ai-fomo-skills: Personal superalignment skills for turning AI information overload into reusable knowledge, signals

by vincelele · Codex Skill · ★ 248

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

How to install ai-fomo-skills

git clone https://github.com/vincelele/ai-fomo-skills.git

Taken from the project's README on GitHub.

Security audit verdict: SAFE · quality 70/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

aicodexknowledge-managementpersonal-aiskills

Quick Facts

Stars248
Forks1
LanguagePython
CategoryCodex Skill
LicenseMIT
Quality Score70/100
Open Issues1
Last Updated2026-05-29
Created2026-05-19
Platformscodex, python
Est. Tokens~5k

About ai-fomo-skills

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...

Compatible Skills

These tools work well together with ai-fomo-skills for enhanced workflows:

  • llm-wiki-template — semantic(0.27)+complementary+same_lang+similar_pop+shared_platform (60%)
  • arkon — semantic(0.24)+complementary+same_lang+similar_pop+shared_platform (59%)
  • kc_ai_skills — semantic(0.22)+complementary+same_lang+similar_pop+shared_platform (58%)
  • 10xProductivity — semantic(0.21)+complementary+same_lang+similar_pop+shared_platform (57%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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:

View on GitHub → Browse Codex Skill tools