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+…
Draft, restructure, or plan Nature-style manuscript sections from author-provided claims, results, figures, notes, or Chinese drafts. Use when the user wants to write or rebuild an abstract, introduction, results narrative, discussion, conclusion, title, or full manuscript
$ npx -y skills add LiHongwei-cn/lihongwei-cn --skill nature-writing --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/nature-writingContext preview
The summary Claude sees to decide when to auto-load this skill.
Draft, restructure, or plan Nature-style manuscript sections from author-provided claims, results, figures, notes, or Chinese drafts. Use when the user wants to write or rebuild an abstract, introduction, results narrative, discussion, conclusion, title, or full manuscript
name: nature-writing description: Draft, restructure, or plan Nature-style manuscript sections from author-provided claims, results, figures, notes, or Chinese drafts. Use when the user wants to write or rebuild an abstract, introduction, results narrative, discussion, conclusion, title, or full manuscript argument rather than only polish finished prose. version: 0.2.0 author: Community contribution based on curated Nature/Nature Communications writing patterns and open research-writing notes
Use this skill when the user needs help creating or rebuilding manuscript prose, not merely polishing existing sentences.
methods, novelty, sample sizes, statistics or limitations.
input instead of filling the gap.
| File | Open when | |---|---| | [references/article-architecture.md](references/article-architecture.md) | You need section-level structure, argument order, or published-article writing patterns | | [references/abstract.md](references/abstract.md) | Drafting or revising an abstract, especially challenge-contribution and challenge-insight-contribution forms | | [references/introduction.md](references/introduction.md) | Drafting or revising an Introduction, task framing, technical challenge, contribution framing, or teaser/pipeline logic | | [references/related-work.md](references/related-work.md) | Rebuilding Related Work as topic synthesis instead of a paper-by-paper list | | [references/method.md](references/method.md) | Writing Method sections, pipeline modules, module motivation, technical advantages, or implementation details | | [references/experiments.md](references/experiments.md) | Planning or writing Experiments/Results around baselines, ablations, metrics, tables, figures, and claim support | | [references/conclusion.md](references/conclusion.md) | Writing a bounded conclusion with contribution, evidence, impact, limitation, and future direction | | [references/paragraph-flow.md](references/paragraph-flow.md) | User asks whether a paragraph flows, makes sense, or is clear; use reverse outlining and paragraph-message checks | | [references/paper-review.md](references/paper-review.md) | Final manuscript self-review, rejection-risk audit, claim-evidence alignment, or reviewer-facing critique | | [references/chinese-author-workflow.md](references/chinese-author-workflow.md) | The user's notes are Chinese, mixed Chinese-English, or organized as lab notes rather than manuscript prose | | [references/examples/index.md](references/examples/index.md) | You need concrete abstract, introduction, or method examples after choosing the relevant guide |
Before drafting, identify:
conclusion, significance paragraph or full outline
computational or interdisciplinary
If any of `core claim`, `evidence` or `boundary` is absent, expose the gap before drafting. You may still produce a scaffold with explicit placeholders.
1. Build a one-sentence argument: `In [system/problem], we show [advance] using [approach], supported by [evidence], with [boundary].` 2. Choose the section architecture from `references/article-architecture.md`. 3. Map each paragraph to one job: context, gap, approach, result, comparison, mechanism, implication or limitation. 4. Draft from evidence outward. Keep claims near the data that support them. 5. Calibrate verbs: `show`, `demonstrate`, `suggest`, `indicate`, `enable`, `may`, `could`. 6. Remove unsupported novelty and universal claims. 7. Run a paragraph-flow check: one paragraph, one message, with a clear first sentence and explicit sentence-to-sentence relation. 8. Return prose plus concise notes on assumptions and missing inputs.
Default Nature pattern:
`context/problem -> gap -> approach -> key result -> implication -> boundary`
For technical AI, ML, CV or method-heavy manuscripts, open `references/abstract.md` and choose one of:
Keep it compact. Include quantitative or comparative detail when the user provided it. End with what the work enables, not generic importance.
Use:
`field scale -> bottleneck -> prior attempts -> unresolved gap -> present study`
For method-heavy papers, open `references/introduction.md` and reason backward from the technical challenge and contribution before drafting forward.
Do not summarize all results. The final paragraph should state what this paper does and how it addresses the gap.
Use an evidence ladder:
`system/workflow -> validation -> main result -> baseline comparison -> mechanism/diagnostic analysis -> application or generalization`
Each subsection should have a claim-first opening and then data support.
For ML/conference-style experiment sections, open `references/experiments.md` and make sure each major claim is backed by comparison, ablation, or stress-test evidence.
Use:
`topic scope -> representative methods -> limitation tied to this paper -> distinction`
Group prior work by technical topic and mechanism, not by publication year.
Use:
`central advance -> evidence meaning -> relation to prio
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…