--- id: gh-nature-citation name: "nature-citation" url: https://skills.yangsir.net/skill/gh-nature-citation author: yuan1z0825 domain: science tags: ["citation", "academic-writing", "literature-review", "research", "scientific-publishing"] install_count: 15400 rating: 4.50 (120 reviews) github: https://github.com/yuan1z0825/nature-skills/tree/main/plugins/nature-skills/skills/nature-citation --- # nature-citation > 此技能可将手稿文本转化为可辩护的引用导出文件，包括分段文本、引用候选和详细的证据说明。它能根据用户指定的期刊范围（如Nature系列、CNS）进行精准搜索，并处理长篇文章的批量引用需求，显著提高学术论文的引用生成效率和准确性。 **Stats**: 15,400 installs · 4.5/5 (120 reviews) ## Before / After 对比 ### 引用生成效率 **Before**: 研究人员手动查找、验证和格式化学术引用，耗时费力，且易出错。对于一篇论文的引用部分，可能需要数小时甚至数天来确保其准确性和规范性，尤其是在需要严格遵循特定期刊要求时。 **After**: 此技能自动将手稿文本分段，识别引用候选，并生成符合特定期刊要求的引用导出文件。它大大减少了手动工作量，确保了引用的准确性和可辩护性，将耗时数小时的工作缩短至几分钟。 | Metric | Before | After | Change | |---|---|---|---| | 引用生成时间 | 60分钟 | 10分钟 | -83% | ## Readme # Nature Citation Use this skill to turn manuscript text into a defensible citation export: - segmented text with citation candidates for each segment - a reference-manager import file in `.enw`, `.ris`, or Zotero `.rdf` - conservative evidence notes explaining whether each candidate truly supports the segment ## Chinese-user operating mode When the user writes in Chinese, asks for "Nature系列", "CNS及其子刊", "支撑文献", "补引用", "自动给出引用", "分段引用", "导出EndNote", "RIS", "Zotero", "RDF", or provides Chinese manuscript text: - Accept the text in Chinese, but search using English concept queries unless the topic is explicitly China-specific or Chinese-language scholarship. - Return segment notes and evidence notes in Chinese by default. - Preserve the exact source segment and translate it into one or more English search claims. - Flag overclaiming clearly in Chinese: `强支撑`, `部分支撑`, `背景支撑`, `不建议引用为该句支撑`. - Do not present a paper as supporting the claim merely because its title is related. ## Default scope Interpret journal scope from the user's wording, but keep the filter strict: - `Nature系列`: search Nature Portfolio first. Include `Nature`, `Nature [field]`, `Nature Communications`, `Communications [field]`, `Scientific Reports`, and `npj` journals. - `CNS`: search `Cell`, `Nature`, and `Science` plus their major sister journals. - `CNS及其子刊` or `CNS/sister journals`: search only accepted flagship and subjournal titles in Nature Portfolio, the AAAS Science family, and Cell Press. - `只要Nature/Science/Cell正刊`: restrict to the flagship journals `Nature`, `Science`, and `Cell`. Do not treat merely related journals as in-scope. A title is valid only if it is in the accepted publisher-family whitelist or clearly matches the official naming pattern for that family. If the user needs an exhaustive or submission-critical boundary, verify current official journal pages before finalizing because journal portfolios change. ## Source hierarchy Use sources in this order: 1. Structured bibliographic metadata: Crossref, PubMed/NCBI E-utilities, DOI metadata. 2. Publisher pages: `nature.com`, `science.org`, `cell.com`, and official journal pages. 3. Full text or abstract pages, if accessible. 4. Secondary databases such as Google Scholar, Semantic Scholar, Web of Science, or Scopus only as discovery aids, not as the sole support basis. Prefer structured APIs for metadata and publisher pages for claim verification. If metadata and publisher page disagree, preserve the DOI and journal-page facts and flag the discrepancy. ## Long-article strategy When the input text is longer than roughly 3000 characters (about 10+ segments), the skill must switch to a batched workflow to avoid timeout, context overflow, or incomplete results: 1. **Auto-detect length.** Count segments after segmentation. If there are more than 10 segments, switch to batch mode automatically. 2. **Split by section.** Prefer splitting at paragraph double-line breaks or explicit section headings (`Introduction`, `Results`, etc.) so each batch is a coherent unit, not arbitrary sentence groups. 3. **Process each batch independently.** Run the Python script once per batch using `--batch-size` or `--max-segments`, OR split the text externally and call the script once per chunk. Each call writes its own intermediate export file. 4. **Merge results at the end.** After all batches finish, combine the intermediate files into one final export. Deduplicate by DOI. 5. **Minimize inline analysis.** For long articles, do NOT write detailed support-grade notes for every single segment inline. Instead: - Write a compact summary table (segment ID → best candidate → support grade). - Point the user to the HTML visualization for full browsing. - Only elaborate on segments where no candidate was found or evidence is contradictory. ### Quick guide for Claude | Segments | Strategy | |---|---| | 1–10 | Run once, full inline analysis is fine. | | 11–25 | Use `--batch-size 10`. Write a compact summary table. Point to HTML. | | 26+ | Split by section. Run script per section with `--batch-size 10`. Compact summary + HTML only. | ## Workflow ### 1. Segment the text For each input text: - Split long text into citable segments. Prefer paragraph boundaries first, then sentence boundaries. - Keep each segment focused on one citable idea when possible. - Preserve original order and stable segment IDs such as `S001`, `S002`, `S003`. - Skip obvious non-citable connective sentences unless the user asks to cite every sentence. - For very long text, process in batches but keep a single final mapping table. - If the input has more than about 10 segments, prefer batch mode. Default segmentation rules: - Use blank lines as paragraph boundaries. - If a paragraph is longer than about 700 characters or contains multiple claims, split into sentences. - Merge very short fragments into neighboring text unless they contain a distinct claim. - Keep section headings as labels, not as citable segments. ### 2. Parse each segment For each citable segment: - Extract the core claim in one sentence. - Identify claim type: `mechanism`, `association`, `method`, `clinical`, `epidemiology`, `background`, `definition`, or `review-context`. - Identify entities, intervention/exposure, outcome, population/model, directionality, and boundary. - Convert the claim into 2-4 English search queries: - one precise query with all key terms - one synonym query - one broader background query - one methods or model query if relevant If the claim is too broad, split it into citable subclaims rather than searching the whole sentence. ### 3. Search candidate papers Start with `scripts/nature_citation.py` when internet access is available: ```bash python scripts/nature_citation.py \ --text "PASTE MANUSCRIPT TEXT HERE" \ --scope cns \ --outdir /tmp/nature-citation \ --format enw \ --with-artifacts ``` Useful options: - `--text-file manuscript.txt`: read long text from a file. - `--claim "CLAIM TEXT"` or `--claim-file claims.txt`: treat each claim as a segment. - `--doi 10.xxxx/xxxxx` or `--doi-file dois.txt`: export known DOI records after screening. - `--scope nature`: Nature Portfolio-style journals only. - `--scope flagship`: Nature, Science, and Cell only. - `--from-year 2018 --to-year 2026`: constrain publication dates. - `--rows 40`: raise for broad searches; keep top candidates manageable. - `--per-segment 3`: number of citation candidates to keep per segment. - `--batch-size 2`: process long text in smaller batches. - `--max-segments 12`: cap the number of segments processed in one run. - `--max-retries 2`: retry transient Crossref failures before skipping a query. - `--format enw|ris|zotero-rdf`: export format. If omitted and `--output-file` is set, infer from suffix. - `--mailto you@example.com`: use Crossref's polite pool. - `--batch-size 10`: process segments in batches of N. Each batch writes an incremental export file. - `--max-segments 20`: only process the first N segments. Useful for testing or section-by-section workflows. - `--sleep 0.3`: seconds between Crossref requests. Default is 0.3; raise to 1.0 if rate-limited. Long-article strategy: - 1-10 segments: run normally. - 11-25 segments: use batch mode and keep the HTML browser open for screening. - 26+ segments: split by section or subsection first, then run each part separately if needed. - For long texts, prefer the HTML browser for review and selection instead of relying only on inline notes. When the topic is biomedical or PubMed-indexed, also search PubMed with journal filters and compare results against Crossref. Use NCBI E-utilities rate limits and include `tool`/`email` parameters if running repeated searches. ### 4. Evaluate whether each paper supports the segment Use a conservative support scale: - `strong support`: the paper directly tests the same relationship/mechanism/method and the result supports the segment. - `partial support`: the paper supports part of the segment, a related model, or a narrower condition. - `background support`: the paper supports field context, not the specific claim. - `contradictory/limiting`: the paper conflicts with or narrows the claim. - `metadata-only candidate`: title/metadata suggest relevance, but abstract/full text has not been checked. Never cite a `metadata-only candidate` as support without checking the abstract or publisher page. If a paper is a review, label it as review/context and avoid using it as primary evidence for an experimental claim when primary articles are available. ### 5. Export reference-manager file Default behavior: - write one reference-manager file - support publication time filters with `--from-year` and `--to-year` - for long or ambiguous texts, use `--with-artifacts` so the HTML browser is available Default file: - `references.enw`: EndNote tagged export Optional: - `references.ris`: if the user requests RIS instead of ENW - `references.rdf`: if the user requests Zotero RDF - review artifacts only when explicitly requested If the user asks to choose the download format, treat `ENW`, `RIS`, and `Zotero RDF` as the supported options and return only one export file unless they explicitly ask for multiple formats. Do not invent missing fields. If DOI, pages, volume, or issue are missing, leave them absent rather than fabricating them. ### 6. Optional review artifacts Generate review artifacts (HTML/TSV/JSON/report) for long or ambiguous runs. They are the primary way the user browses, filters, and selects candidates: - Use `--with-artifacts` when the text is long, the query is broad, or the user needs manual curation. - Report the HTML visualization path prominently in your final answer when artifacts are enabled. - Generate TSV/JSON/report alongside the HTML so the user has multiple views. ### 7. Report results Unless the user asks for a different format, return: ```text 交互式引用浏览器 - [absolute path to citation_visualization.html] ← 在浏览器中打开此文件，可筛选/选择/下载引用 检索范围 - [Nature Portfolio / Science family / Cell Press / flagship only, plus date limits] 分段引用对应关系 S001: [source segment] - [Author, year, title, journal, DOI] - 支撑等级: [strong/partial/background/limiting/metadata-only] - 插入建议: [e.g. after sentence / after clause] 导出文件 - [absolute path to references.enw / references.ris / references.rdf] 风险和缺口 - [missing full-text check, contradictory evidence, no direct CNS literature, etc.] ``` Put the HTML browser path FIRST in the report, above everything else, so the user can immediately open and browse candidates. If no suitable CNS/Nature-series paper exists, say so plainly and suggest the best nearby options from non-CNS literature only if the user wants broader coverage. If the text is long, mention the batch strategy used, especially when you limited the run with `--batch-size` or `--max-segments`. ## Search quality rules - Prefer precision over volume. A useful answer is usually 3-8 candidates, not 50 loosely related papers. - Use exact phrase searches only for distinctive terms; otherwise use concept terms and synonyms. - Check journal identity. Many journals contain the word "nature" but are not Nature Portfolio journals. - Treat citation count as a tie-breaker, not evidence of support. - Capture retractions, corrections, and expressions of concern when visible in Crossref or publisher metadata. - Date-sensitive topics require current searching and explicit search date. - For medical, clinical, or safety claims, search current literature and state that citations do not replace clinical guidance or systematic review. ## Related files | File | Open when | |---|---| | [references/search-strategy.md](references/search-strategy.md) | You need help translating a manuscript claim into search queries and support grades | | [references/journal-scope.md](references/journal-scope.md) | You need the default Nature/CNS journal-family boundary and official source notes | | [references/ris-endnote.md](references/ris-endnote.md) | You need RIS, EndNote, or Zotero RDF export guidance | | [scripts/nature_citation.py](scripts/nature_citation.py) | You need to segment text, search Crossref, export ENW/RIS/RDF, and generate HTML | ## Source notes This skill is based on public bibliographic APIs and official publisher/import documentation: Crossref REST API and filters, NCBI E-utilities, EndNote RIS import options, Nature Portfolio, AAAS Science journals, and Cell Press portfolio descriptions. Verify pages at use time when exact journal coverage or current import behavior matters. --- # `nature-citation` skill A citation-search skill for turning manuscript text or standalone claims into strict Nature / CNS-family reference exports with segment-level mapping and reference-manager-ready downloads. This skill is bilingual-aware. It accepts Chinese manuscript text and citation requests such as "分段引用", "Nature系列引用", "CNS及子刊", "补引用", "支撑文献", or "导出 Zotero", then searches with English scientific concepts while returning Chinese review notes by default. ## What it does - splits manuscript text into citable segments with stable IDs such as `S001`, `S002`, and `S003` - converts each segment into search queries for Crossref-led discovery - filters results to Nature Portfolio, the AAAS Science family, Cell Press, or flagship-only scope - maps each segment to candidate citations and suggested in-text insertion markers - exports one reference-manager file in `ENW`, `RIS`, or Zotero `RDF` - optionally builds JSON, TSV, Markdown, and HTML review artifacts for manual screening - supports long-article batch processing with partial checkpoints - retries transient Crossref failures instead of failing immediately - supports limiting one run to part of a long manuscript - supports DOI-only export when the user already knows which records should be included ## Source hierarchy - Crossref structured metadata and DOI records - PubMed / NCBI E-utilities for biomedical cross-checking when relevant - Official publisher pages from Nature Portfolio, AAAS Science, and Cell Press - Secondary scholarly indexes only as discovery aids, never as the sole support basis ## File structure ```text nature-citation/ ├── SKILL.md ├── README.md ├── references/ │ ├── journal-scope.md │ ├── ris-endnote.md │ └── search-strategy.md └── scripts/ └── nature_citation.py ``` ## When to use - adding citations to a paragraph, abstract, introduction, results, or discussion section - turning long text into segment-by-segment citation candidates - restricting references to `Nature系列`, `CNS`, `CNS及其子刊`, or `只看正刊` - exporting references for EndNote, Zotero, or other citation managers - screening whether a sentence has direct support, partial support, or only background support - producing an HTML review page where the user filters by year, selects citations, and downloads only the records they want ## Long-text behavior This skill now has a safer path for long inputs such as a full Introduction or multi-paragraph text. - for short inputs, it still works as a normal one-pass citation search - for longer inputs, it can process segments in batches - after each batch, it writes a partial export checkpoint so progress is not lost if a later batch fails - transient Crossref failures are retried automatically Useful rules of thumb: - 1-10 segments: normal run - 11-25 segments: prefer batch mode - 26+ segments: prefer section-by-section runs ## Design intent The skill should prioritize defensibility over volume. It is designed to help the user find likely in-scope papers, not to pretend that metadata alone proves a claim. Every exported record should preserve real metadata, avoid fabricated fields, and make the evidence-review burden explicit. For long manuscripts, the design goal is not only citation quality but also run stability: fewer lost runs, smaller batches, and a reviewable checkpoint trail. ## Reference map - `search-strategy.md`: claim decomposition, support grades, and common retrieval failure modes - `journal-scope.md`: Nature / Science / Cell family boundaries and flagship-only interpretation - `ris-endnote.md`: ENW, RIS, and Zotero RDF export guidance - `scripts/nature_citation.py`: local CLI for segmentation, Crossref retrieval, export, and HTML review generation ## Useful CLI options - `--batch-size 2`: process long text in smaller batches - `--max-segments 12`: cap the number of segments processed in one run - `--max-retries 2`: retry transient Crossref failures - `--sleep 0.3`: shorter default pause between requests - `--with-artifacts`: generate HTML, TSV, JSON, and Markdown review files ## Notes - Default output is a single reference-manager file; additional artifacts are opt-in. - `metadata-only candidate` means the abstract or full text still needs human review before citation. - The HTML review page can export selected references as `ENW`, `RIS`, or Zotero `RDF`. - For long texts, `--with-artifacts` is strongly recommended because the HTML browser is the easiest way to curate results. - Batch mode writes `.partial.enw` / `.partial.ris` / `.partial.rdf` checkpoints during the run before the final export is written. --- *Source: https://skills.yangsir.net/skill/gh-nature-citation* *Markdown mirror: https://skills.yangsir.net/api/skill/gh-nature-citation/markdown*