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+…
Fetch any X/Twitter post as clean LLM-friendly JSON. Converts x.com, twitter.com, or adhx.com links into structured data with full article content, author info, and engagement metrics. No scraping or browser required.
$ npx -y skills add LiHongwei-cn/lihongwei-cn --skill adhx --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/adhxContext preview
The summary Claude sees to decide when to auto-load this skill.
Fetch any X/Twitter post as clean LLM-friendly JSON. Converts x.com, twitter.com, or adhx.com links into structured data with full article content, author info, and engagement metrics. No scraping or browser required.
name: adhx description: "Fetch any X/Twitter post as clean LLM-friendly JSON. Converts x.com, twitter.com, or adhx.com links into structured data with full article content, author info, and engagement metrics. No scraping or browser required." risk: safe source: community date_added: "2026-03-25"
Fetch any X/Twitter post as structured JSON for analysis using the ADHX API.
ADHX provides a free API that returns clean JSON for any X post, including full long-form article content. This is far superior to scraping or browser-based approaches for LLM consumption. Works with regular tweets and full X Articles.
https://adhx.com/api/share/tweet/{username}/{statusId}Extract `username` and `statusId` from any of these URL formats:
| Format | Example | |--------|---------| | `x.com/{user}/status/{id}` | `https://x.com/dgt10011/status/2020167690560647464` | | `twitter.com/{user}/status/{id}` | `https://twitter.com/dgt10011/status/2020167690560647464` | | `adhx.com/{user}/status/{id}` | `https://adhx.com/dgt10011/status/2020167690560647464` |
When a user shares an X/Twitter link:
1. **Parse the URL** to extract `username` and `statusId` from the path segments 2. **Fetch the JSON** using curl:
curl -s "https://adhx.com/api/share/tweet/{username}/{statusId}"3. **Use the structured response** to answer the user's question (summarize, analyze, extract key points, etc.)
{
"id": "statusId",
"url": "original x.com URL",
"text": "short-form tweet text (empty if article post)",
"author": {
"name": "Display Name",
"username": "handle",
"avatarUrl": "profile image URL"
},
"createdAt": "timestamp",
"engagement": {
"replies": 0,
"retweets": 0,
"likes": 0,
"views": 0
},
"article": {
"title": "Article title (for long-form posts)",
"previewText": "First ~200 chars",
"coverImageUrl": "hero image URL",
"content": "Full markdown content with images"
}
}/plugin marketplace add itsmemeworks/adhx
curl -sL https://raw.githubusercontent.com/itsmemeworks/adhx/main/skills/adhx/SKILL.md -o ~/.claude/skills/adhx/SKILL.md
User: "Summarize this post https://x.com/dgt10011/status/2020167690560647464"
curl -s "https://adhx.com/api/share/tweet/dgt10011/2020167690560647464"
Then use the returned JSON to provide the summary.
User: "How many likes did this tweet get? https://x.com/handle/status/123"
1. Parse URL: username = `handle`, statusId = `123` 2. Fetch: `curl -s "https://adhx.com/api/share/tweet/handle/123"` 3. Return the `engagement.likes` value from the response
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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…