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 Kianzzz · Agent Tool · ★ 206
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is book-sales-video safe to install? View the security audit →
图书带货视频 Skill 一个面向 Codex/Agents 的中文 Skill：从书名或飞书多维表格中的成稿文案出发，完成书籍核验、语义分镜、豆包配音、Pexels 开场、AI 配图、双语字幕和 OpenChatCut 可编辑视频初稿。 主要能力 支持“输入书名后原创文案”和“从飞书 Base 读取现成文案”两条路线。 首次使用自动检查软件、命令、API 配置和飞书只读权限，并给出缺失项安装指引。 飞书中存在同书多条记录时要求用户选择，不默认读取第一条。 配音只生成一条完整音频，以真实音频时长统一对齐画面和字幕。 字幕按标点与完整意群切分，避免把下一句话的词放进上一条字幕。 主书揭示后持续显示书名和作者，直至视频结束。 所有素材以独立可编辑项进入本机 OpenChatCut。 安装 安装后可这样触发： 或： 首次运行检查 使用飞书路线时： 检查结果不会输出 API Key、App Secret 或访问令牌。缺失项会写入 ，由 Agent 用中文解释安装、授权和复验方法。 依赖 Python 3 与 FFmpeg / FFprobe 微信读书 Skill 与对应授权 豆包 TTS 配置 Pexels API Key Codex 图像生成能力 OpenChatCut Desktop 飞书路线可选依赖：lark-cli 所有密钥只应保存在安全环境、系统钥匙串或工具自带的凭据存储中，不要提交到 Git，也不要发到对话中。 目录 ：中文主流程和决策规则 ：环境检查、配音、素材搜索和交付校验 ：文案、分镜、字幕、画面和剪辑规范 asse
| Stars | 206 |
| Forks | 43 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 55.8556937441374/100 |
| Last Updated | 2026-08-10 |
| Created | 2026-08-10 |
| Platforms | python |
| Est. Tokens | ~13k |
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book-sales-video is 中文图书带货视频 Agent Skill：飞书取稿、书籍核验、配音、配图、双语字幕与 OpenChatCut 自动剪辑. It is categorized as a Agent Tool with 206 GitHub stars.
book-sales-video is primarily written in Python.
You can find installation instructions and usage details in the book-sales-video GitHub repository at github.com/Kianzzz/book-sales-video. The project has 206 stars and 43 forks, indicating an active community.
book-sales-video is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to book-sales-video on Agent Skills Hub include awesome-android-agent-skills, agent-skills, claude-code-skill-factory. 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: