literature-review by davila7/claude-code-templates
npx skills add https://github.com/davila7/claude-code-templates --skill literature-review遵循严谨的学术方法进行系统、全面的文献综述。检索多个文献数据库,按主题综合发现,验证所有引用的准确性,并以 Markdown 和 PDF 格式生成专业的输出文档。
此技能集成了多个用于数据库访问的科学技能(gget、bioservices、datacommons-client),并为引用验证、结果聚合和文档生成提供了专门的工具。
在以下情况下使用此技能:
⚠️ 强制要求:每篇文献综述必须使用 scientific-schematics 技能包含至少 1-2 个 AI 生成的图表。
这是强制性的。没有视觉元素的文献综述是不完整的。在最终确定任何文档之前:
如何生成图表:
如何生成示意图:
python scripts/generate_schematic.py "your diagram description" -o figures/output.png
AI 将自动:
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何时添加示意图:
有关创建示意图的详细指导,请参阅 scientific-schematics 技能文档。
文献综述遵循结构化的多阶段工作流程:
选择适合该领域的数据库:
生物医学与生命科学:
gget 技能:gget search pubmed "search terms" 检索 PubMed/PMCgget 技能:gget search biorxiv "search terms" 检索预印本bioservices 技能检索 ChEMBL、KEGG、UniProt 等通用科学文献:
专业数据库:
gget alphafold 获取蛋白质结构gget cosmic 获取癌症基因组学数据datacommons-client 获取人口/统计数据## 检索策略
### 数据库:PubMed
- **检索日期**:2024-10-25
- **日期范围**:2015-01-01 至 2024-10-25
- **检索式**:
("CRISPR"[Title] OR "Cas9"[Title]) AND ("sickle cell"[MeSH] OR "SCD"[Title/Abstract]) AND 2015:2024[Publication Date]
- **结果**:247 篇文章
为每个检索的数据库重复此步骤。
scripts/search_databases.py 进行后处理:python search_databases.py combined_results.json \
--deduplicate \
--format markdown \
--output aggregated_results.md
python search_databases.py results.json --deduplicate --output unique_results.json
初始检索:n = X
├─ 去重后:n = Y
├─ 标题筛选后:n = Z
├─ 摘要筛选后:n = A
└─ 纳入综述:n = B
cp assets/review_template.md my_literature_review.md
示例结构:
#### 3.3.1 主题:CRISPR 递送方法
已研究了多种用于治疗性基因编辑的递送方法。病毒载体(AAV)在 15 项研究^1-15^ 中被使用,显示出高转导效率(65-85%),但引起了免疫原性担忧^3,7,12^。相比之下,脂质纳米颗粒表现出较低的效率(40-60%),但安全性更好^16-23^。
关键步骤:所有引用在最终提交前必须验证其准确性。
python scripts/verify_citations.py my_literature_review.md
此脚本:
references/citation_styles.md)python scripts/generate_pdf.py my_literature_review.md \
--citation-style apa \
--output my_review.pdf
选项:
--citation-style:apa、nature、chicago、vancouver、ieee--no-toc:禁用目录--no-numbers:禁用章节编号--check-deps:检查是否安装了 pandoc/xelatex通过 gget 技能访问:
# 检索 PubMed
gget search pubmed "CRISPR gene editing" -l 100
# 使用过滤器检索
# 使用 PubMed 高级检索构建器构建复杂查询
# 然后通过 gget 或直接 Entrez API 执行
检索技巧:
"sickle cell disease"[MeSH][Title]、[Title/Abstract]、[Author]2020:2024[Publication Date]通过 gget 技能访问:
gget search biorxiv "CRISPR sickle cell" -l 50
重要注意事项:
通过直接 API 或 WebFetch 访问:
# 示例检索类别:
# q-bio.QM (定量方法)
# q-bio.GN (基因组学)
# q-bio.MN (分子网络)
# cs.LG (机器学习)
# stat.ML (机器学习统计)
# 检索格式:类别 AND 术语
search_query = "cat:q-bio.QM AND ti:\"single cell sequencing\""
通过直接 API 访问(需要 API 密钥,或使用免费层级):
使用适当的技能:
bioservices 技能获取化学生物活性数据gget 或 bioservices 技能获取蛋白质信息bioservices 技能获取通路和基因信息gget 技能获取癌症突变数据gget alphafold 获取蛋白质结构gget 或直接 API 获取实验结构通过引用网络扩展检索:
详细的格式指南在 references/citation_styles.md 中。快速参考:
在最终定稿前,始终使用 verify_citations.py 验证引用。
生物医学文献综述的完整工作流程:
# 1. 从模板创建综述文档
cp assets/review_template.md crispr_sickle_cell_review.md
# 2. 使用适当的技能检索多个数据库
# - 使用 gget 技能检索 PubMed、bioRxiv
# - 使用直接 API 访问检索 arXiv、Semantic Scholar
# - 以 JSON 格式导出结果
# 3. 聚合和处理结果
python scripts/search_databases.py combined_results.json \
--deduplicate \
--rank citations \
--year-start 2015 \
--year-end 2024 \
--format markdown \
--output search_results.md \
--summary
# 4. 筛选结果并提取数据
# - 手动筛选标题、摘要、全文
# - 将关键数据提取到综述文档中
# - 按主题组织
# 5. 按照模板结构撰写综述
# - 引言部分明确目标
# - 详细的方法学部分
# - 按主题组织的结果
# - 批判性讨论
# - 清晰的结论
# 6. 验证所有引用
python scripts/verify_citations.py crispr_sickle_cell_review.md
# 审查引用报告
cat crispr_sickle_cell_review_citation_report.json
# 修复任何失败的引用并重新验证
python scripts/verify_citations.py crispr_sickle_cell_review.md
# 7. 生成专业 PDF
python scripts/generate_pdf.py crispr_sickle_cell_review.md \
--citation-style nature \
--output crispr_sickle_cell_review.pdf
# 8. 审查最终的 PDF 和 markdown 输出
此技能可与其他科学技能无缝协作:
脚本:
scripts/verify_citations.py:验证 DOI 并生成格式化引用scripts/generate_pdf.py:将 markdown 转换为专业 PDFscripts/search_databases.py:处理、去重和格式化检索结果参考文献:
references/citation_styles.md:详细的引用格式指南(APA、Nature、Vancouver、Chicago、IEEE)references/database_strategies.md:全面的数据库检索策略资源文件:
assets/review_template.md:包含所有部分的完整文献综述模板指南:
工具:
引用风格:
pip install requests # 用于引用验证
# 用于 PDF 生成
brew install pandoc # macOS
apt-get install pandoc # Linux
# 用于 LaTeX (PDF 生成)
brew install --cask mactex # macOS
apt-get install texlive-xetex # Linux
检查依赖项:
python scripts/generate_pdf.py --check-deps
此文献综述技能提供:
进行彻底、严谨的文献综述,满足学术标准,并提供任何领域当前知识的全面综合。
每周安装数
487
代码仓库
GitHub 星标数
22.6K
首次出现
Jan 21, 2026
安全审计
安装于
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Conduct systematic, comprehensive literature reviews following rigorous academic methodology. Search multiple literature databases, synthesize findings thematically, verify all citations for accuracy, and generate professional output documents in markdown and PDF formats.
This skill integrates with multiple scientific skills for database access (gget, bioservices, datacommons-client) and provides specialized tools for citation verification, result aggregation, and document generation.
Use this skill when:
⚠️ MANDATORY: Every literature review MUST include at least 1-2 AI-generated figures using the scientific-schematics skill.
This is not optional. Literature reviews without visual elements are incomplete. Before finalizing any document:
How to generate figures:
How to generate schematics:
python scripts/generate_schematic.py "your diagram description" -o figures/output.png
The AI will automatically:
When to add schematics:
For detailed guidance on creating schematics, refer to the scientific-schematics skill documentation.
Literature reviews follow a structured, multi-phase workflow:
Define Research Question : Use PICO framework (Population, Intervention, Comparison, Outcome) for clinical/biomedical reviews
Establish Scope and Objectives :
Develop Search Strategy :
Set Inclusion/Exclusion Criteria :
Select databases appropriate for the domain:
Biomedical & Life Sciences:
* Use `gget` skill: `gget search pubmed "search terms"` for PubMed/PMC
* Use `gget` skill: `gget search biorxiv "search terms"` for preprints
* Use `bioservices` skill for ChEMBL, KEGG, UniProt, etc.
General Scientific Literature:
* Search arXiv via direct API (preprints in physics, math, CS, q-bio)
* Search Semantic Scholar via API (200M+ papers, cross-disciplinary)
* Use Google Scholar for comprehensive coverage (manual or careful scraping)
Specialized Databases:
* Use `gget alphafold` for protein structures
* Use `gget cosmic` for cancer genomics
* Use `datacommons-client` for demographic/statistical data
* Use specialized databases as appropriate for the domain
2. Document Search Parameters :
## Search Strategy
### Database: PubMed
- **Date searched**: 2024-10-25
- **Date range**: 2015-01-01 to 2024-10-25
- **Search string**:
("CRISPR"[Title] OR "Cas9"[Title]) AND ("sickle cell"[MeSH] OR "SCD"[Title/Abstract]) AND 2015:2024[Publication Date]
- **Results**: 247 articles
Repeat for each database searched.
Export and Aggregate Results :
Export results in JSON format from each database
Combine all results into a single file
Use scripts/search_databases.py for post-processing:
python search_databases.py combined_results.json \
--deduplicate \
--format markdown \
--output aggregated_results.md
Deduplication :
python search_databases.py results.json --deduplicate --output unique_results.json
Title Screening :
Abstract Screening :
Full-Text Screening :
Create PRISMA Flow Diagram :
Initial search: n = X
├─ After deduplication: n = Y
├─ After title screening: n = Z
├─ After abstract screening: n = A
└─ Included in review: n = B
Extract Key Data from each included study:
Assess Study Quality :
Organize by Themes :
Create Review Document from template:
cp assets/review_template.md my_literature_review.md
Write Thematic Synthesis (NOT study-by-study summaries):
Example structure:
#### 3.3.1 Theme: CRISPR Delivery Methods
Multiple delivery approaches have been investigated for therapeutic
gene editing. Viral vectors (AAV) were used in 15 studies^1-15^ and
showed high transduction efficiency (65-85%) but raised immunogenicity
concerns^3,7,12^. In contrast, lipid nanoparticles demonstrated lower
efficiency (40-60%) but improved safety profiles^16-23^.
3. Critical Analysis :
* Evaluate methodological strengths and limitations across studies
* Assess quality and consistency of evidence
* Identify knowledge gaps and methodological gaps
* Note areas requiring future research
4. Write Discussion :
* Interpret findings in broader context
* Discuss clinical, practical, or research implications
* Acknowledge limitations of the review itself
* Compare with previous reviews if applicable
* Propose specific future research directions
CRITICAL : All citations must be verified for accuracy before final submission.
Verify All DOIs :
python scripts/verify_citations.py my_literature_review.md
This script:
* Extracts all DOIs from the document
* Verifies each DOI resolves correctly
* Retrieves metadata from CrossRef
* Generates verification report
* Outputs properly formatted citations
2. Review Verification Report :
* Check for any failed DOIs
* Verify author names, titles, and publication details match
* Correct any errors in the original document
* Re-run verification until all citations pass
3. Format Citations Consistently :
* Choose one citation style and use throughout (see `references/citation_styles.md`)
* Common styles: APA, Nature, Vancouver, Chicago, IEEE
* Use verification script output to format citations correctly
* Ensure in-text citations match reference list format
Generate PDF :
python scripts/generate_pdf.py my_literature_review.md \
--citation-style apa \
--output my_review.pdf
Options:
* `--citation-style`: apa, nature, chicago, vancouver, ieee
* `--no-toc`: Disable table of contents
* `--no-numbers`: Disable section numbering
* `--check-deps`: Check if pandoc/xelatex are installed
2. Review Final Output :
* Check PDF formatting and layout
* Verify all sections are present
* Ensure citations render correctly
* Check that figures/tables appear properly
* Verify table of contents is accurate
3. Quality Checklist :
* All DOIs verified with verify_citations.py
* Citations formatted consistently
* PRISMA flow diagram included (for systematic reviews)
* Search methodology fully documented
* Inclusion/exclusion criteria clearly stated
* Results organized thematically (not study-by-study)
* Quality assessment completed
* Limitations acknowledged
* References complete and accurate
* PDF generates without errors
Access via gget skill:
# Search PubMed
gget search pubmed "CRISPR gene editing" -l 100
# Search with filters
# Use PubMed Advanced Search Builder to construct complex queries
# Then execute via gget or direct Entrez API
Search tips :
"sickle cell disease"[MeSH][Title], [Title/Abstract], [Author]2020:2024[Publication Date]Access via gget skill:
gget search biorxiv "CRISPR sickle cell" -l 50
Important considerations :
Access via direct API or WebFetch:
# Example search categories:
# q-bio.QM (Quantitative Methods)
# q-bio.GN (Genomics)
# q-bio.MN (Molecular Networks)
# cs.LG (Machine Learning)
# stat.ML (Machine Learning Statistics)
# Search format: category AND terms
search_query = "cat:q-bio.QM AND ti:\"single cell sequencing\""
Access via direct API (requires API key, or use free tier):
Use appropriate skills:
bioservices skill for chemical bioactivitygget or bioservices skill for protein informationbioservices skill for pathways and genesgget skill for cancer mutationsgget alphafold for protein structuresgget or direct API for experimental structuresExpand search via citation networks:
Forward citations (papers citing key papers):
Backward citations (references from key papers):
Detailed formatting guidelines are in references/citation_styles.md. Quick reference:
Always verify citations with verify_citations.py before finalizing.
Complete workflow for a biomedical literature review:
# 1. Create review document from template
cp assets/review_template.md crispr_sickle_cell_review.md
# 2. Search multiple databases using appropriate skills
# - Use gget skill for PubMed, bioRxiv
# - Use direct API access for arXiv, Semantic Scholar
# - Export results in JSON format
# 3. Aggregate and process results
python scripts/search_databases.py combined_results.json \
--deduplicate \
--rank citations \
--year-start 2015 \
--year-end 2024 \
--format markdown \
--output search_results.md \
--summary
# 4. Screen results and extract data
# - Manually screen titles, abstracts, full texts
# - Extract key data into the review document
# - Organize by themes
# 5. Write the review following template structure
# - Introduction with clear objectives
# - Detailed methodology section
# - Results organized thematically
# - Critical discussion
# - Clear conclusions
# 6. Verify all citations
python scripts/verify_citations.py crispr_sickle_cell_review.md
# Review the citation report
cat crispr_sickle_cell_review_citation_report.json
# Fix any failed citations and re-verify
python scripts/verify_citations.py crispr_sickle_cell_review.md
# 7. Generate professional PDF
python scripts/generate_pdf.py crispr_sickle_cell_review.md \
--citation-style nature \
--output crispr_sickle_cell_review.pdf
# 8. Review final PDF and markdown outputs
This skill works seamlessly with other scientific skills:
Scripts:
scripts/verify_citations.py: Verify DOIs and generate formatted citationsscripts/generate_pdf.py: Convert markdown to professional PDFscripts/search_databases.py: Process, deduplicate, and format search resultsReferences:
references/citation_styles.md: Detailed citation formatting guide (APA, Nature, Vancouver, Chicago, IEEE)references/database_strategies.md: Comprehensive database search strategiesAssets:
assets/review_template.md: Complete literature review template with all sectionsGuidelines:
Tools:
Citation Styles:
pip install requests # For citation verification
# For PDF generation
brew install pandoc # macOS
apt-get install pandoc # Linux
# For LaTeX (PDF generation)
brew install --cask mactex # macOS
apt-get install texlive-xetex # Linux
Check dependencies:
python scripts/generate_pdf.py --check-deps
This literature-review skill provides:
Conduct thorough, rigorous literature reviews that meet academic standards and provide comprehensive synthesis of current knowledge in any domain.
Weekly Installs
487
Repository
GitHub Stars
22.6K
First Seen
Jan 21, 2026
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Installed on
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github-copilot349
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