审查，最后全部变钝 架构图 下面这张图对应当前版本的整体产品架构，展示了路由层、专项模块、语料底座和统一审查闭环之间的关系。 当前模块 当前已接入这些专项模块： 模块总入口： references/modules/README.md 每个模块默认都按同一套结构组织： 个别模块会额外附带模板资产，例如 下的卷纲和章纲模板。 模块职责总览 作用：把一句简介压成题材、消费点、hook、premise、故事引擎、长度判断和第一卷方向。 位置：最上游的前置规划模块，决定“值不值得写、该怎么开”。 作用
| Stars | 693 |
| Forks | 82 |
| Language | Python |
| Category | Codex Skill |
| Quality Score | 58.5427746771585/100 |
| Open Issues | 2 |
| Last Updated | 2026-08-28 |
| Created | 2026-04-15 |
| Platforms | browser, claude-code, codex, python |
| Est. Tokens | ~15k |
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Chinese-WebNovel-Skill is 中文网文小说写作skill. It is categorized as a Codex Skill with 693 GitHub stars.
Chinese-WebNovel-Skill is primarily written in Python. It covers topics such as agent, claude-code, codex.
You can find installation instructions and usage details in the Chinese-WebNovel-Skill GitHub repository at github.com/Tomsawyerhu/Chinese-WebNovel-Skill. The project has 693 stars and 82 forks, indicating an active community.
The top alternatives to Chinese-WebNovel-Skill on Agent Skills Hub include terrashark, ui-craft, chinese-novelist-skill. 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: