cheat-on-content
给所有想把"感觉"变成可校准预测的内容创作者。**方法论通用**——打分 → 盲预测 → T+3d 复盘 → 进化 rubric 的循环适用任何能被量化(播放 / 阅读 / 收听 / 点击)的内容。**rubric 是循环的内容,不是循环本身**——当前内置一份观点视频 rubric(参考博主 25+…
Complete App Store Optimization (ASO) toolkit for researching, optimizing, and tracking mobile app performance on Apple App Store and Google Play Store
$ npx -y skills add LiHongwei-cn/lihongwei-cn --skill app-store-optimization --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/app-store-optimizationContext preview
The summary Claude sees to decide when to auto-load this skill.
Complete App Store Optimization (ASO) toolkit for researching, optimizing, and tracking mobile app performance on Apple App Store and Google Play Store
name: app-store-optimization description: "Complete App Store Optimization (ASO) toolkit for researching, optimizing, and tracking mobile app performance on Apple App Store and Google Play Store" risk: unknown source: community date_added: "2026-02-27"
This comprehensive skill provides complete ASO capabilities for successfully launching and optimizing mobile applications on the Apple App Store and Google Play Store.
{
"app_name": "MyApp",
"category": "Productivity",
"target_keywords": ["task manager", "productivity", "todo list"],
"competitors": ["Todoist", "Any.do", "Microsoft To Do"],
"language": "en-US"
}{
"platform": "apple" | "google",
"app_info": {
"name": "MyApp",
"category": "Productivity",
"target_audience": "Professionals aged 25-45",
"key_features": ["Task management", "Team collaboration", "AI assistance"],
"unique_value": "AI-powered task prioritization"
},
"current_metadata": {
"title": "Current Title",
"subtitle": "Current Subtitle",
"description": "Current description..."
},
"target_keywords": ["productivity", "task manager", "todo"]
}{
"app_id": "com.myapp.app",
"platform": "apple" | "google",
"date_range": "last_30_days" | "last_90_days" | "all_time",
"rating_filter": [1, 2, 3, 4, 5],
"language": "en"
}{
"metadata": {
"title_quality": 0.8,
"description_quality": 0.7,
"keyword_density": 0.6
},
"ratings": {
"average_rating": 4.5,
"total_ratings": 15000
},
"conversion": {
"impression_to_install": 0.05
},
"keyword_rankings": {
"top_10": 5,
"top_50": 12,
"top_100": 18
}
}MUNDO - THE EMPEROR. Complete AI orchestration system with 1208 skills, 25 capability modules, self-evolving, collective consciousness. GitHub Actions 24/7 automation.
Repo: LiHongwei-cn/lihongwei-cn
给所有想把"感觉"变成可校准预测的内容创作者。**方法论通用**——打分 → 盲预测 → T+3d 复盘 → 进化 rubric 的循环适用任何能被量化(播放 / 阅读 / 收听 / 点击)的内容。**rubric 是循环的内容,不是循环本身**——当前内置一份观点视频 rubric(参考博主 25+…
提议并执行 rubric 或 bucket 升级。两种模式:**完整 rubric bump**(最高风险动作,5 步强制 + 跨模型审核)和 **--bucket-only 轻量重校**(只换 bucket 边界,不动 rubric 公式)。**Phase 2 强制走 cheat-score-blind…
cheat-on-content 的首次 onboarding 与脚手架创建器。统一流程——所有用户都走相同 5 阶段闭环,唯一区别是"发过视频的人"会在 init 时多一步:抓取已有视频建立历史 context(用于后续 cheat-seed 给更贴合的选题、更准的…
从对标账号导入 script + 数据 → 拆 pattern + 派生 base rubric 信号 → 写到 benchmark.md / script_patterns.md / rubric_notes.md。**这是工具最早期信号的来源**——cold-start…
把老用户的 .cheat-state.json 升级到当前 schema_version。读 migrations/registry.md 算迁移链,按顺序应用每一步迁移文件。幂等:跑两次结果一样。失败停在中间版本不前进。触发词:"迁移"/"升级 state"/"migrate"/"我的 state…
从复盘评论数据派生 / 刷新账号的受众画像,写入 audience.md。这是和 rubric 平行的第二个派生物——rubric 答"怎么打分",persona 答"谁在看"。cheat-seed 选题 / 写稿时读它。**audience.md 含实绩信号,cheat-score-blind…