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。
Generates a first draft of a clinical meta-analysis paper. Input the research report (including Methods and Results sections), language, and title to automatically generate a complete paper draft including Abstract, Introduction, Discussion, and other sections, with automatic ...
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Generates a first draft of a meta-analysis paper meeting SCI journal standards based on the user-provided research report, including reference support.
scripts/insert_references.py is the most direct path to complete the request.meta-manuscript-generator package behavior rather than a generic answer.scripts/insert_references.py plus 1 additional script(s).references/ for task-specific guidance.Python: 3.10+. Repository baseline for current packaged skills.Third-party packages: not explicitly version-pinned in this skill package. Add pinned versions if this skill needs stricter environment control.cd "20260316/scientific-skills/Academic Writing/meta-manuscript-generator"
python -m py_compile scripts/insert_references.py
python scripts/insert_references.py --help
Example run plan:
CONFIG block or documented parameters if the script uses fixed settings.python scripts/insert_references.py with the validated inputs.See ## Workflow above for related details.
scripts/insert_references.py with additional helper scripts under scripts/.references/ contains supporting rules, prompts, or checklists.The user needs to provide:
Input Format Example:
Methods and Results: (User's complete methods and results content...)
Language: (Chinese/English)
Title: (Paper title)
Extract and structure the following information from the research report:
Methods Section Keywords (for reference retrieval):
Study Population:
Exposure/Intervention:
Outcome Measures:
Research Direction:
Primary Research Methods:
Key Points of the Results Section(For discussion writing, extracted by module):
Main findings and statistical data
Analysis of the relationship between exposure and outcomes
Findings that need to be compared with previous studies
Contextual and background significance
Results relevant to clinical significance
Methodological limitations
Use scripts/search_references.py to retrieve PubMed references.
API Description:Use the official PubMed E-utilities API
https://eutils.ncbi.nlm.nih.gov/entrez/eutilsSearch Workflow:
Search Allocation:
Usage Example:
from scripts.search_references import search_references_for_theme
# Retrieve references for the Introduction
intro_refs = search_references_for_theme("immune checkpoint inhibitors non-small cell lung cancer efficacy meta-analysis")
# Retrieve references for the Discussion
discussion_refs = search_references_for_theme( "PD-1 inhibitors lung cancer survival mechanism")
Generate each section of the manuscript in the following order. Detailed guidelines are available in references/writing-guide.md。
Citation Format During Writing:Use [PMID: xxxxxxxx] to mark references, which will be processed later
Write in modular order with natural transitions between sections:
| Module | Content | Word Count |
|---|---|---|
| Opening of Discussion | Summary of main findings and statistical significance | 150–200 |
| Results Interpretation | Mechanistic analysis and clinical relevance | ≥150 |
| Literature Comparison | Comparison with previous studies | ≥150 |
| Study Limitations | Methodological and clinical limitations | 100–150 |
| Closing of Discussion | Conclusions and future directions | 100–150 |
Each module should cite no more than 10 references.
Use scripts/insert_references.py to process references.
API Description:Use the PubMed efetch API to retrieve formatted citations
Processing Workflow:
Article Segmentation:Split the manuscript into sections using markers such as ## Discussion
PMID Extraction: Use regular expressions to identify [PMID: number] or 【PMID: number】
PubMed efetch Call:Retrieve full citation details for each PMID.
AMA Citation Generation:Format as: Author(s). Title. Journal. Year;Volume(Issue):Pages.
In-text Citation Replacement:Replace [PMID: xxx] with [[n]](link) format
Renumbering Numeric Citations:Resolve conflicts with existing bracketed numeric citations.
Reference List Generation:Number references sequentially based on citation order.
Usage Example:
from scripts.insert_references import insert_references
# Process the complete manuscript
final_article = insert_references(
article=draft_with_pmid_markers,
new_references="" # Optional: additional references
)
Integrate the generated content with the user-provided Methods and Results sections into a complete manuscript.
Integration Order:
Title (user-provided)
Abstract (generated)
Introduction (generated)
Materials and Methods (extracted from user input, unchanged)
Results (extracted from user input, unchanged)
Discussion (generated)
Conclusion (generated)
References (generated)
Final Output Format:
# [Article Title]
## Abstract
[Abstract content]
## Introduction
[Introduction content with hyperlinks]
## Materials and Methods
[User-provided Methods section, original text preserved]
## Results
[User-provided Results section, original text preserved]
## Discussion
[Discussion content, no subheadings, natural flow]
## Conclusion
[Conclusion]
## Input Validation
This skill accepts requests that match the documented purpose of `meta-manuscript-generator` 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:
> `meta-manuscript-generator` only handles its documented workflow. Please provide the missing required inputs or switch to a more suitable skill.
## References
[1] Author et al. Title. Journal. Year;Vol(Issue):Pages. [https://pubmed.ncbi.nlm.nih.gov/PMID/]
[2] ...
Note:The user-provided Methods and Results sections should be preserved in their original wording, with only minimal formatting adjustments made where necessary.
[PMID: 12345678][[1]](link)Verify after generation:
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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。