by jidouqie · Codex Skill · ★ 254
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RedBeacon · 小红书运营数字员工 RedBeacon 是一款面向小红书运营的本机数字员工。它把账号定位、选题规划、文案与封面生成、本机审稿和发布串成一条可见、可控的工作流，让创作者把时间放在方向和判断上。 产品主页 能做什么 多账号管理与扫码登录：在一台电脑上管理多个小红书账号，账号资料、内容和登录态按账号隔离。 对标账号学习：粘贴小红书账号主页，RedBeacon 学习其定位逻辑、内容节奏、文案方法和视觉语言，再生成适合当前账号的方案。 单篇笔记风格学习：粘贴一篇喜欢的笔记，提炼标题、正文和封面方法，沉淀为可长期复用的内容方案。 选题、文案与封面：围绕账号定位生成选题和笔记，支持 AI 封面、文字卡片以及组合配图。 本机审核与发布：成稿进入本机审稿台，修改、通过后再发布；不会未经确认自动发出内容。 AI 助手协作：支持 Claude Code、Codex、OpenClaw、Hermes 和腾讯 WorkBuddy，也可以完全通过桌面客户端使用。 业务数据默认保存在本机。ByteStaff 平台仅提供账号身份和 AI 能力，不保存你的本地运营资料。 安装正式版 安装入口长期不变，会自动选择当前正式版本，并校验版本、文件大小和 SHA-256。 macOS（Apple Silicon） 在“终端”中运行： Windows 10 / 11 在 PowerShell 中运行： 安装完成后，RedBeacon 会自动启动。以后从客户端设置页、CLI 或 AI 助手触发升级，都会执行同一套全量更新流程。 使用流程 生成与发布都是用户主动触发的前台操作。RedBeacon 不依赖后台常驻服务，也不会绕过审稿状态偷偷发布。 当前正式版 最新正式版：0.1.110 本版重点提升了对标账号学习和单篇笔记风格学习的稳定性，强化了视觉方案对赛道核心诉求、真实主体与安全信息的识别，并完善了浏览器登录、下载回退和双平台安装验证。 关于这个 GitHub 仓库 这里是 RedBeacon 的公开产品介绍与安装入口。当前客户端、CLI、模型提示词、正式安装包和运行时依赖不会通过 GitHub 发布；请只使用上面的官网安装命令获取经过校验的当前版本。 这个仓库保留了早期公开的 skill 与安装器历史，用于兼容已有社区引用。它们不是当前产品的安装来源，也不会继续同步新的产品源码。 使用提醒 RedBeacon 是效率工具。请只运营你有权管理的账号，发布前核对内容事实、版权和平台规则；任何自动化操作都应由用户主动触发并监督。 联系与反馈 产品介绍、安装帮助和合作咨询请前往 [RedBeacon 产品主页](https://byte
| Stars | 254 |
| Forks | 46 |
| Language | Shell |
| Category | Codex Skill |
| Quality Score | 49.5028044956248/100 |
| Open Issues | 1 |
| Last Updated | 2026-08-21 |
| Created | 2026-04-28 |
| Platforms | claude-code, cli, codex |
| Est. Tokens | ~10k |
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redbeacon is RedBeacon｜开源 Skill 与安装入口：连接本机客户端，完成定位、对标、选题、创作、审稿、发布与账号级全流程自动化. It is categorized as a Codex Skill with 254 GitHub stars.
redbeacon is primarily written in Shell. It covers topics such as ai-agent, claude-code, codex.
You can find installation instructions and usage details in the redbeacon GitHub repository at github.com/jidouqie/redbeacon. The project has 254 stars and 46 forks, indicating an active community.
The top alternatives to redbeacon on Agent Skills Hub include palot, socialclaw, OpenContext. 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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