nature-polishing
Yuan1z0825/nature-skills · Agent Skill
將學術文字改進為Nature風格的英語,保留事實和術語。協助編輯、翻譯和LaTeX格式設定。
前往安裝 ↓總覽
| 發布者 | |
|---|---|
| 儲存庫 | Yuan1z0825/nature-skills ↗ |
| 技能資料夾 | nature-polishing |
| 星 | 39,583 |
| 附帶檔案 | 2 |
| 授權 | Apache-2.0 |
| 最近更新 | 2026年9月5日 |
| 連結 | 技能原始碼 ↗ · 儲存庫 ↗ |
Mozgi 檢查
我們在技能檔案中發現的內容。詳細資訊需訂閱。
安裝
技能就是一個含 SKILL.md 的資料夾。選擇你的代理,在專案根目錄的終端機(macOS、Linux 或 Windows 的 Git Bash)執行指令。
把提示貼到 Claude、Codex 或其他助理:它會自行檢查並安裝技能。
Install the “nature-polishing” skill from mozgi.io (https://mozgi.io/skills/nature-polishing/) for me. 1. Download the archive: https://mozgi.io/skills/nature-polishing/download If it fails, take the skill folder from the source: https://github.com/Yuan1z0825/nature-skills/tree/main/skills/nature-polishing 2. Read SKILL.md and the other files. Check the skill is safe: no hidden commands, no sending data elsewhere, no scripts doing something other than described. If anything looks suspicious, stop and tell me. 3. Put the “nature-polishing” folder into your agent's skills folder: .claude/skills/ for Claude Code, .agents/skills/ for Codex, .cursor/skills/ for Cursor, .gemini/skills/ for Gemini CLI. Inside the project if we are in one, otherwise in the home folder (~/.claude/skills/ etc.). 4. Make sure <skills folder>/nature-polishing/SKILL.md exists. 5. Briefly tell me what this skill does and offer to try it on my task. If you cannot do a step yourself, tell me what to click or type.
git clone --depth 1 --branch main https://github.com/Yuan1z0825/nature-skills.git /tmp/nature-skills mkdir -p .claude/skills cp -r /tmp/nature-skills/skills/nature-polishing .claude/skills/nature-polishing
重新啟動 Claude Code — 技能就會出現在清單中,手動呼叫:/nature-polishing。要套用到所有專案,請放在 ~/.claude/skills。
git clone --depth 1 --branch main https://github.com/Yuan1z0825/nature-skills.git /tmp/nature-skills mkdir -p .agents/skills cp -r /tmp/nature-skills/skills/nature-polishing .agents/skills/nature-polishing
Codex 會從目前資料夾往上到儲存庫根目錄,在 .agents/skills 中尋找技能;用 /skills 或 $nature-polishing 呼叫。所有專案:~/.agents/skills。
git clone --depth 1 --branch main https://github.com/Yuan1z0825/nature-skills.git /tmp/nature-skills mkdir -p .cursor/skills cp -r /tmp/nature-skills/skills/nature-polishing .cursor/skills/nature-polishing
Cursor 啟動時會載入技能;在 Agent 聊天輸入「/」呼叫。Cursor 也會讀取 .claude/skills 與 .agents/skills。所有專案:~/.cursor/skills。
git clone --depth 1 --branch main https://github.com/Yuan1z0825/nature-skills.git /tmp/nature-skills mkdir -p .gemini/skills cp -r /tmp/nature-skills/skills/nature-polishing .gemini/skills/nature-polishing
任務符合時,Gemini CLI 會提議啟用技能,由你確認。所有專案:~/.gemini/skills。
安裝前請先檢視 SKILL.md 與相關檔案。點擊檔案即可預覽。
SKILL.md 內容
作者原文 · 唯讀Nature-Style Academic Polishing — Router
This skill is split into two layers:
- A static layer under
static/that holds versioned, reusable content fragments (core principles, paper-type playbooks, per-section guidance, language-specific rules, per-journal style). - A dynamic layer (this file plus
manifest.yaml) that detects the request's axes and loads only the fragments needed for the current job.
Do not try to apply the polishing logic from memory or from this router. Always load fragments from disk as described below.
Routing protocol
Follow these five steps every time the skill is invoked.
1. Load the manifest and the core layer
Read manifest.yaml ↗. It declares the axes (paper_type, section, language, journal), the allowed values, and the file paths each value maps to.
Also read every file listed under always_load. These hold the default stance, failure-mode diagnosis, ethics, and output format that apply to every polish job.
2. Detect the axis values for this request
For each axis in the manifest, decide the value using the manifest's detect: hint and the user's input:
paper_type— research / methods / hypothesis / algorithmic / review. Default: research.section— abstract / intro / results / discussion / conclusion / title / methods. May be multiple. Ask the user if it is ambiguous and matters for the polish.language— en or zh-to-en. Detect from the draft itself.journal— nature / nat-comms / nat-mach-intell / generic. Default:
generic. Use nature only for flagship Nature, nat-comms for Nature
Communications and nat-mach-intell for Nature Machine Intelligence (NMI).
Do not route another Nature Portfolio title through flagship Nature rules.
State the detected axis values in one short line to the user before proceeding, so they can correct you cheaply. This is a progress update, not an approval gate; continue unless a necessary decision remains unresolved.
3. Load the matching fragments
For each axis value, Read the file mapped in the manifest. Skip the section axis only if the user has supplied free-floating prose with no section context.
Do not read every fragment in static/. Load only what step 2 selected.
4. Polish using the loaded material
Apply the loaded fragments in this priority order, matching the paper type -> section job -> paragraph logic -> claim/evidence/boundary -> sentence polish rule from core/failure-modes.md:
1. Paper-type playbook (architecture, writing order).
2. Section-specific job and failure modes.
3. Journal-specific framing and constraints.
4. Language-specific sentence and paragraph rules (apply last).
5. Core stance and ethics throughout.
If a paragraph's structural problem cannot be fixed without inventing content, flag it instead of papering over it.
For Results, full-main-text compression, main-versus-SI allocation, or prose
added during revision, load ../nature-shared/core/main-text-discipline.md
before sentence polishing. Classify each result, retain the shortest sufficient
evidence chain, and require every addition to trigger a deletion or replacement
check across the affected paragraph.
For flagship Nature, Nature Communications, Nature Machine Intelligence, or
another Nature Portfolio title, load the matching shared Nature-style corpus
guidance:
- Results or Discussion →
../nature-shared/core/nature-results-discussion.md
- Introduction or whole-manuscript narrative →
../nature-shared/core/nature-introduction.md
- Abstract →
../nature-shared/core/nature-abstract.md
Preserve claim escalation, the fast question funnel, Introduction–Results
alignment, discovery-centred abstract compression, evidence-bound local
interpretation, and cross-Results synthesis. These defaults were initially
distilled from published NMI papers; treat them as corpus-derived guidance, not
official policy, and obey the target journal's current rules when they differ.
For any Discussion polish or restructuring job, also load
../nature-shared/core/discussion-argument-language.md. Use its function labels
to remove Results replay, repair the movement from specific findings to bounded
implications, calibrate modal and reporting verbs to evidence strength, and make
limitations and future work resolve named claim boundaries. Treat it as general
writing guidance, not journal policy.
5. Reach for references only when needed
The files under references/ are deep references, not defaults. Open them on demand per the references.on_demand table in the manifest, for example when the user explicitly asks for phrasebank-style alternatives or a stricter style audit.
When the target is Nature Machine Intelligence and exact limits, availability
sections, conference-extension disclosure or production checks affect the
revision, load
../nature-shared/journal-formats/nature-machine-intelligence.md.
When the job is a whole manuscript rather than a passage, or the text has already been through more than one round of editing, also load ../nature-shared/core/consistency-sweep.md. Polishing passage by passage cannot see accumulated drift: one experimental factor under several names, the same quantity in two units, a metric at two precisions, or a superlative the paper's own table contradicts. Sweep for those before working on sentences, and repeat the sweep until a pass finds nothing new.
Layout/typesetting (排版) requests are different. If the user asks to fix
*placement* rather than wording — loose/sparse pages, stranded headings, figures
that don't fill the page or split across pages, "Float too large", multi-panel
arrangement, sparse Supplementary Information — skip the prose axes (paper_type,
section, language, journal) and load references/latex-layout.md directly. That
file is self-contained: it carries the diagnosis workflow (render → contact-sheet →
read the log), the float-glue and [H]/\clearpage/placeins patterns, and the
"regenerate wide figures taller at the source" rule. Always compile and visually
inspect rendered pages before and after — never judge layout from the .tex alone.
Why this split
- The static layer is versioned and reviewable. Adding a new journal style or paper type is one new file plus one manifest line.
- The dynamic layer keeps each invocation cheap: only the fragments relevant to this draft enter context, instead of the full 1000-line monolith.
- The router itself is short on purpose. Update fragments, not this file, when adding scope.
常見問題
nature-polishing 技能有什麼用途?
將學術文字改進為Nature風格的英語,保留事實和術語。協助編輯、翻譯和LaTeX格式設定。
如何安裝 nature-polishing?
打開本頁的「安裝」區塊並選擇你的代理 — Claude Code、Codex、Cursor 或 Gemini CLI:那裡有把技能資料夾複製到正確位置的現成指令。也可以複製「透過助理」分頁中的提示,讓助理自行檢查並安裝技能。
nature-polishing 適用哪些 AI 模型?
技能是純 Markdown 指示(Agent Skills 格式),因此 nature-polishing 適用任何讀取 SKILL.md 的代理:Claude Code、Codex、Cursor、Gemini CLI、GitHub Copilot、OpenCode 等。只有在任務符合技能描述時,模型才會載入指示。
nature-polishing 是免費的嗎?
此技能由 Yuan1z0825 公開發布,採用 Apache-2.0 授權。技能本身免費 — 你只需向 AI 供應商支付實際使用的 token 費用。
安裝技能安全嗎?
技能是代理會遵循的指示,安裝前請閱讀 SKILL.md 及相關檔案。技能資料夾中的腳本只有在代理執行時才會運行 — 留意它的動作,不要授予多餘權限。
技能和 MCP 伺服器或外掛有何不同?
MCP 伺服器或外掛為模型提供新工具 — 會執行並回傳結果的程式碼。技能提供的是知識與流程:如何處理任務、要遵循哪些步驟、好的成果長什麼樣子。它不需要伺服器或 API 金鑰。