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AILatest-Studio/ailatest-skill

Draft, revise, and finalize Chinese natural science fund proposals in the user's established academic style. Use when Codex must edit a grant DOCX, learn style from a reference fund PDF/DOCX, restructure research rationale, literature review, scientific questions, three research-content blocks, methods, limitations, feasibility, annual plans, expected outcomes, achievements, sensors, citations, or references; synchronize正文 with figures; verify publication and bibliography facts; or perform final logic, formatting, and submission-readiness audits.

What is ailatest-skill?

This skill enables Codex (or a similar AI coding agent) to act as a co-author and final editor for Chinese National Natural Science Foundation (NSFC) proposals. It provides a structured, multi-step workflow: locating the user's latest DOCX, extracting and verifying key proposal components (scientific questions, research content, methods, limitations, etc.), and performing surgical edits that preserve the user's established academic voice and formal, mechanism-oriented prose. The skill enforces a strict logic contract (e.g., exactly two scientific questions, three research-content blocks) and a formal writing style with precise Chinese academic verbs. It also runs a local audit script and follows detailed formatting rules for figures, citations, and special sections (e.g., bolding the PI's name, formatting sensor specifications). The skill emphasizes evidence integrity—it never invents citations or data, and it uses primary sources to verify publication facts, journal rankings, and sensor specs. It is designed to output a new, versioned DOCX file, leaving the user's original unchanged.

Works with~Claude Code✓Codex CLI~Cursor
npx skills add https://github.com/AILatest-Studio/ailatest-skill/tree/HEAD/skills/write-natural-science-fund-proposals

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Documentation

Write Natural Science Fund Proposals

Write as a proposal co-author and final editor. Preserve the user's latest document, established terminology, and formal mechanism-oriented voice.

Core Workflow

  1. Locate the newest user-edited DOCX. Treat it as the editing base and preserve it.
  2. Identify style references. Convert referenced PDFs to Markdown before substantive reading, then inspect original PDF pages for layout-sensitive details.
  3. Extract the proposal title, two scientific questions, three research contents, corresponding methods, limitations, figures, outcomes, annual plan, and achievements.
  4. Build a consistency matrix before rewriting. Use references/consistency-and-facts.md.
  5. Revise surgically in the existing structure. Follow the user's voice in references/voice-and-structure.md.
  6. Verify publications, journals, years, volumes, pages/article numbers, SCI claims, rankings, and cited facts with primary sources.
  7. Apply proposal-specific Word formatting and final checks from references/docx-and-final-qa.md.
  8. Render the whole DOCX, inspect every page, fix layout defects, and rerender.
  9. Run scripts/audit_proposal_docx.py and resolve errors before delivery.

Logic Contract

  • Use exactly two scientific questions for this small project.
  • Use exactly three major research-content blocks.
  • Map each research-content block to one expanded research-scheme block and one corresponding limitation/gap.
  • Keep content focused on what is studied; put detailed tools, models, sensor specifications, and procedures in the scheme/method section.
  • Keep figures, captions,正文 references, outcomes, and annual tasks consistent with the same three-block logic.
  • Keep the annual plan separate from the technical route.
  • Keep the final application/service target to one sentence.
  • Reconcile every later edit across the full proposal instead of changing one isolated paragraph.

Writing Contract

  • Use formal Chinese academic prose with explicit objects, mechanisms, variables, causal links, evidence, and validation paths.
  • Move from limitation or contradiction to the proposed mechanism and then to expected scientific contribution.
  • Prefer precise verbs such as 构建, 表征, 解析, 识别, 量化, 揭示, 验证, and 界定.
  • Use inline numbered logic such as ①②③ when it improves dense proposal prose.
  • Avoid marketing language, inflated claims, generic AI phrasing, and decorative headings.
  • Never write process language such as 国家基金本子, 参考已有文件, 沿用某本子, or 根据修改指令 in the submission text.
  • Avoid a paragraph ending with a last visual line containing only one Chinese character. Fix wording or spacing after rendering.

Personal Formatting Rules

  • Bold 翁建涛 and Jiantao Weng in the achievements section.
  • Put 6.1.7 知识产权与设备方法基础。 on its own bold line. Start the explanatory paragraph on the next line.
  • Format numeric in-text citations such as [10,15-21] as superscript. Do not superscript bibliography entries or [J], [M], and [P] document-type markers.
  • Keep 申请软件著作权2项 consistent across annual plan and expected outcomes.
  • Include noise level whenever listing temperature/humidity, CO2, PM2.5, and illuminance environmental sensors.
  • Remove unresolved placeholders such as 需填, 待补, or bracketed sensor specifications.
  • Use the figure-caption and diagram rules from $design-natural-science-fund-figures when figures are part of the task.

Evidence And Integrity

  • Never invent citations, DOI values, indexing status, quartiles, TOP status, corresponding-author roles, equipment ranges, or accuracy values.
  • Browse current primary sources for time-sensitive bibliographic and ranking claims.
  • Prefer publisher pages, Crossref, official journal pages, Web of Science/Clarivate records when accessible, and manufacturer datasheets.
  • Mark uncertain facts for verification instead of presenting them as settled.
  • Keep quoted source text minimal and paraphrase in the user's voice.

Deliverables

  • Save a new versioned DOCX; do not overwrite the user's source.
  • Preserve unrelated formatting and user edits.
  • Deliver only the requested final artifact unless the user asks for audit reports or intermediates.

Bundled example

See assets/proposal-core-logic.png for a reconstructed diagram of the proposal's argument skeleton: basis/gap → two scientific questions → three research contents and evidence → contribution and application boundary. The example is synthetic and does not use a user-supplied proposal or project data.

Resources

Individual skills in this repo

This repo contains 2 individual skills — each has its own dedicated page.

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