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:

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.
• `par