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Journal Style Learn

ASecurity

Learn a journal's structural and rhetorical patterns from supplied papers or an existing style analysis, and adapt the local nature skill family to that journal. Use for 把这本期刊适配到nature系列、新增期刊、期刊风格迁移、从论文生成期刊规则, bilingual chapter profiles and journal routing. Can also learn style only. Adapts reusable skills; polishing a manuscript belongs to nature-polishing.

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  • Added October 5, 2026
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npx -y skills add szzjayson25-hub/nature-academic-skills --skill journal-style-learn --agent claude-code

Installs into .claude/skills of the current project.

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SKILL.md
---
name: journal-style-learn
description: >-
  Learn a journal's structural and rhetorical patterns from supplied papers or
  an existing style analysis, and adapt the local nature skill family to that
  journal. Use for 把这本期刊适配到nature系列、新增期刊、期刊风格迁移、从论文生成期刊规则,
  bilingual chapter profiles and journal routing. Can also learn style only.
  Adapts reusable skills; polishing a manuscript belongs to nature-polishing.
---

# Journal Style Learn — Nature family adapter

This skill owns **learning and registering journal adaptations**, not manuscript
rewriting. Reuse the installed nature-writing, nature-polishing and nature-shared
architecture. Keep the current skill name so existing invocations still work.
Chinese reading copy: [SKILL-zh.md](SKILL-zh.md).

## Determine the requested outcome

- “适配到 nature 系列 / add this journal / 生成期刊适配技能” means learn or reuse
  grounded rules, generate bilingual resources, register the journal in the
  selected local nature family, validate and package the adaptation.
- “学习期刊风格 / 分析这些论文 / 只生成规则” means **learn-only**: produce
  `journal_style_rules.md`, leaving installed skills untouched. Explicit preview
  or package-only requests likewise do not authorize installation.
- “把我的论文改成这个期刊风格” means manuscript revision: use the existing
  nature-polishing route. Build a new adaptation only if the user requests it;
  do not silently edit skill infrastructure during a manuscript edit.

Infer journal identity, corpus location, discipline/article type and installation
root from the request and available files before asking. Match explicit journal
context; do not confuse a cited venue with the target. A folder of mixed venues
requires separating primary journal examples from supplemental material. Five to
eight relevant articles is a useful starting sample, not a mandatory quota or
proof of a journal-wide convention. Ask only for an essential unresolved identity
or inaccessible corpus, continuing independent work where possible.

## Learn from evidence

1. Inventory supplied PDFs, Markdown, Word or existing `journal_style_rules.md`.
   Read existing analysis first when provided; mark derivative synthesis clearly
   instead of claiming a new full-text analysis. Do not invent sample counts.
2. Read usable sources with available format tools. Prefer existing readable text;
   use PDF extraction/OCR when needed. MinerU is optional, not a prerequisite.
   Check section order, paragraph boundaries and damaged formulas/tables. Exclude
   unreadable passages from inference and report coverage limits.
3. Extract structural observations by paper and section with source locations:
   entry point, paragraph function chain, literature comparison, gap-to-design
   connection, method explanation, results-to-claim relation, limits and language.
   Describe rhetorical patterns; do not copy source prose, data or findings into
   rules or future manuscripts. A paper title/DOI can identify provenance.
4. Aggregate only comparable papers. Separate observed patterns, operational
   synthesis, general writing advice and verified official requirements. A missing
   feature in a few papers is not a forbidden practice. Keep exceptions and
   evidence strength visible; paragraph/word/contribution counts are not quotas.
5. Where helpful, load one relevant base norm from `base_rules/` (economics,
   ml_cv_nlp, cs_engineering or general_academic). It is a general fallback, not
   journal evidence. Do not impose canned forbidden phrases irrespective of use.

The reusable analysis records journal/key/aliases, corpus and article-type scope,
source coverage, per-section patterns and exceptions, source locations, confidence,
and synthesis rationale. Keep it self-contained and readable. For learn-only,
return this analysis and stop; do not force chapter generation.

## Adapt the nature family

Read [references/nature-adaptation.md](references/nature-adaptation.md) for an
adaptation request. Follow its discovery, file contract, registration, preservation,
validation and packaging steps. Inspect the actual target installation rather
than assuming this machine's paths or a fixed number of family members.

Preserve the target installation's original guides, configured journal routes,
evidence-completion behavior, natural-prose rules, language preferences and
verification annotations. The adapter supplies journal patterns and references;
it must not reset core workflows or import another journal's formal limits.
Generate English runtime files and Chinese reading copies beside originals.

Do not represent an official requirement as verified without its current primary
source. Consult the official author guidance when requirements are being generated;
if inaccessible or browsing is forbidden, mark those fields unverified and omit
unsupported numeric constraints. A source-corpus pattern is never official policy.

## Validate and report

Run `scripts/validate_nature_adapter.py --skills-root PATH --journal KEY` after
integration; the script reads only and checks the current file contract. Inspect
existing-journal preservation and effective instructions as well as paths. Report
static checks separately from real behavioral tests. A genuine writing trial needs
manuscript evidence; do not fabricate research or claim a trial from routing checks.

Return the installed/package paths, affected skills, provenance/scope, unresolved
requirements and validation results. Provide short user prompts for writing and
polishing with the new journal. Do not require the author to fill a setup template.

Files in this skill

  • SKILL-original.md7.5 KB
  • SKILL-zh.md2.7 KB
  • SKILL.md5.6 KB
  • agents/openai.yaml308 B
  • base_rules/cs_engineering.md645 B
  • base_rules/economics.md549 B
  • base_rules/general_academic.md608 B
  • base_rules/ml_cv_nlp.md626 B
  • references/nature-adaptation-zh.md4.5 KB
  • references/nature-adaptation.md8.6 KB
  • scripts/validate_nature_adapter.py4.3 KB

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