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+…
Build full-paper Chinese-English side-by-side, figure/table-aware, source-grounded Markdown readers for journal or conference papers from PDF, DOI, arXiv, publisher HTML, or pasted text. Use whenever the user asks to translate or read a paper, make 中英文对照/原文对照/全文翻译解读, extract
$ npx -y skills add LiHongwei-cn/lihongwei-cn --skill nature-reader --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/nature-readerContext preview
The summary Claude sees to decide when to auto-load this skill.
Build full-paper Chinese-English side-by-side, figure/table-aware, source-grounded Markdown readers for journal or conference papers from PDF, DOI, arXiv, publisher HTML, or pasted text. Use whenever the user asks to translate or read a paper, make 中英文对照/原文对照/全文翻译解读, extract
name: nature-reader description: Build full-paper Chinese-English side-by-side, figure/table-aware, source-grounded Markdown readers for journal or conference papers from PDF, DOI, arXiv, publisher HTML, or pasted text. Use whenever the user asks to translate or read a paper, make 中英文对照/原文对照/全文翻译解读, extract figures or tables into the right positions, preserve figure/table placement near relevant prose, or keep exact source anchors for every block. This skill must not degrade into a summary-only output unless the user explicitly asks for a summary.
Use this skill to turn a research paper into a complete Markdown reading artifact.
The default output should read like a bilingual paper companion, not a summary dump:
This skill is for papers, preprints, and conference proceedings across disciplines. It is not limited to Nature-family journals.
Use this skill when the user wants any of the following:
If the user only wants a summary, use a summarization skill instead. If the user only wants citation search, use a citation skill instead.
When the user asks for paper translation, reading, `nature-reader`, `中英文对照`, `原文对照`, `全文翻译`, or `翻译解读`, produce a paragraph-level bilingual reader by default.
Do not replace the reader with:
If constraints prevent full processing, still create a draft reader and clearly label missing pages, missing figures/tables, untranslated blocks, or low-confidence OCR/crops in `translation_notes.md`.
Translate for meaning, not for style. Preserve the paper's structure, evidence, hedging, terminology, equations, units, and citation markers. Keep the output in prose paragraphs unless the source itself is tabular or list-like. Do not collapse the paper into keyword bullets or slide-style notes.
The reading file should help a reader move between:
Each substantive source block should have a stable anchor and a visible bilingual pair:
<a id="S001"></a> **Source:** p.1 S001 **Original:** [source paragraph] **中文:** [faithful Chinese translation]
For copyrighted publisher PDFs, keep chat responses short and point to the local artifact. In local `paper.md`, include the bilingual reader only for the user-provided source file or clearly lawful open-access content; avoid reproducing large copyrighted text directly in chat.
Determine whether the source is:
Then identify the paper type at a high level:
This helps decide how tightly to couple text, figures, and captions.
If the user provides a full paper, process the entire document. Do not stop at the abstract, introduction, or a few representative pages unless the user explicitly asks for a preview.
Create stable IDs for source blocks:
For each block, capture:
Keep the source map stable so later questions can point back to the same IDs. For long papers, add a page index so the reader can jump across the whole document without losing location.
Translate every extractable substantive block with these rules:
If a sentence contains multiple claims, keep the translation readable but do not split away the original evidence chain.
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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…