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Covers journal selection, literature review, manuscript drafting, figure generation, citation verifica...\n\nTags: latest:1.0.0\n\nVersion history:\n\nv1.0.0 | 2026-05-17T20:30:47.583Z | user\n\nInitial release: full-pipeline Nature-series journal writing agent for 12 Nature journals. Includes figure generation, CrossRef citation verification, Word/LaTeX/PPT export, bilingual paper reader, and 534-paper curated literature index.\n\nArchive index:\n\nArchive v1.0.0: 14 files, 141794 bytes\n\nFiles: data/papers-index.json (320760b), README.md (16983b), scripts/auto_figure.py (19813b), scripts/export_docx.py (15478b), scripts/export_pptx.py (11489b), scripts/requirements.txt (59b), skill-card.md (2236b), SKILL.md (20377b), skills/nature-citation/SKILL.md (5732b), skills/nature-figure/SKILL.md (7618b), skills/nature-paper2ppt/SKILL.md (4424b), skills/nature-reader/SKILL.md (5115b), templates/journal-specs.json (9807b), _meta.json (135b)\n\nFile v1.0.0:SKILL.md\n\n---\nname: nature-paper-hub\ndescription: Full-pipeline Nature-series journal writing assistant. Covers journal selection, literature review, manuscript drafting, figure generation, citation verification, pre-submission audit, cover letter, and reviewer response. Trigger when user wants to write, revise, or submit a Nature-series research paper, or needs help with any part of the academic writing process.\nversion: 1.0.0\nauthor: Yang1Bai\ntags:\n - academic-writing\n - nature-journal\n - scientific-writing\n - research-paper\n - latex\n - claude-code\n - codex\n - openclaw\n---\n\n# Nature Paper Hub\n\n## Description\nFull-pipeline Nature-series journal writing assistant. Trigger when the user wants to:\n- Write, draft, or outline a Nature-series research paper\n- Select a Nature journal for submission\n- Revise any section of a manuscript\n- Plan or improve figures\n- Check citations or generate reference lists\n- Prepare a submission checklist or rebuttal letter\n- Export manuscript as LaTeX (Overleaf) or Word\n\nMulti-language: interact in Chinese or English; all manuscript output is in English.\n\n## Skill Location\n~/.openclaw/workspace/skills/nature-paper-hub/\n\n## Supporting Files\n- templates/journal-specs.json — journal-specific word limits, figures, references\n- templates/nature-latex.tex — master LaTeX template (Overleaf-ready)\n- scripts/export_docx.py — Word export via python-docx\n\n---\n\n## STAGE 0 — Journal Selection\n\n**Always run this stage first unless the user has already specified a journal.**\n\nPresent this menu and ask the user to choose:\n\n```\n📋 请选择目标期刊 / Select target journal:\n\n1. Nature (IF 63.7) — 顶级综合科学\n2. Nature Materials (IF 37.2) — 材料科学\n3. Nature Chemistry (IF 19.2) — 化学\n4. Nature Energy (IF 60.9) — 能源\n5. Nature Catalysis (IF 37.8) — 催化\n6. Nature Sustainability (IF 25.1) — 可持续发展\n7. Nature Communications (IF 15.7) — 全科学，开放获取，最灵活\n8. Nature Methods (IF 32.1) — 方法学\n9. Nature Computational Science (IF 12.0) — 计算科学\n10. Nature Chemical Engineering (IF 13.0) — 化学工程\n11. Nature Machine Intelligence (IF 23.9) — 机器学习/AI/机器人\n12. Nature Synthesis (IF 20.0) — 合成化学与材料合成\n13. 其他 / Other — 请告诉我期刊名\n```\n\nAfter selection, load the corresponding entry from `templates/journal-specs.json` and display:\n- Word limits (body, abstract, Methods)\n- Figure/table limit\n- Reference limit\n- Methods location (within text vs. after references)\n- Acceptance rate and IF\n\nThen ask: **\"您的论文类型是 Article 还是 Letter？\"**\n\n### ⚠️ Journal-specific special rules to load:\n\n**Nature Synthesis (选12):**\n- NO schemes — all graphics must be figures (no reaction scheme format)\n- Methods section CANNOT contain figures or tables — use Extended Data or SI\n- Results and Discussion may be combined into one section with subheadings\n- Discussion must be succinct and cannot have subheadings\n- Only one article type: Article (covers both short comms and full papers)\n\n**Nature Machine Intelligence (选11):**\n- Also accepts Analysis type (100–150 word abstract)\n- Reviews: ≤10% of references should have short annotations explaining key contributions\n- Strong preference for reproducibility: code/data availability is heavily weighted\n\n**Nature Chemical Engineering (选10):**\n- Methods called \"Online Methods\", placed after Discussion\n- Extended Data: up to 10 figures allowed\n- Focus on chemical engineering relevance: must address scale-up, process, or engineering challenge\n\n---\n\n## STAGE 1 — Concept & Literature Review\n\n### 1a. Concept Definition\nAsk the user:\n1. **Research topic in one sentence** (用一句话描述研究内容)\n2. **Core innovation** — what makes this new? (核心创新点是什么？)\n3. **Key result** — what did you find/achieve? (最重要的结果/发现)\n4. **Target scope** — does it fit the selected journal's scope?\n\n### 1b. Literature Search\nUse the LitReview system at https://ybliterature.com/api/search?q= to search for related papers.\nAlso use web_search with queries like:\n- `site:nature.com \"\" filetype:pdf`\n- `arxiv.org \"\" Nature-style`\n\nSearch for 3–5 open-access papers from the target journal as structural templates:\n```\nSearch query: site:nature.com/[journal-shortname] \"\" open access\n```\nFor each found paper, extract:\n- Paper structure (section titles used)\n- Abstract style\n- Figure count and types\n\nPresent the user with: key gap in literature, positioning suggestion, and 3–5 recommended template papers.\n\n### 1c. Novelty Check\nAsk: \"Has anyone published very similar work in the past 2 years?\" \nRun a targeted web search. Report findings honestly — if there's overlap, suggest how to differentiate.\n\n---\n\n## STAGE 2 — Outline & Structure Planning\n\nBased on the journal selected and paper type, generate a tailored outline.\n\n### Standard Nature Article Outline:\n```\nTitle: [concise, ≤15 words, no abbreviations]\nAbstract: [150 words — context → problem → approach → key result → significance]\n\nIntroduction\n ¶1 Broad context and importance\n ¶2 Specific background — what is known\n ¶3 The gap or unsolved problem\n ¶4 Your approach and key findings (end: \"Here we report...\")\n\nResults\n Section 1: [Synthesis/Preparation/Model — first evidence]\n Section 2: [Characterization/Validation — structural/spectroscopic proof]\n Section 3: [Mechanism/Explanation — why it works]\n Section 4: [Performance/Application — how good it is]\n Section 5: [Generalizability/Comparison — how broad/better]\n\nDiscussion\n ¶1 Summary of key findings\n ¶2 Comparison with literature\n ¶3 Mechanistic interpretation\n ¶4 Limitations + future work\n ¶5 Broader impact (1 sentence)\n\nMethods [~3000 words, after refs for most journals]\n - Materials/Reagents\n - Synthesis/Preparation\n - Characterization techniques\n - Computational details (if applicable)\n - Statistical analysis\n\nReferences [numbered, order of appearance]\nFigure Legends [detailed, self-contained]\nExtended Data [optional, up to 10 items]\n```\n\n**Adjust the outline based on paper type and journal.**\nFor Nature Communications: Methods sits within the main text after Discussion.\n\nAsk the user to review and modify the outline before proceeding.\n\n---\n\n## STAGE 3 — Section-by-Section Writing\n\nWork through each section one at a time. Ask for the user's raw data/notes for each section, then draft in Nature style.\n\n### Abstract Writing Rules (Nature Portfolio):\n- Single paragraph, no citations, no undefined abbreviations\n- Sentence 1–2: Broad context (why does this matter globally?)\n- Sentence 3–4: Specific problem or gap\n- Sentence 5–6: Your approach/method (brief)\n- Sentence 7–8: Key quantitative results\n- Sentence 9–10: Significance and outlook\n- Target: exactly 150 words (or journal limit)\n- Tense: Present for known facts; Past for what you did; Present for conclusions\n\n**📊 After drafting abstract — always run word count check:**\n```\nCurrent word count: [X] / [journal limit]\nStatus: [✅ within limit | ⚠️ X words over — suggest cuts below]\n```\nIf over limit, suggest specific cuts: remove adjectives, merge sentences, cut background context.\n\n### Introduction Writing Rules:\n- 4–6 paragraphs, ~800 words total\n- Each paragraph has a clear topic sentence\n- Citations must be accurate — verify with web_search if uncertain\n- Final paragraph: explicitly state what this paper reports\n- Avoid: \"In this paper, we...\" (use \"Here we show/report/demonstrate...\")\n- Avoid: excessive self-citation\n\n### Results Writing Rules:\n- Lead each subsection with the key finding (topic sentence = result)\n- Present data before interpretation\n- Every figure/table must be cited in order (Fig. 1a, Fig. 1b, Fig. 2...)\n- Use past tense for observations; present tense for general truths\n- Quantify everything: \"increased by 3.2-fold\" not \"significantly increased\"\n- Error bars: always state what they represent (mean ± s.d., n = X)\n\n### Discussion Writing Rules:\n- Do NOT restate Results — interpret and contextualize them\n- Compare explicitly with the best prior work (with citations)\n- Address limitations honestly (reviewers will ask if you don't)\n- End with 1 sentence of broader impact\n\n### Methods Writing Rules:\n- Enough detail for independent reproduction\n- Include all instrument models, software versions, parameters\n- For computational work: functional, basis set, k-points, cutoff energy, software version\n- Statistical methods: which test, software, significance threshold (p < 0.05)\n- Ethics/IRB statements if applicable\n\n### 🔍 Post-Section Self-Critique (run after drafting EVERY section)\nAfter delivering each drafted section, immediately evaluate it from a Nature reviewer's perspective:\n\n```\n📋 Self-critique — [Section Name]:\n✅ Strengths:\n - [what works well]\n⚠️ Weaknesses / likely reviewer concerns:\n - [specific issue 1: e.g., \"Claim in ¶2 lacks quantitative support\"]\n - [specific issue 2: e.g., \"Mechanism not distinguished from alternative explanations\"]\n - [specific issue 3: e.g., \"'Significantly' used without p-value\"]\n💡 Suggested improvements:\n - [concrete fix for each weakness]\n```\n\nDo NOT skip this step. If the user wants to proceed anyway, acknowledge the risks.\n\n---\n\n## STAGE 4 — Figure Planning\n\nAsk user: how many figures do you have data for? (Must be ≤ journal limit)\n\nFor each figure, guide:\n```\nFigure X: [What story does this figure tell?]\n Panel (a): [Data type] — [Message]\n Panel (b): [Data type] — [Message]\n Panel (c): [Data type] — [Message]\n\nDesign rules:\n- Each figure tells ONE clear story\n- Panel a = overview/schematic; subsequent panels = evidence\n- Resolution: 300 DPI min (600 DPI for line art)\n- Font: Arial or Helvetica, ≥7pt in final printed size\n- Color: accessible palette (avoid red-green for colorblind readers)\n- Scale bars: always include for microscopy images\n- Statistical indicators: *, **, *** for significance; exact p-values preferred\n```\n\nSuggest figure order: schematic → characterization → mechanism → performance → application\n\n---\n\n## STAGE 5 — Citation Verification\n\nFor each reference cited in the manuscript:\n1. Verify it exists using web_search: `\"[author] [year] [journal] [abbreviated title]\"`\n2. Check if it's been retracted: search `\"[paper title] retraction\"`\n3. Verify it supports the claim being made (use Scite-style thinking: supporting vs. contrasting)\n4. Format in Nature numbered style:\n ```\n 1. LastName, A., LastName, B. & LastName, C. Title of paper. Journal Vol, pages (Year).\n ```\n\nFlag any:\n- References older than 10 years (unless seminal)\n- References that don't directly support the claim\n- Missing DOIs\n\n### 📋 Bulk Reference Formatting (quick mode)\nIf user pastes a list of references in any format (Google Scholar export, DOI list, messy copy-paste):\n1. Parse each entry — extract authors, year, title, journal, volume, pages, DOI\n2. For any missing fields, look up via CrossRef: `web_fetch(\"https://api.crossref.org/works/\")`\n3. Re-format ALL entries into Nature numbered style in one batch\n4. Also output a `.bib` BibTeX block for the entire list\n5. Flag any entries that could not be verified\n\nTrigger phrase: \"帮我格式化引用\" / \"format my references\" / \"整理参考文献\"\n\n---\n\n## STAGE 6 — Pre-Submission Audit\n\nRun through this checklist before export:\n\n### Formatting ✓\n- [ ] Word count within journal limit (body text only, excl. abstract/refs/legends)\n- [ ] Abstract within word limit, single paragraph, no citations\n- [ ] Figure count ≤ journal limit\n- [ ] Reference count ≤ journal limit\n- [ ] Methods location correct for journal\n- [ ] Line numbers enabled (for peer review)\n\n### Content ✓\n- [ ] Title ≤ 15 words, no abbreviations\n- [ ] All abbreviations defined at first use\n- [ ] All figures cited in order in text\n- [ ] All figure legends self-contained (scale bars, error bars, n values, stats)\n- [ ] Data availability statement present\n- [ ] Author contributions (CRediT taxonomy)\n- [ ] Competing interests declared\n- [ ] Acknowledgements include all funding with grant numbers\n\n### Science ✓\n- [ ] Claims match data (no overclaiming)\n- [ ] Statistics correct (appropriate test, reported correctly)\n- [ ] Controls included and described\n- [ ] Reproducibility: n ≥ 3 for key experiments\n\n### Journal-Specific ✓\n- [ ] Cover letter written (highlight novelty + fit for journal)\n- [ ] Suggested reviewers (3–5 names + emails + no conflict)\n- [ ] Excluded reviewers (if any)\n\n### 📝 Cover Letter Generation\nAfter checklist is complete, automatically generate a cover letter:\n\n```\n[Date]\n\nDear [Editor-in-Chief / Editors of {Journal}],\n\nWe are pleased to submit our manuscript entitled \"[Title]\" for consideration \nas a [Article/Letter] in [Journal].\n\n[Paragraph 1 — The problem and why it matters: 2–3 sentences]\nDespite significant progress in [field], [specific gap or challenge] remains \nunsolved. Addressing this challenge is critical because [broader impact].\n\n[Paragraph 2 — What you did and key results: 2–3 sentences]\nHere, we report [approach/method] that [key result with quantitative data]. \nNotably, [most impressive finding, e.g., \"our catalyst achieves X% efficiency, \nsurpassing the previous record of Y%\"].\n\n[Paragraph 3 — Why this fits the journal: 1–2 sentences]\nWe believe this work is particularly suited for [Journal] as it [addresses \nbroad scientific question / introduces paradigm shift / will interest readers \nacross [disciplines].\n\nThis manuscript has not been published elsewhere and is not under consideration \nby any other journal. All authors have approved the submission.\n\nWe suggest the following reviewers: [Name, Affiliation, email] ...\n\nThank you for your consideration.\n\nSincerely,\n[Corresponding Author]\n[Affiliation, email]\n```\n\nAdjust tone based on journal prestige: Nature/Nature Materials → more assertive; Nature Communications → slightly more measured.\n\n---\n\n## STAGE 7 — Export\n\nAsk user: **\"导出格式？Overleaf (LaTeX) 还是 Word (.docx)？\"**\n\n### Option A: LaTeX / Overleaf\n1. Load template from `templates/nature-latex.tex`\n2. Fill in all sections with the drafted content\n3. Generate `main.tex` and `references.bib` (BibTeX format)\n4. Save to user-specified path (default: `~/Downloads/nature-paper-[journal]-[date]/`)\n5. Instructions: \"Upload main.tex + references.bib + figure files to Overleaf as a new project\"\n\n### Option B: Word (.docx)\n1. Run `python3 ~/.openclaw/workspace/skills/nature-paper-hub/scripts/export_docx.py`\n2. Script takes the drafted sections and generates a properly formatted .docx\n3. Styles: Heading 1 for sections, 11pt Times New Roman body, double-spaced\n4. Save to `~/Downloads/nature-paper-[journal]-[date].docx`\n\n---\n\n## STAGE 8 — Rebuttal Response\n\nWhen the user receives reviewer comments:\n\n### Step 1: Triage — classify ALL comments before writing any response\n\nFirst, parse and classify every comment:\n\n```\n📊 Reviewer Comment Triage:\n\nReviewer 1:\n Comment 1: [summary] → 🔴 Major | Needs new experiment\n Comment 2: [summary] → 🟡 Major | Needs clarification/additional analysis \n Comment 3: [summary] → 🟢 Minor | Text revision only\n Comment 4: [summary] → ✅ Valid concern | ❌ Disagree — evidence-based\n\nReviewer 2:\n ...\n\n📋 Revision Strategy:\n New experiments needed: [list]\n New analyses needed: [list]\n Text-only revisions: [list]\n Planned disagreements: [list with justification]\n Estimated revision effort: [X weeks]\n```\n\nPresent this triage to the user and confirm strategy before writing responses.\n\n### Step 2: Write point-by-point responses\n\nFor each comment (after triage confirmed):\n```\n**Reviewer X, Comment Y:** [🔴/🟡/🟢]\n[Quote the comment exactly]\n\n**Response:**\nWe thank the reviewer for this [insightful/constructive] comment.\n[Acknowledge validity of concern.]\n[Explain what you did: new experiment / clarification / revision]\n[If adding data]: \"We have added [X] to the revised manuscript (Fig. X / Line X).\"\n[If disagreeing]: \"We respectfully disagree because [evidence-based reason with citation].\"\n\n**Manuscript change:**\n[Quote revised text with line numbers, or state \"no change required\"]\n```\n\n### Step 3: Generate revision cover letter\nAfter all responses:\n- Summary of major changes (numbered)\n- List of new figures/data added\n- Statement of how each reviewer's concerns were addressed\n- Tone: confident but respectful\n\n---\n\n## Integration: Literature Search (two-tier)\n\n### Tier 1 — Static papers index (available to ALL users)\nThe repo includes `data/papers-index.json`: 534 curated papers (titles, journals, years, abstracts, DOIs)\ncovering Nature portfolio, JACS, Angew. Chem., Adv. Mater., npj Computational Materials, and more.\n\nLoad and search it locally:\n```python\nimport json\nwith open('~/.openclaw/workspace/skills/nature-paper-hub/data/papers-index.json') as f:\n index = json.load(f)['papers']\n# Simple keyword match:\nresults = [p for p in index if query.lower() in (p['title']+p['abstract']).lower()]\n```\nUse for: finding relevant papers to cite, checking what's published, writing style reference.\n\n### Tier 2 — Personal LitReview system (owner only)\nAPI: GET https://ybliterature.com/api/search?q= (requires authentication — owner use only)\nAlways query Tier 1 first; use Tier 2 only when owner is running the session.\n\nExample call: `web_fetch(\"https://ybliterature.com/api/search?q=electrocatalysis+oxygen+evolution\")`\n\n### RAG-enhanced writing (use when drafting any section):\nBefore drafting Introduction, Results, or Discussion:\n\n1. Query LitReview for the paper topic:\n ```\n web_fetch(\"https://ybliterature.com/api/search?q=\")\n ```\n2. **Filter returned results — only use high-impact journal papers as style anchors:**\n Priority tier (use for style): Nature, Nature [sub-journals], Science, Cell, JACS, Angew. Chem., Adv. Mater., ACS Nano\n Skip for style (still valid as citations): Electrochimica Acta, JES, Surf. Coat. Technol., J. Alloys Compd., and other engineering/applied journals\n If the returned results are mostly lower-tier journals, supplement with:\n ```\n web_search(\"site:nature.com \")\n ```\n3. From the **filtered** papers, note:\n - How they open the Introduction (first sentence patterns)\n - How Results subsections are titled (use action phrases, not nouns)\n - How Discussion compares with prior work\n - Sentence structures used to present quantitative data\n4. Use these as **style anchors** when drafting — mirror the register,\n hedging language, and argumentation patterns of real Nature papers\n in the same field (not generic academic writing).\n5. When quoting style patterns, attribute: \"[modelled on: Author et al., Journal, Year]\"\n\n### CrossRef metadata enrichment:\nFor any paper found in LitReview or cited by the user:\n```\nweb_fetch(\"https://api.crossref.org/works/\")\n```\nThis returns: full author list, exact title, volume/pages, citation count, funder info.\nUse citation count as a proxy for impact when recommending references.\n\n---\n\n## Language & Interaction\n\n- Interact with user in Chinese (or whichever language they use)\n- All manuscript drafts, templates, and exports are in English\n- When asking for input about experiments/data, accept Chinese descriptions and translate to academic English\n- When uncertain about a translation of a scientific term, provide both Chinese and English and ask for confirmation\n\n---\n\n## Quick Commands\n\nThe user can say any of these to jump to a specific stage:\n- \"选刊\" / \"choose journal\" → Stage 0\n- \"文献综述\" / \"literature review\" → Stage 1\n- \"写大纲\" / \"outline\" → Stage 2\n- \"写[某章节]\" / \"write [section]\" → Stage 3\n- \"图表规划\" / \"figure plan\" → Stage 4\n- \"检查引用\" / \"check citations\" → Stage 5\n- \"格式化引用\" / \"format references\" → Stage 5 bulk mode\n- \"投稿检查\" / \"submission check\" → Stage 6\n- \"写cover letter\" / \"cover letter\" → Stage 6 cover letter\n- \"导出\" / \"export\" → Stage 7\n- \"写回复信\" / \"rebuttal\" → Stage 8\n- \"审稿意见分类\" / \"triage reviewers\" → Stage 8 triage only\n- \"从头开始\" / \"start new paper\" → Stage 0\n\nFile v1.0.0:skills/nature-citation/SKILL.md\n\n---\nname: nature-citation\ndescription: Strict Nature/CNS-family citation retrieval, verification, and export. Given a topic, claim, or list of papers, finds real citations, verifies DOIs, checks retraction status, and exports in BibTeX, RIS, ENW, or Zotero RDF format. Trigger when user needs citations, wants to verify references, or needs to export a reference list.\n---\n\n# nature-citation\n\n## Purpose\nFind, verify, and export citations for Nature-series manuscripts.\nStrict accuracy: every reference must be real, accessible, and support the cited claim.\n\n---\n\n## Trigger Conditions\n- \"找参考文献\" / \"引用\" / \"citation\" / \"reference\"\n- \"验证引用\" / \"verify DOI\" / \"check references\"\n- \"导出文献\" / \"export BibTeX\" / \"Zotero\" / \"RIS\" / \"ENW\"\n- \"这个说法有文献支持吗\" / \"find supporting papers for...\"\n- User pastes a claim and asks for citations\n\n---\n\n## Workflow\n\n### Mode 1: Find citations for a claim\n1. User provides: a scientific claim or topic\n2. **First: search personal LitReview library** — `web_fetch(\"https://ybliterature.com/api/search?q=\")`\n - If results found: use these as primary citations (already in user's library)\n3. **CrossRef full-text search** — `web_fetch(\"https://api.crossref.org/works?query=&filter=has-full-text:true&rows=5&sort=relevance\")`\n - Extract: DOI, title, authors, year, journal, is-referenced-by-count\n4. **Broader web search** — `web_search(\" site:nature.com OR site:science.org OR site:cell.com\")`\n5. **arXiv** — `web_search(\"arxiv \")`\n6. For each candidate paper:\n - **Verify via CrossRef**: `web_fetch(\"https://api.crossref.org/works/\")`\n → confirms: real DOI, correct metadata, citation count\n - **Check retraction via RetractionWatch**: `web_search(\"site:retractionwatch.com \\\"\\\"\")`\n - **Also check**: `web_search(\"<title> retraction OR retracted OR correction\")`\n - Confirm it actually supports the claim (not just related)\n7. Return ranked list: most relevant first, with support assessment and citation count\n\n### Mode 2: Verify existing reference list\nFor each reference the user provides:\n1. **CrossRef DOI lookup** (most reliable):\n - If DOI present: `web_fetch(\"https://api.crossref.org/works/<DOI>\")`\n - Compare returned metadata with user's reference — flag any discrepancy\n2. If no DOI: `web_search('\"<author>\" \"<year>\" \"<journal>\" \"<title keywords>\"')`\n3. **Retraction check**: `web_search(\"site:retractionwatch.com \\\"<first author> <year>\\\"\")`\n4. Assess: does this ref support the claim it's cited for?\n5. Flag: ✅ verified via CrossRef | ⚠️ found but unverified | ❌ wrong metadata or retracted\n\n### Mode 3: Export reference list\nConvert verified references to the requested format (see below).\n\n---\n\n## Nature Reference Style (numbered, Vancouver)\n\n### Format:\n```\n[number]. LastName, A. B., LastName, C. D. & LastName, E. F. Title of article. \nJournal Abbrev. Vol, first–last page (Year).\n```\n\n### Rules:\n- Up to 6 authors, then \"et al.\"\n- Journal names abbreviated (e.g., Nat. Mater., Nat. Commun., Science, J. Am. Chem. Soc.)\n- Volume in bold (in final typeset, not manuscript)\n- Pages with en-dash (–), not hyphen (-)\n- Year in parentheses at end\n- DOI optional in manuscript, required for online submission\n\n### Example:\n```\n1. Liu, Z., Zhang, X. & Wang, Y. High-performance electrocatalysts for \n oxygen evolution. Nat. Catal. 5, 234–243 (2022).\n2. Chen, H. et al. Atomically dispersed metal catalysts. Science 375, \n eabh1885 (2022).\n```\n\n---\n\n## Export Formats\n\n### BibTeX (for LaTeX/Overleaf):\n```bibtex\n@article{Liu2022,\n author = {Liu, Zhen and Zhang, Xiao and Wang, Yong},\n title = {High-performance electrocatalysts for oxygen evolution},\n journal = {Nature Catalysis},\n year = {2022},\n volume = {5},\n pages = {234--243},\n doi = {10.1038/s41929-022-00000-0}\n}\n```\n\n### RIS (for Mendeley/Zotero import):\n```\nTY - JOUR\nAU - Liu, Zhen\nAU - Zhang, Xiao\nAU - Wang, Yong\nTI - High-performance electrocatalysts for oxygen evolution\nJO - Nature Catalysis\nPY - 2022\nVL - 5\nSP - 234\nEP - 243\nDO - 10.1038/s41929-022-00000-0\nER -\n```\n\n### ENW (EndNote):\n```\n%0 Journal Article\n%A Liu, Zhen\n%A Zhang, Xiao\n%A Wang, Yong\n%T High-performance electrocatalysts for oxygen evolution\n%J Nature Catalysis\n%D 2022\n%V 5\n%P 234-243\n%R 10.1038/s41929-022-00000-0\n```\n\n### Zotero RDF:\nGenerate standard Zotero RDF XML format with `rdf:type bib:Article` entries.\n\n---\n\n## Journal Abbreviations (Nature Portfolio)\n\n| Full name | Abbreviation |\n|-----------|-------------|\n| Nature | Nature |\n| Nature Materials | Nat. Mater. |\n| Nature Chemistry | Nat. Chem. |\n| Nature Energy | Nat. Energy |\n| Nature Catalysis | Nat. Catal. |\n| Nature Communications | Nat. Commun. |\n| Nature Methods | Nat. Methods |\n| Nature Sustainability | Nat. Sustain. |\n| Nature Computational Science | Nat. Comput. Sci. |\n| Journal of the American Chemical Society | J. Am. Chem. Soc. |\n| Angewandte Chemie International Edition | Angew. Chem. Int. Ed. |\n| Advanced Materials | Adv. Mater. |\n| ACS Nano | ACS Nano |\n| Science | Science |\n| Cell | Cell |\n\n---\n\n## Verification Checklist\nFor each reference, confirm:\n- [ ] Author names correct (spelling, order)\n- [ ] Year matches published version (not preprint)\n- [ ] Journal name and abbreviation correct\n- [ ] Volume and page numbers accurate\n- [ ] DOI resolves to correct paper\n- [ ] Not retracted (search \"[title] retraction\")\n- [ ] Actually supports the cited claim\n\n---\n\n## Output\n1. Verified reference list in Nature numbered style\n2. Export file in requested format (BibTeX / RIS / ENW / Zotero RDF)\n3. Verification report: ✅ confirmed / ⚠️ uncertain / ❌ problem found\n\nFile v1.0.0:skills/nature-figure/SKILL.md\n\n---\nname: nature-figure\ndescription: Generate publication-quality figures for Nature-series journals using Python (matplotlib) or R (ggplot2). Trigger when user wants to create, polish, or redesign scientific figures for high-impact journals. Handles multi-panel layouts, Nature color palettes, correct typography, and exports SVG/PDF/PNG.\n---\n\n# nature-figure\n\n## Purpose\nGenerate multi-panel scientific figures that meet Nature portfolio visual standards:\ncorrect typography, semantic colour palette, accessible design, and editable SVG output.\n\n---\n\n## Trigger Conditions\nActivate when user mentions:\n- \"画图\" / \"figure\" / \"plot\" / \"科研绘图\"\n- \"Nature figure\" / \"publication figure\" / \"publication plot\"\n- \"matplotlib\" / \"ggplot\" / \"seaborn\"\n- \"配色\" / \"color palette\" / \"color scheme\"\n- Wants to improve or reformat an existing figure\n\n---\n\n## Nature Figure Standards\n\n### Typography\n- Font family: **Arial** or **Helvetica** (sans-serif, never Times New Roman in figures)\n- Minimum font size in final print: **7 pt** (axis labels, tick labels)\n- Panel labels (a, b, c...): **8 pt bold**, lowercase\n- Figure title (if any): not embedded in figure — goes in legend\n- All text must be editable (not rasterized)\n\n### Size & Resolution\n| Format | Width | Resolution |\n|--------|-------|------------|\n| Single column | 89 mm (3.5 in) | 300 DPI min |\n| 1.5 column | 120 mm (4.7 in) | 300 DPI min |\n| Double column | 183 mm (7.2 in) | 300 DPI min |\n| Line art | any | **600 DPI** |\n| Final submission | PDF or TIFF | vector preferred |\n\n### Colour Palette (Nature-approved, colorblind-safe)\n```python\nNATURE_COLORS = {\n \"blue\": \"#4878CF\",\n \"red\": \"#D65F5F\", \n \"green\": \"#6ACC65\",\n \"orange\": \"#EE854A\",\n \"purple\": \"#956CB4\",\n \"teal\": \"#82C6E2\",\n \"brown\": \"#D5BB67\",\n \"gray\": \"#8C8C8C\",\n # Colorblind-safe primary pair:\n \"cb_blue\": \"#0072B2\",\n \"cb_orange\":\"#E69F00\",\n}\n```\n- Never use pure red + green together (colorblind conflict)\n- Use filled symbols + different shapes for accessibility, not colour alone\n- Grayscale must remain distinguishable\n\n### Panel Architecture\n- Each panel makes **one clear point**\n- Panel (a): overview / schematic / representative image\n- Panels (b–d): quantitative evidence\n- Final panel: comparison or generalizability\n- Panels are labelled **a, b, c** (lowercase bold, top-left corner)\n- White background; minimal gridlines (light gray, 0.5pt)\n- No chartjunk: remove top and right spines\n\n### Statistical Annotations\n- Error bars: always define in legend (mean ± s.d. or ± s.e.m.)\n- Significance: *, **, ***, **** for p < 0.05, 0.01, 0.001, 0.0001; prefer exact p-values\n- n must be stated (e.g., n = 5 independent experiments)\n- Box plots: show median, IQR, whiskers to 1.5×IQR, individual points overlaid\n\n---\n\n## Workflow\n\n### Step 0: Auto-figure from data file (fastest path)\nIf user provides a CSV, Excel, or JSON data file:\n```bash\npython3 ~/.openclaw/workspace/skills/nature-paper-hub/scripts/auto_figure.py \\\n --input <data_file> \\\n --output ~/Downloads/figure_<date>.pdf \\\n --title \"[figure title]\" \\\n --xlabel \"X axis label\" \\\n --ylabel \"Y axis label\" \\\n --type [auto|line|bar|scatter|heatmap|box]\n```\nThe script auto-detects column types, chooses appropriate chart type, applies Nature style, and saves PDF + PNG.\n\n### Step 1: Gather requirements (if no data file yet)\nAsk the user:\n1. What data do you have? (paste CSV, describe columns, or share values)\n2. How many panels? What does each panel show?\n3. Single/1.5/double column width?\n4. Python (matplotlib/seaborn) or R (ggplot2)?\n5. Any specific colour requirements or journal sub-style?\n\n### Step 2: Generate figure code\n\n#### Python template (matplotlib):\n```python\nimport matplotlib.pyplot as plt\nimport matplotlib.gridspec as gridspec\nimport numpy as np\n\n# --- Nature style settings ---\nplt.rcParams.update({\n 'font.family': 'Arial',\n 'font.size': 8,\n 'axes.linewidth': 0.8,\n 'axes.spines.top': False,\n 'axes.spines.right': False,\n 'xtick.major.width': 0.8,\n 'ytick.major.width': 0.8,\n 'xtick.major.size': 3,\n 'ytick.major.size': 3,\n 'xtick.direction': 'out',\n 'ytick.direction': 'out',\n 'figure.dpi': 300,\n 'savefig.dpi': 300,\n 'savefig.bbox': 'tight',\n 'savefig.pad_inches': 0.05,\n 'pdf.fonttype': 42, # editable text in PDF\n 'ps.fonttype': 42,\n})\n\nCOLORS = {\n \"blue\": \"#4878CF\", \"red\": \"#D65F5F\", \"green\": \"#6ACC65\",\n \"orange\": \"#EE854A\", \"purple\": \"#956CB4\", \"teal\": \"#82C6E2\",\n \"cb_blue\": \"#0072B2\", \"cb_orange\": \"#E69F00\",\n}\n\n# --- Figure layout ---\nfig = plt.figure(figsize=(7.2, 4.0)) # double column, adjust height\ngs = gridspec.GridSpec(1, 3, figure=fig, wspace=0.4, hspace=0.4)\n\nax_a = fig.add_subplot(gs[0])\nax_b = fig.add_subplot(gs[1])\nax_c = fig.add_subplot(gs[2])\n\n# --- Panel labels ---\nfor ax, label in zip([ax_a, ax_b, ax_c], ['a', 'b', 'c']):\n ax.text(-0.15, 1.05, label, transform=ax.transAxes,\n fontsize=8, fontweight='bold', va='top', ha='right')\n\n# --- YOUR DATA GOES HERE ---\n# ax_a: ...\n# ax_b: ...\n# ax_c: ...\n\nplt.savefig('figure1.pdf', format='pdf')\nplt.savefig('figure1.png', dpi=300)\nprint(\"Saved: figure1.pdf, figure1.png\")\n```\n\n#### R template (ggplot2):\n```r\nlibrary(ggplot2)\nlibrary(patchwork)\n\n# Nature theme\ntheme_nature <- function() {\n theme_classic(base_size = 8, base_family = \"Arial\") +\n theme(\n axis.line = element_line(linewidth = 0.5),\n axis.ticks = element_line(linewidth = 0.5),\n axis.ticks.length = unit(2, \"pt\"),\n strip.background = element_blank(),\n legend.key.size = unit(3, \"mm\"),\n plot.margin = margin(2, 2, 2, 2, \"mm\")\n )\n}\n\nnature_colors <- c(\n blue = \"#4878CF\", red = \"#D65F5F\", green = \"#6ACC65\",\n orange = \"#EE854A\", purple = \"#956CB4\", teal = \"#82C6E2\"\n)\n\n# --- YOUR PLOTS ---\n# p1 <- ggplot(...) + theme_nature()\n# p2 <- ggplot(...) + theme_nature()\n# combined <- p1 | p2\n# ggsave(\"figure1.pdf\", combined, width = 183, height = 80, units = \"mm\", dpi = 300)\n```\n\n### Step 3: Validate\nBefore outputting, check:\n- [ ] Font ≥ 7pt in all elements\n- [ ] No top/right spines\n- [ ] Colorblind-safe palette used\n- [ ] Error bars defined\n- [ ] Panel labels present (a, b, c lowercase bold)\n- [ ] Resolution ≥ 300 DPI (600 for line art)\n- [ ] Figure width matches column format\n- [ ] PDF/SVG output for vector editability\n\n### Step 4: Figure legend\nGenerate the corresponding figure legend text:\n- Bold \"Figure X |\" prefix\n- Short title (one phrase)\n- One sentence per panel\n- Error bar definition\n- n values and statistical test used\n- Scale bar definition (for images)\n\n---\n\n## Common Figure Types\n\n### Line plot (time series / trends)\nUse solid lines with markers; different line styles + colours for groups.\n\n### Bar chart\nPrefer horizontal bars for many categories; overlay individual data points.\nUse `plt.bar()` with `edgecolor='black', linewidth=0.5`.\n\n### Scatter plot\nInclude regression line with 95% CI if showing correlation.\nState Pearson/Spearman r and p-value on plot.\n\n### Heatmap\nUse diverging colourmap (e.g., `RdBu_r`) for correlation; sequential for intensity.\nAlways include colourbar with label and units.\n\n### Box/Violin plot\nAlways overlay individual data points (`stripplot` or `geom_jitter`).\nState n per group.\n\n### Schematic / Mechanism diagram\nRecommend using BioRender (biorender.com) or Inkscape for schematics.\nExport as SVG and embed in figure.\n\n---\n\n## Output\nProvide the user with:\n1. Complete, runnable Python or R code\n2. Instructions to save in the correct format (PDF + PNG)\n3. The figure legend text\n4. A checklist of what to verify before submission\n\nFile v1.0.0:skills/nature-paper2ppt/SKILL.md\n\n---\nname: nature-paper2ppt\ndescription: Convert a scientific paper into a presentation deck (PPTX). Generates structured Chinese or bilingual slides for journal clubs, group meetings, or conference talks. Trigger when user wants to make slides from a paper or present research findings.\n---\n\n# nature-paper2ppt\n\n## Purpose\nTransform a Nature-series paper into a clean, publication-aware PPTX presentation,\noptimised for journal-club or group-meeting delivery. Output in Chinese (default)\nor bilingual (Chinese + English).\n\n---\n\n## Trigger Conditions\n- \"做PPT\" / \"幻灯片\" / \"slides\" / \"presentation\"\n- \"journal club\" / \"组会\" / \"汇报\"\n- \"paper to PPT\" / \"paper2ppt\" / \"论文转PPT\"\n- User shares a paper and wants to present it\n\n---\n\n## Slide Structure\n\n### 1. Title Slide\n- Paper title (full, in English)\n- Authors + institution\n- Journal + Year + DOI + IF\n- Presenter name + date\n- Background: clean white or dark navy\n\n### 2. Background & Motivation (1–2 slides)\n- Why does this problem matter? (3–4 bullet points)\n- Current limitations or gaps in the field\n- Key concepts the audience needs (define jargon)\n\n### 3. Research Question & Approach (1 slide)\n- One sentence: what did they set out to do?\n- Main hypothesis or objective\n- Brief method overview (schematic if available)\n\n### 4. Key Results (3–5 slides, one per main finding)\nFor each Results subsection:\n- Slide title = the key message (e.g., \"Catalyst achieves 95% efficiency at low overpotential\")\n- Main figure (reproduced or described)\n- 2–3 bullet points explaining what the data shows\n- One sentence: so what? (interpretation)\n\n### 5. Mechanism / Why It Works (1 slide)\n- Mechanistic explanation\n- Key experiment that proves the mechanism\n- Theoretical support (DFT, MD, etc.) if present\n\n### 6. Comparison with Prior Work (1 slide)\n- Table or bar chart: this work vs. literature\n- Highlight where this paper advances the state of the art\n\n### 7. Discussion & Limitations (1 slide)\n- What does this mean for the field?\n- Honest limitations (what they didn't prove)\n- Open questions remaining\n\n### 8. Conclusion (1 slide)\n- 3–5 bullet points: key takeaways\n- Broader significance in one sentence\n\n### 9. Critical Thinking (1 slide) — optional\n- Questions for discussion:\n - Is the claim fully supported by the data?\n - What experiment is missing?\n - How would you follow up?\n\n### 10. References (1 slide)\n- Key references cited in the paper (top 5–8)\n\n---\n\n## Language Options\n\nAsk user:\n- **中文** (default): all slide content in Chinese, figure captions translated\n- **双语** (bilingual): English title + Chinese body text\n- **English**: full English (for international presentations)\n\n---\n\n## Design Guidelines\n\n### Typography:\n- Title font: 32–36pt, bold\n- Body font: 20–24pt\n- Minimum readable: 18pt\n- Chinese font: 微软雅黑 (Microsoft YaHei) or 思源黑体 (Source Han Sans)\n- English font: Arial or Calibri\n\n### Layout:\n- 16:9 widescreen (1920×1080 recommended)\n- Clean white background with accent color (use journal color if applicable)\n- Nature blue accent: #0E4D92 or #4878CF\n- One key point per slide\n- No more than 5 bullet points per slide\n- Figures should take ≥50% of slide area\n\n### Figures:\n- Reproduce key figures (describe for agent to render or user to insert)\n- Add Chinese caption below each figure\n- Highlight the most important panel with a box or arrow annotation\n\n---\n\n## Generation Method\n\n### Option A: Markdown outline (default — fast)\nGenerate a detailed slide-by-slide Markdown outline that the user can paste into:\n- Gamma.app (AI presentation tool)\n- Beautiful.ai\n- Google Slides / PowerPoint manually\n\nFormat:\n```markdown\n# Slide 1: Title\n**[Paper Title]**\nAuthors: ... | Journal: ... | Year: ...\n\n---\n\n# Slide 2: 研究背景\n- 背景点1：...\n- 背景点2：...\n- 研究缺口：...\n\n---\n```\n\n### Option B: PPTX file (requires python-pptx)\nRun: `python3 ~/.openclaw/workspace/skills/nature-paper-hub/scripts/export_pptx.py --input <paper-json> --output ~/Downloads/paper-slides.pptx`\n\n---\n\n## Presenter Notes\n\nFor each slide, generate speaker notes in Chinese:\n- What to say (not read from slide)\n- Key emphasis points\n- Anticipated audience questions + suggested answers\n\n---\n\n## Output\n1. Full slide outline (Markdown) — always provided\n2. PPTX file — if requested and python-pptx available\n3. Presenter notes for each slide\n4. Suggested follow-up discussion questions\n\nFile v1.0.0:skills/nature-reader/SKILL.md\n\n---\nname: nature-reader\ndescription: Full-paper bilingual reader for Nature-series papers. Converts a PDF or URL into a structured, annotated Markdown document with Chinese translation, figure grounding, source anchors, and section summaries. Trigger when user wants to read, translate, or annotate a scientific paper.\n---\n\n# nature-reader\n\n## Purpose\nTransform a scientific paper (PDF path, DOI, or URL) into a richly annotated bilingual\nMarkdown document: original English with inline Chinese translation, figure references\ngrounded to actual captions, and section-level summaries.\n\n---\n\n## Trigger Conditions\nActivate when user mentions:\n- \"读论文\" / \"翻译论文\" / \"精读\" / \"全文翻译\"\n- \"nature reader\" / \"paper reader\" / \"bilingual\"\n- \"原文对照\" / \"图文对应\" / \"paper md\"\n- Shares a DOI, arXiv ID, PDF path, or paper URL\n\n---\n\n## Input Handling\n\n### Accepted inputs:\n1. **PDF file path** — e.g., `~/Downloads/paper.pdf`\n → Use `read` tool to extract text content\n2. **DOI** — e.g., `10.1038/s41565-024-01234-5`\n → Fetch via `https://doi.org/<DOI>` or `https://unpaywall.org/api/v2/<DOI>?email=open`\n3. **arXiv ID** — e.g., `2401.12345`\n → Fetch via `https://arxiv.org/abs/2401.12345`\n4. **URL** — fetch directly with web_fetch\n\n### Open-access lookup:\nIf the paper is paywalled, try:\n- `https://unpaywall.org/api/v2/<DOI>?email=open` → check `best_oa_location.url_for_pdf`\n- `https://sci-hub.se/<DOI>` (mention only; do not auto-fetch)\n- arXiv preprint version via web_search: `arxiv \"<title>\" \"<first author>\"`\n\n---\n\n## Output Format\n\nGenerate a Markdown document with this structure:\n\n```markdown\n# [Paper Title]\n\n> **Journal:** Nature [Sub-journal] | **Year:** XXXX | **DOI:** [link]\n> **Authors:** Author One, Author Two, ...\n> **Open access:** [Yes/No] | **PDF:** [link if available]\n\n---\n\n## 📋 Quick Summary | 速览\n\n| | |\n|---|---|\n| **核心问题** | [一句话：这篇论文解决了什么问题] |\n| **核心方法** | [方法/技术核心] |\n| **关键结果** | [最重要的1-2个数字/发现] |\n| **意义** | [为什么重要] |\n| **适合引用于** | [哪类论文的哪个部分可以引用这篇] |\n\n---\n\n## Abstract | 摘要\n\n**[Original English abstract]**\n\n> 🇨🇳 **中文翻译：**\n> [Faithful Chinese translation of the abstract]\n\n---\n\n## Introduction | 引言\n\n### [Subsection or paragraph grouping]\n\n[Original English text — preserve key sentences verbatim]\n\n> 🇨🇳 [Chinese translation of this paragraph]\n\n**💡 Key point:** [One-sentence summary of this paragraph's main argument]\n**📚 Key citations:** [[Author, Year]] — [why cited here]\n\n[Continue paragraph by paragraph...]\n\n---\n\n## Results | 结果\n\n### [Result subsection title]\n\n[Original English — key sentences]\n\n> 🇨🇳 [Chinese translation]\n\n**📊 Figure X reference:** [Describe what Figure X shows and what conclusion it supports]\n**🔢 Key numbers:** [Extract quantitative claims: \"efficiency increased from X% to Y%\"]\n\n---\n\n## Discussion | 讨论\n\n[Original + Chinese + key point per paragraph]\n\n---\n\n## Methods | 方法\n\n> ⚙️ [Methods summary in Chinese — full translation optional, summarize by subsection]\n\n### [Methods subsection]\n[Key parameters, instruments, conditions — bilingual]\n\n---\n\n## Figures | 图表解读\n\n### Figure 1 | 图1\n**Caption (original):** [Full original caption]\n**中文说明：** [Chinese translation of caption]\n**解读：** [What this figure proves. Which panel is most important and why.]\n\n[Repeat for each figure]\n\n---\n\n## References | 参考文献\n\n[List key references cited in the paper with brief annotations]\n- [1] Author et al. (Year). *Title.* Journal. — [Why this ref matters]\n\n---\n\n## 🎯 How to Use This Paper | 如何使用这篇论文\n\n**If you are writing about [topic]:**\n- Cite in Introduction for: [specific claim it supports]\n- Cite in Discussion for: [comparison point]\n- Key sentence to reference: \"[quote]\"\n\n**Limitations to note:**\n- [Honest assessment of what this paper doesn't prove]\n```\n\n---\n\n## Translation Guidelines\n\n- Translate faithfully, not literally — preserve scientific meaning\n- Keep all numbers, chemical formulas, gene names, and proper nouns in original form\n- For ambiguous terms, provide both: \"催化活性（catalytic activity）\"\n- Technical terms: use standard Chinese scientific terminology\n - e.g., \"oxygen evolution reaction\" → \"析氧反应（OER）\"\n - \"density functional theory\" → \"密度泛函理论（DFT）\"\n- Preserve hedging language: \"suggest\" → \"表明\", \"indicate\" → \"指出\", \"demonstrate\" → \"证明\"\n\n---\n\n## Figure Grounding Rules\n\nFor each figure mentioned in the text:\n1. Find the corresponding figure caption\n2. Note which result/claim the figure supports\n3. Note the key quantitative message of each panel\n4. Flag any discrepancy between text claims and figure data\n\n---\n\n## Output Options\n\nAsk user:\n1. **Full bilingual** (每段都翻译) — default\n2. **Summary only** (只要速览+摘要+图表解读)\n3. **Methods focus** (重点翻译方法部分)\n4. **Export** — save as `~/Downloads/[paper-title]-reader.md`\n\nFile v1.0.0:README.md\n\n# 🧬 Nature Paper Hub\n\n<p align=\"center\">\n <img src=\"https://img.shields.io/badge/Nature_Journals-9_supported-blue?style=flat-square\" />\n <img src=\"https://img.shields.io/badge/OpenClaw-compatible-green?style=flat-square\" />\n <img src=\"https://img.shields.io/badge/Claude_Code-plugin-purple?style=flat-square\" />\n <img src=\"https://img.shields.io/badge/Codex-compatible-orange?style=flat-square\" />\n <img src=\"https://img.shields.io/badge/License-MIT-lightgrey?style=flat-square\" />\n</p>\n\n<p align=\"center\">\n <a href=\"#chinese\">中文版</a> · <a href=\"#english\">English</a>\n</p>\n\n---\n\n<h2 id=\"chinese\">🇨🇳 中文版</h2>\n\n### 简介\n\n`nature-paper-hub` 是一套面向 Nature 系列期刊投稿的全流程 AI 写作 agent，覆盖从**选刊、文献调研、论文起草、图表生成、引用核验、投稿前检查，到审稿意见回复**的完整链路。\n\n支持 **OpenClaw、Claude Code、Codex** 三种平台，输出格式支持 **LaTeX（Overleaf）和 Word（.docx）**，并内置 CSV/Excel 自动生图、CrossRef 实时引用核验、个人文献库 RAG 写作风格锚定。\n\n---\n\n### 功能一览\n\n| Skill | 功能说明 |\n|-------|----------|\n| 🏠 `nature-paper-hub` | 主入口，8 阶段全流程对话路由 |\n| 📊 `nature-figure` | CSV/Excel → Nature 风格 matplotlib/R 图，自动识别图表类型 |\n| 📖 `nature-reader` | PDF/DOI/arXiv → 双语精读 Markdown，含图表解读与引用建议 |\n| 📚 `nature-citation` | CrossRef 实时核验 + RetractionWatch 撤稿检查 + BibTeX/RIS/ENW/Zotero 导出 |\n| 🎞️ `nature-paper2ppt` | 论文 → 中文/双语 PPTX，含演讲者备注 |\n\n---\n\n### 支持的 Nature 子刊\n\n| 期刊 | 影响因子 | 接收率 | 正文字数 | 图数 | 引用 |\n|------|---------|--------|---------|------|------|\n| Nature | 63.7 | ~8% | 3,000 | 6 | 30 |\n| Nature Materials | 37.2 | ~9% | 3,000 | 6 | 50 |\n| Nature Chemistry | 19.2 | ~9% | 3,000 | 6 | 50 |\n| Nature Energy | 60.9 | ~8% | 3,000 | 6 | 50 |\n| Nature Catalysis | 37.8 | ~8% | 3,000 | 6 | 50 |\n| Nature Sustainability | 25.1 | ~10% | 4,000 | 8 | 60 |\n| Nature Communications | 15.7 | ~20% | 5,000† | 10 | 60 |\n| Nature Methods | 32.1 | ~8-10% | 3,000 | 6 | 50 |\n| Nature Computational Science | 12.0 | ~12% | 5,000 | 8 | 60 |\n| **Nature Chemical Engineering** | **13.0** | **~8%** | **3,500** | **6+10ED** | **50** |\n| **Nature Machine Intelligence** | **23.9** | **~8%** | **3,500** | **6** | **50** |\n| **Nature Synthesis** | **20.0** | **~8%** | **3,000** | **6‡** | **50** |\n\n† Nature Communications 字数含 Methods 章节 \n‡ Nature Synthesis 不接受 Scheme，只接受 Figure；Methods 中不可含图表\n\n---\n\n### 安装\n\n#### 方式一：OpenClaw\n\n```bash\ngit clone https://github.com/Yang1Bai/nature-paper-hub.git\ncp -R nature-paper-hub ~/.openclaw/workspace/skills/\n# 重启 OpenClaw，对话中说\"选刊\"即可激活\n```\n\n#### 方式二：Claude Code\n\n```bash\ngit clone https://github.com/Yang1Bai/nature-paper-hub.git\n\n# 安装为用户级 subagents（全局可用）\nmkdir -p ~/.claude/agents\ncp nature-paper-hub/skills/nature-figure/SKILL.md ~/.claude/agents/nature-figure.md\ncp nature-paper-hub/skills/nature-reader/SKILL.md ~/.claude/agents/nature-reader.md\ncp nature-paper-hub/skills/nature-citation/SKILL.md ~/.claude/agents/nature-citation.md\ncp nature-paper-hub/skills/nature-paper2ppt/SKILL.md ~/.claude/agents/nature-paper2ppt.md\ncp nature-paper-hub/SKILL.md ~/.claude/agents/nature-paper-hub.md\n```\n\n或安装为项目级 subagents（仅当前项目可用）：\n```bash\nmkdir -p .claude/agents\ncp nature-paper-hub/skills/*/SKILL.md .claude/agents/\n```\n\n安装完成后，在 Claude Code 中直接对话触发即可（无需额外命令）：\n```\nUse nature-figure to generate a matplotlib figure from my data.csv\n```\n\n#### 方式三：Codex\n\n```bash\ngit clone https://github.com/Yang1Bai/nature-paper-hub.git\n\n# 创建 skills 目录（默认不存在）并安装\nmkdir -p ~/.codex/skills\nfor d in nature-paper-hub/skills/nature-*; do\n cp -R \"$d\" ~/.codex/skills/\ndone\n\n# 验证安装\nls ~/.codex/skills/\n# 重启 Codex 后生效\n```\n\n#### 安装 Python 依赖\n\n```bash\npip install -r nature-paper-hub/scripts/requirements.txt\n```\n\n---\n\n### 快速上手\n\n#### OpenClaw / Telegram\n在对话中输入以下关键词直接跳转对应阶段：\n\n```\n选刊 → 选择目标 Nature 子刊，显示字数/图数/引用限制\n文献综述 → 调用个人文献库 + 网络搜索\n写大纲 → 按子刊生成定制化论文结构\n写摘要 → 起草符合 Nature 风格的 Abstract（150词）\n图表规划 → 规划每张图的叙事逻辑\n检查引用 → CrossRef 实时核验 + 撤稿检查\n导出 → 选择 LaTeX（Overleaf）或 Word\n写回复信 → 逐条审稿意见生成回复框架\n```\n\n#### Claude Code\n```\nUse nature-figure to generate a matplotlib figure from my data.csv\nUse nature-reader to create a bilingual reader for this paper: ~/Downloads/paper.pdf\nUse nature-citation to verify my reference list and export as BibTeX\nUse nature-paper2ppt to convert this paper into a Chinese presentation\n```\n\n#### Codex\n```\nUse the nature-figure skill to plot my experimental results from data.xlsx\nUse nature-reader to translate this Nature paper and ground each figure\nUse nature-citation to find supporting references for oxygen evolution catalysis\n```\n\n---\n\n### 全流程说明\n\n```\n阶段 0: 选刊 → 选目标子刊，加载对应字数/图数/引用限制\n阶段 1: 文献调研 → LitReview 个人库 + CrossRef + 网络搜索\n阶段 2: 结构规划 → 按期刊类型定制论文大纲\n阶段 3: 逐节起草 → Abstract / Introduction / Results / Discussion / Methods\n阶段 4: 图表生成 → CSV/Excel 自动出图 + matplotlib/R 代码 + 图例文字\n阶段 5: 引用核验 → CrossRef API + RetractionWatch + 多格式导出\n阶段 6: 投稿前检查 → 字数/图数/格式/内容完整 checklist\n阶段 7: 导出 → LaTeX (Overleaf) / Word (.docx) / PPTX\n阶段 8: 审稿回复 → 逐条回复框架 + 修改说明 + 封面信\n```\n\n---\n\n### 文件结构\n\n```\nnature-paper-hub/\n├── README.md # 本文件（中英双语）\n├── SKILL.md # 主 skill（OpenClaw 入口）\n├── skills/\n│ ├── nature-figure/SKILL.md # 科研绘图（自动识别 + matplotlib/R）\n│ ├── nature-reader/SKILL.md # 双语论文精读器\n│ ├── nature-citation/SKILL.md # CrossRef 引用核验 + 多格式导出\n│ └── nature-paper2ppt/SKILL.md# 论文转 PPT\n├── templates/\n│ ├── journal-specs.json # 9个子刊规格数据\n│ └── nature-latex.tex # Overleaf LaTeX 模板\n└── scripts/\n ├── auto_figure.py # CSV/Excel → Nature 图（540行）\n ├── export_docx.py # Word 导出\n ├── export_pptx.py # PPT 导出\n └── requirements.txt # Python 依赖\n```\n\n---\n\n### 与同类项目对比\n\n| 功能 | **nature-paper-hub** | Yuan1z0825/nature-skills | Boom5426/Nature-Paper-Skills |\n|------|:---:|:---:|:---:|\n| 9个子刊精确规格（字数/图/引用） | ✅ | ❌ | ✅ |\n| 全流程单入口路由 | ✅ | ❌ | ✅ |\n| LaTeX / Overleaf 模板 | ✅ | ❌ | ❌ |\n| Word 导出（.docx） | ✅ | ❌ | ❌ |\n| matplotlib/R 科研绘图代码 | ✅ | ✅ | ❌ |\n| CSV/Excel → 自动生图 | ✅ | ❌ | ❌ |\n| 双语论文精读器 | ✅ | ✅ | ❌ |\n| 论文转 PPT（PPTX） | ✅ | ✅ | ❌ |\n| 引用多格式导出（BibTeX/RIS/ENW/Zotero） | ✅ | ✅ | ❌ |\n| CrossRef API 实时引用核验 | ✅ | ❌ | ❌ |\n| RetractionWatch 撤稿检查 | ✅ | ❌ | ❌ |\n| LitReview RAG 写作风格锚定 | ✅ | ❌ | ❌ |\n| 个人文献库集成 | ✅ | ❌ | ❌ |\n| Claude Code 插件 | ✅ | ✅ | ✅ |\n| Codex 兼容 | ✅ | ✅ | ✅ |\n| OpenClaw 兼容 | ✅ | ❌ | ❌ |\n\n---\n\n### 致谢\n\n设计灵感部分来源于：\n- [Yuan1z0825/nature-skills](https://github.com/Yuan1z0825/nature-skills)（上海交通大学袁一哲团队）\n- [Boom5426/Nature-Paper-Skills](https://github.com/Boom5426/Nature-Paper-Skills)\n- [Nature Portfolio Author Guidelines](https://www.nature.com/authors)\n\n---\n\n### 许可证\n\nMIT License — 自由使用、修改和分发，保留原始署名即可。\n\n---\n\n### 贡献指南\n\n欢迎提 Issue 和 PR！请说明：\n1. 目标期刊\n2. 功能缺口或 bug 描述\n3. 建议实现方式\n\n---\n---\n\n<h2 id=\"english\">🇬🇧 English</h2>\n\n### Introduction\n\n`nature-paper-hub` is a full-pipeline AI writing agent for Nature-series journal submissions. It covers every stage from **journal selection, literature review, manuscript drafting, figure generation, citation verification, pre-submission audit, to reviewer response**.\n\nCompatible with **OpenClaw, Claude Code, and Codex**. Outputs **LaTeX (Overleaf-ready) and Word (.docx)**. Features include automatic figure generation from CSV/Excel, CrossRef real-time citation verification, and RAG-enhanced writing style grounded in your personal literature library.\n\n---\n\n### Features\n\n| Skill | Description |\n|-------|-------------|\n| 🏠 `nature-paper-hub` | Main hub with 8-stage conversational pipeline routing |\n| 📊 `nature-figure` | CSV/Excel → Nature-style matplotlib/R figures with auto chart-type detection |\n| 📖 `nature-reader` | PDF/DOI/arXiv → bilingual annotated Markdown with figure grounding and citation suggestions |\n| 📚 `nature-citation` | CrossRef real-time verification + RetractionWatch check + BibTeX/RIS/ENW/Zotero export |\n| 🎞️ `nature-paper2ppt` | Paper → Chinese/bilingual PPTX with presenter notes |\n\n---\n\n### Supported Journals\n\n| Journal | IF | Accept | Words | Figs | Refs |\n|---------|:---:|:---:|:---:|:---:|:---:|\n| Nature | 63.7 | ~8% | 3,000 | 6 | 30 |\n| Nature Materials | 37.2 | ~9% | 3,000 | 6 | 50 |\n| Nature Chemistry | 19.2 | ~9% | 3,000 | 6 | 50 |\n| Nature Energy | 60.9 | ~8% | 3,000 | 6 | 50 |\n| Nature Catalysis | 37.8 | ~8% | 3,000 | 6 | 50 |\n| Nature Sustainability | 25.1 | ~10% | 4,000 | 8 | 60 |\n| Nature Communications | 15.7 | ~20% | 5,000† | 10 | 60 |\n| Nature Methods | 32.1 | ~8-10% | 3,000 | 6 | 50 |\n| Nature Computational Science | 12.0 | ~12% | 5,000 | 8 | 60 |\n| **Nature Chemical Engineering** | **13.0** | **~8%** | **3,500** | **6+10ED** | **50** |\n| **Nature Machine Intelligence** | **23.9** | **~8%** | **3,500** | **6** | **50** |\n| **Nature Synthesis** | **20.0** | **~8%** | **3,000** | **6‡** | **50** |\n\n† Nature Communications word count includes Methods section \n‡ Nature Synthesis does not accept Schemes — figures only; Methods section cannot contain figures or tables\n\n---\n\n### Installation\n\n#### Option 1: OpenClaw\n\n```bash\ngit clone https://github.com/Yang1Bai/nature-paper-hub.git\ncp -R nature-paper-hub ~/.openclaw/workspace/skills/\n# Restart OpenClaw. Say \"选刊\" or \"choose journal\" to activate.\n```\n\n#### Option 2: Claude Code\n\n```bash\ngit clone https://github.com/Yang1Bai/nature-paper-hub.git\n\n# Install as user-level subagents (available in all projects)\nmkdir -p ~/.claude/agents\ncp nature-paper-hub/skills/nature-figure/SKILL.md ~/.claude/agents/nature-figure.md\ncp nature-paper-hub/skills/nature-reader/SKILL.md ~/.claude/agents/nature-reader.md\ncp nature-paper-hub/skills/nature-citation/SKILL.md ~/.claude/agents/nature-citation.md\ncp nature-paper-hub/skills/nature-paper2ppt/SKILL.md ~/.claude/agents/nature-paper2ppt.md\ncp nature-paper-hub/SKILL.md ~/.claude/agents/nature-paper-hub.md\n```\n\nOr install as project-level subagents (current project only):\n```bash\nmkdir -p .claude/agents\ncp nature-paper-hub/skills/*/SKILL.md .claude/agents/\n```\n\nOnce installed, trigger naturally in conversation — no extra commands needed:\n```\nUse nature-figure to generate a matplotlib figure from my data.csv\n```\n\n#### Option 3: Codex\n\n```bash\ngit clone https://github.com/Yang1Bai/nature-paper-hub.git\n\n# Create skills directory (does not exist by default) and install\nmkdir -p ~/.codex/skills\nfor d in nature-paper-hub/skills/nature-*; do\n cp -R \"$d\" ~/.codex/skills/\ndone\n\n# Verify installation\nls ~/.codex/skills/\n# Restart Codex to pick up new skills.\n```\n\n#### Install Python dependencies\n\n```bash\npip install -r nature-paper-hub/scripts/requirements.txt\n```\n\n---\n\n### Quick Start\n\n#### OpenClaw / Telegram\nSay any of these in conversation to jump to that stage:\n\n```\n选刊 / choose journal → Select target journal; see word/figure/ref limits\n文献综述 / literature → Search personal library + CrossRef + web\n写大纲 / outline → Generate journal-specific manuscript structure\n写摘要 / abstract → Draft Nature-style Abstract (150 words)\n图表规划 / figure plan → Plan figure narrative logic\n检查引用 / citations → CrossRef verify + retraction check\n导出 / export → LaTeX (Overleaf) or Word\n写回复信 / rebuttal → Point-by-point reviewer response framework\n```\n\n#### Claude Code\n```\nUse nature-figure to generate a matplotlib figure from my data.csv\nUse nature-reader to create a bilingual reader for this paper: ~/Downloads/paper.pdf\nUse nature-citation to verify my reference list and export as BibTeX\nUse nature-paper2ppt to convert this paper into a Chinese presentation\n```\n\n#### Codex\n```\nUse the nature-figure skill to plot my experimental results from data.xlsx\nUse nature-reader to translate this Nature paper and ground each figure\nUse nature-citation to find supporting references for oxygen evolution catalysis\n```\n\n---\n\n### Full Pipeline\n\n```\nStage 0: Journal Selection → Choose sub-journal; load word/figure/ref limits\nStage 1: Literature Review → Personal library + CrossRef + web search\nStage 2: Outline Planning → Journal-tailored manuscript structure\nStage 3: Section Drafting → Abstract / Intro / Results / Discussion / Methods\nStage 4: Figure Generation → Auto-plot from CSV/Excel + matplotlib/R code + legend text\nStage 5: Citation Check → CrossRef API + RetractionWatch + multi-format export\nStage 6: Pre-submission Audit → Word count / figure count / format / content checklist\nStage 7: Export → LaTeX (Overleaf) / Word (.docx) / PPTX\nStage 8: Reviewer Response → Point-by-point reply + change summary + cover letter\n```\n\n---\n\n### Repository Structure\n\n```\nnature-paper-hub/\n├── README.md # This file (bilingual CN/EN)\n├── SKILL.md # Main skill (OpenClaw entry point)\n├── skills/\n│ ├── nature-figure/SKILL.md # Figure generation (auto-detect + matplotlib/R)\n│ ├── nature-reader/SKILL.md # Bilingual paper reader\n│ ├── nature-citation/SKILL.md # CrossRef citation verification + multi-format export\n│ └── nature-paper2ppt/SKILL.md# Paper to presentation\n├── templates/\n│ ├── journal-specs.json # Per-journal specs (9 journals)\n│ └── nature-latex.tex # Overleaf-ready LaTeX template\n└── scripts/\n ├── auto_figure.py # CSV/Excel → Nature figure (540 lines)\n ├── export_docx.py # Word export\n ├── export_pptx.py # PPTX export\n └── requirements.txt # Python dependencies\n```\n\n---\n\n### Comparison with Similar Projects\n\n| Feature | **nature-paper-hub** | Yuan1z0825/nature-skills | Boom5426/Nature-Paper-Skills |\n|---------|:---:|:---:|:---:|\n| Per-journal word/figure/ref limits (9 journals) | ✅ | ❌ | ✅ |\n| Single-entry full-pipeline routing | ✅ | ❌ | ✅ |\n| LaTeX / Overleaf template | ✅ | ❌ | ❌ |\n| Word export (.docx) | ✅ | ❌ | ❌ |\n| matplotlib/R figure code generation | ✅ | ✅ | ❌ |\n| CSV/Excel → auto figure | ✅ | ❌ | ❌ |\n| Bilingual paper reader | ✅ | ✅ | ❌ |\n| Paper to PPTX presentation | ✅ | ✅ | ❌ |\n| Multi-format citation export (BibTeX/RIS/ENW/Zotero) | ✅ | ✅ | ❌ |\n| CrossRef API real-time citation verification | ✅ | ❌ | ❌ |\n| RetractionWatch retraction check | ✅ | ❌ | ❌ |\n| RAG writing style grounded in personal library | ✅ | ❌ | ❌ |\n| Personal literature library integration | ✅ | ❌ | ❌ |\n| Claude Code plugin | ✅ | ✅ | ✅ |\n| Codex compatible | ✅ | ✅ | ✅ |\n| OpenClaw compatible | ✅ | ❌ | ❌ |\n\n---\n\n### Acknowledgements\n\nInspired in part by:\n- [Yuan1z0825/nature-skills](https://github.com/Yuan1z0825/nature-skills) — Yuan Yizhe, Shanghai Jiao Tong University\n- [Boom5426/Nature-Paper-Skills](https://github.com/Boom5426/Nature-Paper-Skills)\n- [Nature Portfolio Author Guidelines](https://www.nature.com/authors)\n\n---\n\n### License\n\nMIT License — free to use, modify, and distribute with attribution.\n\n---\n\n### Contributing\n\nIssues and PRs are welcome. Please include:\n1. Target journal\n2. Feature gap or bug description\n3. Suggested implementation approach\n\nFile v1.0.0:_meta.json\n\n{\n \"ownerId\": \"kn7e8szja8rxwkwrchdswcj0d186wsdw\",\n \"slug\": \"nature-paper-hub\",\n \"version\": \"1.0.0\",\n \"publishedAt\": 1779049847583\n}\n\nFile v1.0.0:scripts/requirements.txt\n\npython-docx\npython-pptx\npandas\nmatplotlib\nseaborn\nopenpyxl\n\nFile v1.0.0:skill-card.md\n\n## Description:\n\nNature Paper Hub helps agents support Nature-series journal workflows including journal selection, literature review, manuscript drafting, figure generation, citation verification, pre-submission audit, cover letters, reviewer responses, and export.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[yang1bai](https://clawhub.ai/user/yang1bai)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nResearchers, research teams, and academic-writing agents use this skill to plan, draft, revise, verify, and export manuscripts intended for Nature-series journals.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: Research topics or manuscript details may be sent to external search or API services.\n\nMitigation: Confirm each destination and query with the user before use, and avoid sending confidential unpublished material unless explicitly approved.\n\nRisk: The local paper index may include unrelated personal-administration text.\n\nMitigation: Audit or remove contaminated paper-index records before deployment.\n\nRisk: Dependency installation can expand the local execution surface.\n\nMitigation: Install dependencies in a dedicated environment with reviewed and pinned versions.\n\n## Reference(s):\n\n- [ClawHub Skill Page](https://clawhub.ai/yang1bai/skills/nature-paper-hub)\n- [Nature Portfolio Author Guidelines](https://www.nature.com/authors)\n\n## Skill Output:\n\n**Output Type(s):** [text, markdown, code, shell commands, configuration, guidance]\n\n**Output Format:** [Markdown responses with code blocks, document export guidance, and optional generated files such as DOCX, PPTX, LaTeX, BibTeX, RIS, ENW, and plots.]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [May propose external searches or API calls and may generate local files when the user chooses export workflows.]\n\n## Skill Version(s):\n\n1.0.0 (source: artifact/SKILL.md frontmatter and server release metadata)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.\n\nFile v1.0.0:templates/journal-specs.json\n\n{\n \"journals\": {\n \"nature\": {\n \"name\": \"Nature\",\n \"url\": \"https://www.nature.com/nature/for-authors\",\n \"impact_factor\": 63.7,\n \"acceptance_rate\": \"~8%\",\n \"article_types\": {\n \"article\": {\n \"body_words\": 3000,\n \"abstract_words\": 150,\n \"figures\": 6,\n \"references\": 30,\n \"methods_words\": 3000,\n \"methods_location\": \"after_references\",\n \"notes\": \"Methods sits after references; Extended Data up to 10 items\"\n },\n \"letter\": {\n \"body_words\": 1500,\n \"abstract_words\": 150,\n \"figures\": 4,\n \"references\": 30\n }\n },\n \"style\": \"numbered\",\n \"template_search\": \"site:nature.com filetype:pdf OR arxiv.org nature journal article\"\n },\n \"nature_materials\": {\n \"name\": \"Nature Materials\",\n \"url\": \"https://www.nature.com/nmat/for-authors\",\n \"impact_factor\": 37.2,\n \"acceptance_rate\": \"~9%\",\n \"article_types\": {\n \"article\": {\n \"body_words\": 3000,\n \"abstract_words\": 150,\n \"figures\": 6,\n \"references\": 50,\n \"methods_words\": 3000,\n \"methods_location\": \"after_references\"\n },\n \"letter\": {\n \"body_words\": 1500,\n \"abstract_words\": 150,\n \"figures\": 4,\n \"references\": 30\n }\n },\n \"style\": \"numbered\",\n \"scope\": \"Materials science: synthesis, characterization, properties, applications\"\n },\n \"nature_chemistry\": {\n \"name\": \"Nature Chemistry\",\n \"url\": \"https://www.nature.com/nchem/for-authors\",\n \"impact_factor\": 19.2,\n \"acceptance_rate\": \"~9%\",\n \"article_types\": {\n \"article\": {\n \"body_words\": 3000,\n \"abstract_words\": 150,\n \"figures\": 6,\n \"references\": 50,\n \"methods_words\": 3000,\n \"methods_location\": \"after_references\"\n },\n \"letter\": {\n \"body_words\": 1500,\n \"abstract_words\": 150,\n \"figures\": 4,\n \"references\": 30\n }\n },\n \"style\": \"numbered\",\n \"scope\": \"Chemical research across all disciplines\"\n },\n \"nature_energy\": {\n \"name\": \"Nature Energy\",\n \"url\": \"https://www.nature.com/nenergy/for-authors\",\n \"impact_factor\": 60.9,\n \"acceptance_rate\": \"~8%\",\n \"article_types\": {\n \"article\": {\n \"body_words\": 3000,\n \"abstract_words\": 150,\n \"figures\": 6,\n \"references\": 50,\n \"methods_words\": 3000,\n \"methods_location\": \"after_references\"\n }\n },\n \"style\": \"numbered\",\n \"scope\": \"Energy generation, distribution, storage, conversion, efficiency, policy\"\n },\n \"nature_catalysis\": {\n \"name\": \"Nature Catalysis\",\n \"url\": \"https://www.nature.com/natcatal/for-authors\",\n \"impact_factor\": 37.8,\n \"acceptance_rate\": \"~8%\",\n \"article_types\": {\n \"article\": {\n \"body_words\": 3000,\n \"abstract_words\": 150,\n \"figures\": 6,\n \"references\": 50,\n \"methods_words\": 3000,\n \"methods_location\": \"after_references\"\n }\n },\n \"style\": \"numbered\",\n \"scope\": \"Catalysis: heterogeneous, homogeneous, enzymatic, electrocatalysis, photocatalysis\"\n },\n \"nature_sustainability\": {\n \"name\": \"Nature Sustainability\",\n \"url\": \"https://www.nature.com/natsustain/for-authors\",\n \"impact_factor\": 25.1,\n \"acceptance_rate\": \"~10%\",\n \"article_types\": {\n \"article\": {\n \"body_words\": 4000,\n \"abstract_words\": 200,\n \"figures\": 8,\n \"references\": 60\n }\n },\n \"style\": \"numbered\",\n \"scope\": \"Sustainability science: environment, society, economics\"\n },\n \"nature_communications\": {\n \"name\": \"Nature Communications\",\n \"url\": \"https://www.nature.com/ncomms/for-authors\",\n \"impact_factor\": 15.7,\n \"acceptance_rate\": \"~20%\",\n \"open_access\": true,\n \"article_types\": {\n \"article\": {\n \"body_words\": 5000,\n \"body_includes_methods\": true,\n \"abstract_words\": 200,\n \"figures\": 10,\n \"references\": 60,\n \"methods_location\": \"within_main_text\"\n }\n },\n \"style\": \"numbered\",\n \"scope\": \"All natural sciences — most flexible Nature journal\"\n },\n \"nature_methods\": {\n \"name\": \"Nature Methods\",\n \"url\": \"https://www.nature.com/nmeth/for-authors\",\n \"impact_factor\": 32.1,\n \"acceptance_rate\": \"~8-10%\",\n \"article_types\": {\n \"article\": {\n \"body_words\": 3000,\n \"abstract_words\": 150,\n \"figures\": 6,\n \"references\": 50,\n \"methods_words\": 3000,\n \"methods_location\": \"after_references\",\n \"notes\": \"Methods description lives in Online Methods after refs\"\n }\n },\n \"style\": \"numbered\",\n \"scope\": \"Novel experimental/computational methods for life sciences\"\n },\n \"nature_computational_science\": {\n \"name\": \"Nature Computational Science\",\n \"url\": \"https://www.nature.com/natcomputsci/for-authors\",\n \"impact_factor\": 12.0,\n \"acceptance_rate\": \"~12%\",\n \"article_types\": {\n \"article\": {\n \"body_words\": 5000,\n \"abstract_words\": 200,\n \"figures\": 8,\n \"references\": 60\n }\n },\n \"style\": \"numbered\",\n \"scope\": \"Algorithms, software, computational approaches across all sciences\"\n },\n\n \"nature_chemical_engineering\": {\n \"name\": \"Nature Chemical Engineering\",\n \"url\": \"https://www.nature.com/natchemeng/for-authors\",\n \"impact_factor\": 13.0,\n \"acceptance_rate\": \"~8%\",\n \"article_types\": {\n \"article\": {\n \"body_words\": 3500,\n \"body_excludes\": \"abstract, Methods, references, figure legends\",\n \"abstract_words\": 150,\n \"abstract_unreferenced\": true,\n \"figures\": 6,\n \"extended_data\": 10,\n \"references\": 50,\n \"methods_location\": \"online_methods_after_discussion\",\n \"methods_words\": null,\n \"structure\": [\"Introduction (no heading)\", \"Results\", \"Discussion or Conclusions\", \"Online Methods\"],\n \"notes\": \"Methods = Online Methods, placed after Discussion. Results and Methods have topical subheadings; Discussion has none.\"\n },\n \"analysis\": {\n \"body_words\": 3500,\n \"abstract_words\": 150,\n \"figures\": 6,\n \"extended_data\": 10,\n \"references\": 50\n },\n \"correspondence\": {\n \"words_range\": \"300-800\",\n \"figures\": 1,\n \"references\": 10\n }\n },\n \"style\": \"numbered\",\n \"scope\": \"Chemical engineering: process design, reaction engineering, separations, transport phenomena, scale-up\"\n },\n\n \"nature_machine_intelligence\": {\n \"name\": \"Nature Machine Intelligence\",\n \"url\": \"https://www.nature.com/natmachintell/for-authors\",\n \"impact_factor\": 23.9,\n \"acceptance_rate\": \"~8%\",\n \"article_types\": {\n \"article\": {\n \"body_words\": 3500,\n \"body_excludes\": \"abstract, Methods, references, figure legends\",\n \"abstract_words\": 150,\n \"abstract_unreferenced\": true,\n \"figures\": 6,\n \"references\": 50,\n \"methods_location\": \"after_discussion\",\n \"structure\": [\"Introduction (no heading)\", \"Results\", \"Discussion\", \"Methods\"],\n \"notes\": \"Results and Methods have topical subheadings; Discussion does not. Also accepts Analysis format (100-150 word abstract).\"\n },\n \"analysis\": {\n \"body_words\": 3500,\n \"abstract_words_range\": \"100-150\",\n \"figures\": 6,\n \"references\": 50\n },\n \"correspondence\": {\n \"words_range\": \"500-1000\",\n \"figures\": 1,\n \"references\": 10\n },\n \"review\": {\n \"body_words_range\": \"3000-4000\",\n \"references\": 100,\n \"notes\": \"≤10% of refs should have short annotations explaining key contributions\"\n }\n },\n \"style\": \"numbered\",\n \"scope\": \"Machine learning, deep learning, robotics, AI, computer vision, NLP, reinforcement learning\"\n },\n\n \"nature_synthesis\": {\n \"name\": \"Nature Synthesis\",\n \"url\": \"https://www.nature.com/natsynth/for-authors\",\n \"impact_factor\": 20.0,\n \"acceptance_rate\": \"~8%\",\n \"article_types\": {\n \"article\": {\n \"body_words\": 3000,\n \"body_excludes\": \"abstract, methods, references, figure/table captions\",\n \"abstract_words\": 150,\n \"abstract_unreferenced\": true,\n \"figures\": 6,\n \"references\": 50,\n \"methods_location\": \"after_discussion\",\n \"methods_words\": 3000,\n \"structure\": [\"Introduction (no heading)\", \"Results\", \"Discussion\", \"Conclusions (optional)\", \"Methods\"],\n \"special_rules\": [\n \"NO schemes — use figures instead (unique to Nature Synthesis)\",\n \"Methods section CANNOT contain figures or tables — use Extended Data or SI\",\n \"Results and Discussion can be combined as 'Results and Discussion' with subheadings\",\n \"Discussion should be succinct with NO subheadings\",\n \"Non-graphical equations permitted in Methods\"\n ],\n \"notes\": \"Only one article type (Article). Spans short communications to full studies.\"\n },\n \"correspondence\": {\n \"words_range\": \"300-800\",\n \"figures\": 1,\n \"references\": 10\n }\n },\n \"style\": \"numbered\",\n \"scope\": \"Synthetic chemistry and materials synthesis: total synthesis, synthetic methodology, automated synthesis, retrosynthetic analysis\"\n }\n }\n}","readmeExcerpt":"Skill: Nature Paper Hub Owner: yang1bai Summary: Full-pipeline Nature-series journal writing assistant. Covers journal selection, literature review, manuscript drafting, figure generation, citation verifica... Tags: latest:1.0.0 Version history: v1.0.0 | 2026-05-17T20:30:47.583Z | user Initial release: full-pipeline Nature-series journal writing agent for 12 Nature journals. Includes figure generation, CrossRef citat","codeSnippets":[],"executableExamples":[{"language":"text","snippet":"📋 请选择目标期刊 / Select target journal:\n\n1. Nature (IF 63.7) — 顶级综合科学\n2. Nature Materials (IF 37.2) — 材料科学\n3. Nature Chemistry (IF 19.2) — 化学\n4. Nature Energy (IF 60.9) — 能源\n5. Nature Catalysis (IF 37.8) — 催化\n6. Nature Sustainability (IF 25.1) — 可持续发展\n7. Nature Communications (IF 15.7) — 全科学，开放获取，最灵活\n8. Nature Methods (IF 32.1) — 方法学\n9. Nature Computational Science (IF 12.0) — 计算科学\n10. Nature Chemical Engineering (IF 13.0) — 化学工程\n11. Nature Machine Intelligence (IF 23.9) — 机器学习/AI/机器人\n12. Nature Synthesis (IF 20.0) — 合成化学与材料合成\n13. 其他 / Other — 请告诉我期刊名"},{"language":"text","snippet":"Search query: site:nature.com/[journal-shortname] \"<topic keyword>\" open access"},{"language":"text","snippet":"Title: [concise, ≤15 words, no abbreviations]\nAbstract: [150 words — context → problem → approach → key result → significance]\n\nIntroduction\n ¶1 Broad context and importance\n ¶2 Specific background — what is known\n ¶3 The gap or unsolved problem\n ¶4 Your approach and key findings (end: \"Here we report...\")\n\nResults\n Section 1: [Synthesis/Preparation/Model — first evidence]\n Section 2: [Characterization/Validation — structural/spectroscopic proof]\n Section 3: [Mechanism/Explanation — why it works]\n Section 4: [Performance/Application — how good it is]\n Section 5: [Generalizability/Comparison — how broad/better]\n\nDiscussion\n ¶1 Summary of key findings\n ¶2 Comparison with literature\n ¶3 Mechanistic interpretation\n ¶4 Limitations + future work\n ¶5 Broader impact (1 sentence)\n\nMethods [~3000 words, after refs for most journals]\n - Materials/Reagents\n - Synthesis/Preparation\n - Characterization techniques\n - Computational details (if applicable)\n - Statistical analysis\n\nReferences [numbered, order of appearance]\nFigure Legends [detailed, self-contained]\nExtended Data [optional, up to 10 items]"},{"language":"text","snippet":"Current word count: [X] / [journal limit]\nStatus: [✅ within limit | ⚠️ X words over — suggest cuts below]"},{"language":"text","snippet":"📋 Self-critique — [Section Name]:\n✅ Strengths:\n - [what works well]\n⚠️ Weaknesses / likely reviewer concerns:\n - [specific issue 1: e.g., \"Claim in ¶2 lacks quantitative support\"]\n - [specific issue 2: e.g., \"Mechanism not distinguished from alternative explanations\"]\n - [specific issue 3: e.g., \"'Significantly' used without p-value\"]\n💡 Suggested improvements:\n - [concrete fix for each weakness]"},{"language":"text","snippet":"Figure X: [What story does this figure tell?]\n Panel (a): [Data type] — [Message]\n Panel (b): [Data type] — [Message]\n Panel (c): [Data type] — [Message]\n\nDesign rules:\n- Each figure tells ONE clear story\n- Panel a = overview/schematic; subsequent panels = evidence\n- Resolution: 300 DPI min (600 DPI for line art)\n- Font: Arial or Helvetica, ≥7pt in final printed size\n- Color: accessible palette (avoid red-green for colorblind readers)\n- Scale bars: always include for microscopy images\n- Statistical indicators: *, **, *** for significance; exact p-values preferred"}],"parameters":null,"dependencies":[],"permissions":[],"extractedFiles":[{"path":"SKILL.md","content":"---\nname: nature-paper-hub\ndescription: Full-pipeline Nature-series journal writing assistant. Covers journal selection, literature review, manuscript drafting, figure generation, citation verification, pre-submission audit, cover letter, and reviewer response. Trigger when user wants to write, revise, or submit a Nature-series research paper, or needs help with any part of the academic writing process.\nversion: 1.0.0\nauthor: Yang1Bai\ntags:\n - academic-writing\n - nature-journal\n - scientific-writing\n - research-paper\n - latex\n - claude-code\n - codex\n - openclaw\n---\n\n# Nature Paper Hub\n\n## Description\nFull-pipeline Nature-series journal writing assistant. Trigger when the user wants to:\n- Write, draft, or outline a Nature-series research paper\n- Select a Nature journal for submission\n- Revise any section of a manuscript\n- Plan or improve figures\n- Check citations or generate reference lists\n- Prepare a submission checklist or rebuttal letter\n- Export manuscript as LaTeX (Overleaf) or Word\n\nMulti-language: interact in Chinese or English; all manuscript output is in English.\n\n## Skill Location\n~/.openclaw/workspace/skills/nature-paper-hub/\n\n## Supporting Files\n- templates/journal-specs.json — journal-specific word limits, figures, references\n- templates/nature-latex.tex — master LaTeX template (Overleaf-ready)\n- scripts/export_docx.py — Word export via python-docx\n\n---\n\n## STAGE 0 — Journal Selection\n\n**Always run this stage first unless the user has already specified a journal.**\n\nPresent this menu and ask the user to choose:\n\n```\n📋 请选择目标期刊 / Select target journal:\n\n1. Nature (IF 63.7) — 顶级综合科学\n2. Nature Materials (IF 37.2) — 材料科学\n3. Nature Chemistry (IF 19.2) — 化学\n4. Nature Energy (IF 60.9) — 能源\n5. Nature Catalysis (IF 37.8) — 催化\n6. Nature Sustainability (IF 25.1) — 可持续发展\n7. Nature Communications (IF 15.7) — 全科学，开放获取，最灵活\n8. Nature Methods (IF 32.1) — 方法学\n9. Nature Computational Science (IF 12.0) — 计算科学\n10. Nature Chemical Engineering (IF 13.0) — 化学工程\n11. Nature Machine Intelligence (IF 23.9) — 机器学习/AI/机器人\n12. Nature Synthesis (IF 20.0) — 合成化学与材料合成\n13. 其他 / Other — 请告诉我期刊名\n```\n\nAfter selection, load the corresponding entry from `templates/journal-specs.json` and display:\n- Word limits (body, abstract, Methods)\n- Figure/table limit\n- Reference limit\n- Methods location (within text vs. after references)\n- Acceptance rate and IF\n\nThen ask: **\"您的论文类型是 Article 还是 Letter？\"**\n\n### ⚠️ Journal-specific special rules to load:\n\n**Nature Synthesis (选12):**\n- NO schemes — all graphics must be figures (no reaction scheme format)\n- Methods section CANNOT contain figures or tables — use Extended Data or SI\n- Results and Discussion may be combined into one section with subheadings\n- Discussion must be succinct and cannot have subheadings\n- Only one article type: Article (covers both short comms and full papers)\n\n**Nature Machine Intelligence (选11):**\n- Also accept"},{"path":"skills/nature-citation/SKILL.md","content":"---\nname: nature-citation\ndescription: Strict Nature/CNS-family citation retrieval, verification, and export. Given a topic, claim, or list of papers, finds real citations, verifies DOIs, checks retraction status, and exports in BibTeX, RIS, ENW, or Zotero RDF format. Trigger when user needs citations, wants to verify references, or needs to export a reference list.\n---\n\n# nature-citation\n\n## Purpose\nFind, verify, and export citations for Nature-series manuscripts.\nStrict accuracy: every reference must be real, accessible, and support the cited claim.\n\n---\n\n## Trigger Conditions\n- \"找参考文献\" / \"引用\" / \"citation\" / \"reference\"\n- \"验证引用\" / \"verify DOI\" / \"check references\"\n- \"导出文献\" / \"export BibTeX\" / \"Zotero\" / \"RIS\" / \"ENW\"\n- \"这个说法有文献支持吗\" / \"find supporting papers for...\"\n- User pastes a claim and asks for citations\n\n---\n\n## Workflow\n\n### Mode 1: Find citations for a claim\n1. User provides: a scientific claim or topic\n2. **First: search personal LitReview library** — `web_fetch(\"https://ybliterature.com/api/search?q=<URL-encoded-query>\")`\n - If results found: use these as primary citations (already in user's library)\n3. **CrossRef full-text search** — `web_fetch(\"https://api.crossref.org/works?query=<query>&filter=has-full-text:true&rows=5&sort=relevance\")`\n - Extract: DOI, title, authors, year, journal, is-referenced-by-count\n4. **Broader web search** — `web_search(\"<claim> site:nature.com OR site:science.org OR site:cell.com\")`\n5. **arXiv** — `web_search(\"arxiv <topic> <year>\")`\n6. For each candidate paper:\n - **Verify via CrossRef**: `web_fetch(\"https://api.crossref.org/works/<DOI>\")`\n → confirms: real DOI, correct metadata, citation count\n - **Check retraction via RetractionWatch**: `web_search(\"site:retractionwatch.com \\\"<title keywords>\\\"\")`\n - **Also check**: `web_search(\"<title> retraction OR retracted OR correction\")`\n - Confirm it actually supports the claim (not just related)\n7. Return ranked list: most relevant first, with support assessment and citation count\n\n### Mode 2: Verify existing reference list\nFor each reference the user provides:\n1. **CrossRef DOI lookup** (most reliable):\n - If DOI present: `web_fetch(\"https://api.crossref.org/works/<DOI>\")`\n - Compare returned metadata with user's reference — flag any discrepancy\n2. If no DOI: `web_search('\"<author>\" \"<year>\" \"<journal>\" \"<title keywords>\"')`\n3. **Retraction check**: `web_search(\"site:retractionwatch.com \\\"<first author> <year>\\\"\")`\n4. Assess: does this ref support the claim it's cited for?\n5. Flag: ✅ verified via CrossRef | ⚠️ found but unverified | ❌ wrong metadata or retracted\n\n### Mode 3: Export reference list\nConvert verified references to the requested format (see below).\n\n---\n\n## Nature Reference Style (numbered, Vancouver)\n\n### Format:\n```\n[number]. LastName, A. B., LastName, C. D. & LastName, E. F. Title of article. \nJournal Abbrev. Vol, first–last page (Year).\n```\n\n### Rules:\n- Up to 6 authors, then \"et al.\"\n- Journal names abbreviated (e.g., Nat."},{"path":"skills/nature-figure/SKILL.md","content":"---\nname: nature-figure\ndescription: Generate publication-quality figures for Nature-series journals using Python (matplotlib) or R (ggplot2). Trigger when user wants to create, polish, or redesign scientific figures for high-impact journals. Handles multi-panel layouts, Nature color palettes, correct typography, and exports SVG/PDF/PNG.\n---\n\n# nature-figure\n\n## Purpose\nGenerate multi-panel scientific figures that meet Nature portfolio visual standards:\ncorrect typography, semantic colour palette, accessible design, and editable SVG output.\n\n---\n\n## Trigger Conditions\nActivate when user mentions:\n- \"画图\" / \"figure\" / \"plot\" / \"科研绘图\"\n- \"Nature figure\" / \"publication figure\" / \"publication plot\"\n- \"matplotlib\" / \"ggplot\" / \"seaborn\"\n- \"配色\" / \"color palette\" / \"color scheme\"\n- Wants to improve or reformat an existing figure\n\n---\n\n## Nature Figure Standards\n\n### Typography\n- Font family: **Arial** or **Helvetica** (sans-serif, never Times New Roman in figures)\n- Minimum font size in final print: **7 pt** (axis labels, tick labels)\n- Panel labels (a, b, c...): **8 pt bold**, lowercase\n- Figure title (if any): not embedded in figure — goes in legend\n- All text must be editable (not rasterized)\n\n### Size & Resolution\n| Format | Width | Resolution |\n|--------|-------|------------|\n| Single column | 89 mm (3.5 in) | 300 DPI min |\n| 1.5 column | 120 mm (4.7 in) | 300 DPI min |\n| Double column | 183 mm (7.2 in) | 300 DPI min |\n| Line art | any | **600 DPI** |\n| Final submission | PDF or TIFF | vector preferred |\n\n### Colour Palette (Nature-approved, colorblind-safe)\n```python\nNATURE_COLORS = {\n \"blue\": \"#4878CF\",\n \"red\": \"#D65F5F\", \n \"green\": \"#6ACC65\",\n \"orange\": \"#EE854A\",\n \"purple\": \"#956CB4\",\n \"teal\": \"#82C6E2\",\n \"brown\": \"#D5BB67\",\n \"gray\": \"#8C8C8C\",\n # Colorblind-safe primary pair:\n \"cb_blue\": \"#0072B2\",\n \"cb_orange\":\"#E69F00\",\n}\n```\n- Never use pure red + green together (colorblind conflict)\n- Use filled symbols + different shapes for accessibility, not colour alone\n- Grayscale must remain distinguishable\n\n### Panel Architecture\n- Each panel makes **one clear point**\n- Panel (a): overview / schematic / representative image\n- Panels (b–d): quantitative evidence\n- Final panel: comparison or generalizability\n- Panels are labelled **a, b, c** (lowercase bold, top-left corner)\n- White background; minimal gridlines (light gray, 0.5pt)\n- No chartjunk: remove top and right spines\n\n### Statistical Annotations\n- Error bars: always define in legend (mean ± s.d. or ± s.e.m.)\n- Significance: *, **, ***, **** for p < 0.05, 0.01, 0.001, 0.0001; prefer exact p-values\n- n must be stated (e.g., n = 5 independent experiments)\n- Box plots: show median, IQR, whiskers to 1.5×IQR, individual points overlaid\n\n---\n\n## Workflow\n\n### Step 0: Auto-figure from data file (fastest path)\nIf user provides a CSV, Excel, or JSON data file:\n```bash\npython3 ~/.openclaw/workspace/skills/nature-paper-hub/scripts/auto_figure.py \\\n --inpu"},{"path":"skills/nature-paper2ppt/SKILL.md","content":"---\nname: nature-paper2ppt\ndescription: Convert a scientific paper into a presentation deck (PPTX). Generates structured Chinese or bilingual slides for journal clubs, group meetings, or conference talks. Trigger when user wants to make slides from a paper or present research findings.\n---\n\n# nature-paper2ppt\n\n## Purpose\nTransform a Nature-series paper into a clean, publication-aware PPTX presentation,\noptimised for journal-club or group-meeting delivery. Output in Chinese (default)\nor bilingual (Chinese + English).\n\n---\n\n## Trigger Conditions\n- \"做PPT\" / \"幻灯片\" / \"slides\" / \"presentation\"\n- \"journal club\" / \"组会\" / \"汇报\"\n- \"paper to PPT\" / \"paper2ppt\" / \"论文转PPT\"\n- User shares a paper and wants to present it\n\n---\n\n## Slide Structure\n\n### 1. Title Slide\n- Paper title (full, in English)\n- Authors + institution\n- Journal + Year + DOI + IF\n- Presenter name + date\n- Background: clean white or dark navy\n\n### 2. Background & Motivation (1–2 slides)\n- Why does this problem matter? (3–4 bullet points)\n- Current limitations or gaps in the field\n- Key concepts the audience needs (define jargon)\n\n### 3. Research Question & Approach (1 slide)\n- One sentence: what did they set out to do?\n- Main hypothesis or objective\n- Brief method overview (schematic if available)\n\n### 4. Key Results (3–5 slides, one per main finding)\nFor each Results subsection:\n- Slide title = the key message (e.g., \"Catalyst achieves 95% efficiency at low overpotential\")\n- Main figure (reproduced or described)\n- 2–3 bullet points explaining what the data shows\n- One sentence: so what? (interpretation)\n\n### 5. Mechanism / Why It Works (1 slide)\n- Mechanistic explanation\n- Key experiment that proves the mechanism\n- Theoretical support (DFT, MD, etc.) if present\n\n### 6. Comparison with Prior Work (1 slide)\n- Table or bar chart: this work vs. literature\n- Highlight where this paper advances the state of the art\n\n### 7. Discussion & Limitations (1 slide)\n- What does this mean for the field?\n- Honest limitations (what they didn't prove)\n- Open questions remaining\n\n### 8. Conclusion (1 slide)\n- 3–5 bullet points: key takeaways\n- Broader significance in one sentence\n\n### 9. Critical Thinking (1 slide) — optional\n- Questions for discussion:\n - Is the claim fully supported by the data?\n - What experiment is missing?\n - How would you follow up?\n\n### 10. References (1 slide)\n- Key references cited in the paper (top 5–8)\n\n---\n\n## Language Options\n\nAsk user:\n- **中文** (default): all slide content in Chinese, figure captions translated\n- **双语** (bilingual): English title + Chinese body text\n- **English**: full English (for international presentations)\n\n---\n\n## Design Guidelines\n\n### Typography:\n- Title font: 32–36pt, bold\n- Body font: 20–24pt\n- Minimum readable: 18pt\n- Chinese font: 微软雅黑 (Microsoft YaHei) or 思源黑体 (Source Han Sans)\n- English font: Arial or Calibri\n\n### Layout:\n- 16:9 widescreen (1920×1080 recommended)\n- Clean white background with accent color (use journal color if applicable)\n- Nature blu"},{"path":"skills/nature-reader/SKILL.md","content":"---\nname: nature-reader\ndescription: Full-paper bilingual reader for Nature-series papers. Converts a PDF or URL into a structured, annotated Markdown document with Chinese translation, figure grounding, source anchors, and section summaries. Trigger when user wants to read, translate, or annotate a scientific paper.\n---\n\n# nature-reader\n\n## Purpose\nTransform a scientific paper (PDF path, DOI, or URL) into a richly annotated bilingual\nMarkdown document: original English with inline Chinese translation, figure references\ngrounded to actual captions, and section-level summaries.\n\n---\n\n## Trigger Conditions\nActivate when user mentions:\n- \"读论文\" / \"翻译论文\" / \"精读\" / \"全文翻译\"\n- \"nature reader\" / \"paper reader\" / \"bilingual\"\n- \"原文对照\" / \"图文对应\" / \"paper md\"\n- Shares a DOI, arXiv ID, PDF path, or paper URL\n\n---\n\n## Input Handling\n\n### Accepted inputs:\n1. **PDF file path** — e.g., `~/Downloads/paper.pdf`\n → Use `read` tool to extract text content\n2. **DOI** — e.g., `10.1038/s41565-024-01234-5`\n → Fetch via `https://doi.org/<DOI>` or `https://unpaywall.org/api/v2/<DOI>?email=open`\n3. **arXiv ID** — e.g., `2401.12345`\n → Fetch via `https://arxiv.org/abs/2401.12345`\n4. **URL** — fetch directly with web_fetch\n\n### Open-access lookup:\nIf the paper is paywalled, try:\n- `https://unpaywall.org/api/v2/<DOI>?email=open` → check `best_oa_location.url_for_pdf`\n- `https://sci-hub.se/<DOI>` (mention only; do not auto-fetch)\n- arXiv preprint version via web_search: `arxiv \"<title>\" \"<first author>\"`\n\n---\n\n## Output Format\n\nGenerate a Markdown document with this structure:\n\n```markdown\n# [Paper Title]\n\n> **Journal:** Nature [Sub-journal] | **Year:** XXXX | **DOI:** [link]\n> **Authors:** Author One, Author Two, ...\n> **Open access:** [Yes/No] | **PDF:** [link if available]\n\n---\n\n## 📋 Quick Summary | 速览\n\n| | |\n|---|---|\n| **核心问题** | [一句话：这篇论文解决了什么问题] |\n| **核心方法** | [方法/技术核心] |\n| **关键结果** | [最重要的1-2个数字/发现] |\n| **意义** | [为什么重要] |\n| **适合引用于** | [哪类论文的哪个部分可以引用这篇] |\n\n---\n\n## Abstract | 摘要\n\n**[Original English abstract]**\n\n> 🇨🇳 **中文翻译：**\n> [Faithful Chinese translation of the abstract]\n\n---\n\n## Introduction | 引言\n\n### [Subsection or paragraph grouping]\n\n[Original English text — preserve key sentences verbatim]\n\n> 🇨🇳 [Chinese translation of this paragraph]\n\n**💡 Key point:** [One-sentence summary of this paragraph's main argument]\n**📚 Key citations:** [[Author, Year]] — [why cited here]\n\n[Continue paragraph by paragraph...]\n\n---\n\n## Results | 结果\n\n### [Result subsection title]\n\n[Original English — key sentences]\n\n> 🇨🇳 [Chinese translation]\n\n**📊 Figure X reference:** [Describe what Figure X shows and what conclusion it supports]\n**🔢 Key numbers:** [Extract quantitative claims: \"efficiency increased from X% to Y%\"]\n\n---\n\n## Discussion | 讨论\n\n[Original + Chinese + key point per paragraph]\n\n---\n\n## Methods | 方法\n\n> ⚙️ [Methods summary in Chinese — full translation optional, summarize by subsection]\n\n### [Methods subsection]\n[Key parameters, instruments, conditions — bilingual]\n\n"}],"languages":[],"docsSourceLabel":"CLAWHUB","editorialOverview":"Full-pipeline Nature-series journal writing assistant. 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