by vincelele · Codex Skill · ★ 188
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AI FOMO Skills 为了对抗 AI FOMO,我专门做了一个 Skill。 它不是 AI 日报,也不是 prompt 合集,而是帮你从 AI 信息流里筛出真正值得看的内容,并沉淀成自己的知识库。 这是一套给 AI 创业者、产品经理、独立开发者 使用的 Agent Skill。它不是让 Agent 帮你多看一点信息,而是让 Agent 帮你少浪费一点注意力。 我真正缺的不是信息,而是判断 AI 信息太多了。 不看,怕错过真正优秀的分享、认知和机会;看了,又经常发现内容很浅、质量不高,浪费时间。每天各种 AI 博文、YouTube、小宇宙、公众号、GitHub Trending、产品更新的信息都太多了,人不可能每条都认真看。 所以我更需要的是:从这些渠道里筛选出我最关心、最值得看的内容。 AI FOMO Skills 要回答的不是“这篇内容讲了什么”,而是: 什么是 AI 超级对齐 这里说的 AI 超级对齐,不是宏大的 AI 安全概念,而是一个很具体的个人工作流: 普通总结器会问:“这篇文章说了什么?” AI FOMO Skills 会问: 这件事是否影响你的产品、创业方向或技术判断? 这条信息是否来自你信任的来源? 它是新信号,还是重复噪音? 它应该进入长期知识库,还是只适合快速看一眼? 如果信息不够,应该先问你什么问题? 它主要做四件事 核心不是“看更多”,而是更快判断哪些值得看,哪些可以先跳过。 Status Alpha / developer preview. 可以开始试用,但还不是面向所有人的稳定产品。建议先用少量公开资料跑通流程,再接入账号型数据源。 适合谁 AI 创业者:需要快速判断哪些变化会影响机会、产品和市场。 产品经理:需要跟踪 AI 产品、模型能力、交互范式和用户行为变化。 独立开发者:需要发现值得尝试的新工具、开源项目和产品机会。 高频使用 AI Agent 的人:已经在用 Codex、Claude Code、OpenClaw、Cursor 等支持 skill 或规则文件的工具。 被 AI 信息流淹没的人:不想错过重要信息,也不想把时间浪费在低质量内容上。 不适合谁 你只是想看一份通用 AI 日报。 你不想提供任何个人背景
| Stars | 188 |
| Forks | 0 |
| Language | Python |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 69.8185130472639/100 |
| Open Issues | 1 |
| Last Updated | 2026-05-29 |
| Created | 2026-05-19 |
| Platforms | codex, python |
| Est. Tokens | ~5k |
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ai-fomo-skills is Personal superalignment skills for turning AI information overload into reusable knowledge, signals, and digests.. It is categorized as a Codex Skill with 188 GitHub stars.
ai-fomo-skills is primarily written in Python. It covers topics such as ai, codex, knowledge-management.
You can find installation instructions and usage details in the ai-fomo-skills GitHub repository at github.com/vincelele/ai-fomo-skills. The project has 188 stars and 0 forks, indicating an active community.
ai-fomo-skills is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to ai-fomo-skills on Agent Skills Hub include agnix, neurolink, codexia. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.