Transform lengthy academic papers into concise, structured 250-word abstracts.
日本語の概要は準備中です。原文の説明を表示しています。
Generate a complete NSFC (National Natural Science Foundation of China) General Program grant application in Chinese, following the 2026 official template. Use when the user provides a scientific hypothesis and wants to generate a full grant application document, including abs...
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Generate a complete NSFC General Program grant application from a scientific hypothesis and export it as a Word document.
Step 1: Parse hypothesis → extract key terms
Step 2: PubMed search → retrieve real references
Step 3: Generate full Markdown document (all sections)
Step 4: Save Markdown → call generate_docx.py → output .docx
Extract from the hypothesis:
Also generate the project title (≤30 Chinese characters):
Mechanism of {key molecules}/{axis}/{pathway} regulating {biological process} or similarMechanism of KDM5D/NEDD4/Cx43/ATP axis in astrocytes regulating microglial activation and inflammatory response in NP# [Title] heading at the top of the documentDo NOT ask the user for additional information unless they have provided it. Proceed with only the hypothesis.
MANDATORY: All references must be retrieved via real PubMed API calls. Never fabricate references.
Read references/pubmed-guide.md for the full search protocol.
Quick reference:
https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=pubmed&term={QUERY}&retmax=10&sort=relevance&retmode=jsonhttps://eutils.ncbi.nlm.nih.gov/entrez/eutils/esummary.fcgi?db=pubmed&id={PMIDs}&retmode=jsonRead references/writing-guide.md for detailed section-by-section requirements.
Generate the complete document as a single Markdown string following the structure in references/template.md.
Section generation rules:
| Section | Action |
|---|---|
| Abstract | Generate Chinese + English abstract + keywords |
| Section I: Research Rationale | Generate full content: 5 numbered subsections with #### subheadings + hypothesis statement + research directions + reference list |
| Section II: Research Content | Generate: 3-4 research modules + innovation points + 4-year plan |
| Section III-1.1: Preliminary Research Foundation | Generate: expand on preliminary data from Section I |
| Section III-1.2: Feasibility Analysis | Generate: 4-dimension feasibility analysis |
| Section III (2-4) and Section IV (all subsections) | Leave as headings only — do NOT generate content |
Citation rules:
[N] or [N][M] (max 2 per location), placed before punctuationConsistency rule: Every preliminary data item mentioned in Section I (Figure 1, Figure 2, Table 1, etc.) MUST appear again in Section III 1.1, expanded with methods and results detail.
Extract 2-3 core keywords from the hypothesis (Chinese preferred, use underscores).
Format: {keyword1}_{keyword2}_{YYYYMMDD_HHMMSS}
Example: EGFR_LungCancer_20260527_143022
Create this folder in the current working directory (or user's specified path).
Save the full generated document as {folder_name}.md inside the output folder (same stem as the folder name).
Run the script:
python scripts/generate_docx.py {output_folder}/{folder_name}.md {output_folder}
This produces {output_folder}/{folder_name}.docx.
If python-docx is not installed, inform the user:
pip install python-docx
Then re-run the script.
After successful generation, report:
{folder_name}.md and {folder_name}.docxreferences/template.md — 2026 official template structurereferences/writing-guide.md — detailed writing requirements per sectionreferences/pubmed-guide.md — PubMed API search protocol and reference formattingscripts/generate_docx.py — Markdown to Word converter (requires python-docx)This skill accepts requests that match the documented purpose of nsfc-grant-writer and include enough context to complete the workflow safely.
Do not continue the workflow when the request is out of scope, missing a critical input, or would require unsupported assumptions. Instead respond:
nsfc-grant-writeronly handles its documented workflow. Please provide the missing required inputs or switch to a more suitable skill.
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概要と使いどころ
Transform lengthy academic papers into concise, structured 250-word abstracts.
日本語の概要は準備中です。原文の説明を表示しています。
Precision editing tool that reduces abstract word count through intelligent compression techniques, maintaining scientific rigor while meeting strict journal and conference requirements.
日本語の概要は準備中です。原文の説明を表示しています。
Refines long medical academic texts into SCI-style unstructured Chinese and English abstracts; use when you need to condense drafts/reports/summaries into bilingual abstracts and generate Summary_Report.md.
日本語の概要は準備中です。原文の説明を表示しています。
Generates submission-ready Elsevier/SCI Highlights from manuscript text or extracted PDF/DOCX/TXT content. Use when a user needs 3-5 concise, evidence-grounded highlight bullets for a research paper, review, meta-analysis, case report, or bioinformatics manuscript.
日本語の概要は準備中です。原文の説明を表示しています。
Generates complete FAERS pharmacovigilance study designs for multi-drug or class-level safety comparison inside one predefined SOC or AE family using active comparators, disproportionality analysis, subgroup characterization, and reviewer-facing evidence control.
日本語の概要は準備中です。原文の説明を表示しています。
Analyze data with `adme-property-predictor` using a reproducible workflow, explicit validation, and structured outputs for review-ready interpretation.
日本語の概要は準備中です。原文の説明を表示しています。