npx skills add ...
npx skills add davila7/claude-code-templates --skill citation-management
npx skills add davila7/claude-code-templates --skill citation-management
Comprehensive citation management for academic research. Search Google Scholar and PubMed for papers, extract accurate metadata, validate citations, and generate properly formatted BibTeX entries. This skill should be used when you need to find papers, verify citation information, convert DOIs to BibTeX, or ensure reference accuracy in scientific writing.
Manage citations systematically throughout the research and writing process. This skill provides tools and strategies for searching academic databases (Google Scholar, PubMed), extracting accurate metadata from multiple sources (CrossRef, PubMed, arXiv), validating citation information, and generating properly formatted BibTeX entries.
Critical for maintaining citation accuracy, avoiding reference errors, and ensuring reproducible research. Integrates seamlessly with the literature-review skill for comprehensive research workflows.
Use this skill when:
When creating documents with this skill, always consider adding scientific diagrams and schematics to enhance visual communication.
If your document does not already contain schematics or diagrams:
For new documents: Scientific schematics should be generated by default to visually represent key concepts, workflows, architectures, or relationships described in the text.
How to generate schematics:
The AI will automatically:
When to add schematics:
For detailed guidance on creating schematics, refer to the scientific-schematics skill documentation.
Citation management follows a systematic process:
Goal: Find relevant papers using academic search engines.
Google Scholar provides the most comprehensive coverage across disciplines.
Basic Search:
Advanced Search Strategies (see references/google_scholar_search.md):
"deep learning"author:LeCunintitle:"neural networks"machine learning -surveyBest Practices:
PubMed specializes in biomedical and life sciences literature (35+ million citations).
Basic Search:
Advanced PubMed Queries (see references/pubmed_search.md):
"Diabetes Mellitus"[MeSH]"cancer"[Title], "Smith J"[Author]AND, OR, NOT2020:2024[Publication Date]"Review"[Publication Type]Best Practices:
Goal: Convert paper identifiers (DOI, PMID, arXiv ID) to complete, accurate metadata.
For single DOIs, use the quick conversion tool:
For DOIs, PMIDs, arXiv IDs, or URLs:
Metadata Sources (see references/metadata_extraction.md):
CrossRef API: Primary source for DOIs
PubMed E-utilities: Biomedical literature
arXiv API: Preprints in physics, math, CS, q-bio
DataCite API: Research datasets, software, other resources
What Gets Extracted:
Goal: Generate clean, properly formatted BibTeX entries.
See references/bibtex_formatting.md for complete guide.
Common Entry Types:
@article: Journal articles (most common)@book: Books@inproceedings: Conference papers@incollection: Book chapters@phdthesis: Dissertations@misc: Preprints, software, datasetsRequired Fields by Type:
Use the formatter to standardize BibTeX files:
Formatting Operations:
Goal: Verify all citations are accurate and complete.
Validation Checks (see references/citation_validation.md):
DOI Verification:
Required Fields:
Data Consistency:
Duplicate Detection:
Format Compliance:
Validation Output:
Complete workflow for creating a bibliography:
This skill complements the literature-review skill:
Literature Review Skill → Systematic search and synthesis Citation Management Skill → Technical citation handling
Combined Workflow:
literature-review for comprehensive multi-database searchcitation-management to extract and validate all citationsliterature-review to synthesize findings thematicallycitation-management to verify final bibliography accuracyFinding Seminal Papers:
Advanced Operators (full list in references/google_scholar_search.md):
Example Searches:
Using MeSH Terms: MeSH (Medical Subject Headings) provides controlled vocabulary for precise searching.
"Diabetes Mellitus, Type 2"[MeSH]Field Tags:
Building Complex Queries:
E-utilities for Automation: The scripts use NCBI E-utilities API for programmatic access:
See references/pubmed_search.md for complete API documentation.
Search Google Scholar and export results.
Features:
Usage:
Search PubMed using E-utilities API.
Features:
Usage:
Extract complete metadata from paper identifiers.
Features:
Usage:
Validate BibTeX entries for accuracy and completeness.
Features:
Usage:
Format and clean BibTeX files.
Features:
Usage:
Quick DOI to BibTeX conversion.
Features:
Usage:
Start broad, then narrow:
Use multiple sources:
Leverage citations:
Document your searches:
Always use DOIs when available:
Verify extracted metadata:
Handle edge cases:
Maintain consistency:
Follow conventions:
Keep it clean:
Organize systematically:
Validate early and often:
Fix issues promptly:
Manual review for critical citations:
Single source bias: Only using Google Scholar or PubMed
Accepting metadata blindly: Not verifying extracted information
Ignoring DOI errors: Broken or incorrect DOIs in bibliography
Inconsistent formatting: Mixed citation key styles, formatting
Duplicate entries: Same paper cited multiple times with different keys
Missing required fields: Incomplete BibTeX entries
Outdated preprints: Citing preprint when published version exists
Special character issues: Broken LaTeX compilation due to characters
No validation before submission: Submitting with citation errors
Manual BibTeX entry: Typing entries by hand
Citation Management provides the technical infrastructure for Literature Review:
Combined workflow:
Citation Management ensures accurate references for Scientific Writing:
Citation Management works with Venue Templates for submission-ready manuscripts:
References (in references/):
google_scholar_search.md: Complete Google Scholar search guidepubmed_search.md: PubMed and E-utilities API documentationmetadata_extraction.md: Metadata sources and field requirementscitation_validation.md: Validation criteria and quality checksbibtex_formatting.md: BibTeX entry types and formatting rulesScripts (in scripts/):
search_google_scholar.py: Google Scholar search automationsearch_pubmed.py: PubMed E-utilities API clientextract_metadata.py: Universal metadata extractorvalidate_citations.py: Citation validation and verificationformat_bibtex.py: BibTeX formatter and cleanerdoi_to_bibtex.py: Quick DOI to BibTeX converterAssets (in assets/):
bibtex_template.bib: Example BibTeX entries for all typescitation_checklist.md: Quality assurance checklistSearch Engines:
Metadata APIs:
Tools and Validators:
Citation Styles:
The citation-management skill provides:
Use this skill to maintain accurate, complete citations throughout your research and ensure publication-ready bibliographies.
# Search PubMed
python scripts/search_pubmed.py "Alzheimer's disease treatment" \
--limit 100 \
--output alzheimers.json
# Search with MeSH terms and filters
python scripts/search_pubmed.py \
--query '"Alzheimer Disease"[MeSH] AND "Drug Therapy"[MeSH]' \
--date-start 2020 \
--date-end 2024 \
--publication-types "Clinical Trial,Review" \
--output alzheimers_trials.json# Convert single DOI
python scripts/doi_to_bibtex.py 10.1038/s41586-021-03819-2
# Convert multiple DOIs from a file
python scripts/doi_to_bibtex.py --input dois.txt --output references.bib
# Different output formats
python scripts/doi_to_bibtex.py 10.1038/nature12345 --format json# Extract from DOI
python scripts/extract_metadata.py --doi 10.1038/s41586-021-03819-2
# Extract from PMID
python scripts/extract_metadata.py --pmid 34265844
# Extract from arXiv ID
python scripts/extract_metadata.py --arxiv 2103.14030
# Extract from URL
python scripts/extract_metadata.py --url "https://www.nature.com/articles/s41586-021-03819-2"
# Batch extraction from file (mixed identifiers)
python scripts/extract_metadata.py --input identifiers.txt --output citations.bib@article{citationkey,
author = {Last1, First1 and Last2, First2},
title = {Article Title},
journal = {Journal Name},
year = {2024},
volume = {10},
number = {3},
pages = {123--145},
doi = {10.1234/example}
}
@inproceedings{citationkey,
author = {Last, First},
title = {Paper Title},
booktitle = {Conference Name},
year = {2024},
pages = {1--10}
}
@book{citationkey,
author = {Last, First},
title = {Book Title},
publisher = {Publisher Name},
year = {2024}
}# Format and clean BibTeX file
python scripts/format_bibtex.py references.bib \
--output formatted_references.bib
# Sort entries by citation key
python scripts/format_bibtex.py references.bib \
--sort key \
--output sorted_references.bib
# Sort by year (newest first)
python scripts/format_bibtex.py references.bib \
--sort year \
--descending \
--output sorted_references.bib
# Remove duplicates
python scripts/format_bibtex.py references.bib \
--deduplicate \
--output clean_references.bib
# Validate and report issues
python scripts/format_bibtex.py references.bib \
--validate \
--report validation_report.txt# Validate BibTeX file
python scripts/validate_citations.py references.bib
# Validate and fix common issues
python scripts/validate_citations.py references.bib \
--auto-fix \
--output validated_references.bib
# Generate detailed validation report
python scripts/validate_citations.py references.bib \
--report validation_report.json \
--verbose{
"total_entries": 150,
"valid_entries": 145,
"errors": [
{
"citation_key": "Smith2023",
"error_type": "missing_field",
"field": "journal",
"severity": "high"
},
{
"citation_key": "Jones2022",
"error_type": "invalid_doi",
"doi": "10.1234/broken",
"severity": "high"
}
],
"warnings": [
{
"citation_key": "Brown2021",
"warning_type": "possible_duplicate",
"duplicate_of": "Brown2021a",
"severity": "medium"
}
]
}# 1. Search for papers on your topic
python scripts/search_pubmed.py \
'"CRISPR-Cas Systems"[MeSH] AND "Gene Editing"[MeSH]' \
--date-start 2020 \
--limit 200 \
--output crispr_papers.json
# 2. Extract DOIs from search results and convert to BibTeX
python scripts/extract_metadata.py \
--input crispr_papers.json \
--output crispr_refs.bib
# 3. Add specific papers by DOI
python scripts/doi_to_bibtex.py 10.1038/nature12345 >> crispr_refs.bib
python scripts/doi_to_bibtex.py 10.1126/science.abcd1234 >> crispr_refs.bib
# 4. Format and clean the BibTeX file
python scripts/format_bibtex.py crispr_refs.bib \
--deduplicate \
--sort year \
--descending \
--output references.bib
# 5. Validate all citations
python scripts/validate_citations.py references.bib \
--auto-fix \
--report validation.json \
--output final_references.bib
# 6. Review validation report and fix any remaining issues
cat validation.json
# 7. Use in your LaTeX document
# \bibliography{final_references}# After completing literature review
# Verify all citations in the review document
python scripts/validate_citations.py my_review_references.bib --report review_validation.json
# Format for specific citation style if needed
python scripts/format_bibtex.py my_review_references.bib \
--style nature \
--output formatted_refs.bib"exact phrase" # Exact phrase matching
author:lastname # Search by author
intitle:keyword # Search in title only
source:journal # Search specific journal
-exclude # Exclude terms
OR # Alternative terms
2020..2024 # Year range# Find recent reviews on a topic
"CRISPR" intitle:review 2023..2024
# Find papers by specific author on topic
author:Church "synthetic biology"
# Find highly cited foundational work
"deep learning" 2012..2015 sort:citations
# Exclude surveys and focus on methods
"protein folding" -survey -review intitle:method[Title] # Search in title only
[Title/Abstract] # Search in title or abstract
[Author] # Search by author name
[Journal] # Search specific journal
[Publication Date] # Date range
[Publication Type] # Article type
[MeSH] # MeSH term# Clinical trials on diabetes treatment published recently
"Diabetes Mellitus, Type 2"[MeSH] AND "Drug Therapy"[MeSH]
AND "Clinical Trial"[Publication Type] AND 2020:2024[Publication Date]
# Reviews on CRISPR in specific journal
"CRISPR-Cas Systems"[MeSH] AND "Nature"[Journal] AND "Review"[Publication Type]
# Specific author's recent work
"Smith AB"[Author] AND cancer[Title/Abstract] AND 2022:2024[Publication Date]# Basic search
python scripts/search_google_scholar.py "quantum computing"
# Advanced search with filters
python scripts/search_google_scholar.py "quantum computing" \
--year-start 2020 \
--year-end 2024 \
--limit 100 \
--sort-by citations \
--output quantum_papers.json
# Export directly to BibTeX
python scripts/search_google_scholar.py "machine learning" \
--limit 50 \
--format bibtex \
--output ml_papers.bib# Simple keyword search
python scripts/search_pubmed.py "CRISPR gene editing"
# Complex query with filters
python scripts/search_pubmed.py \
--query '"CRISPR-Cas Systems"[MeSH] AND "therapeutic"[Title/Abstract]' \
--date-start 2020-01-01 \
--date-end 2024-12-31 \
--publication-types "Clinical Trial,Review" \
--limit 200 \
--output crispr_therapeutic.json
# Export to BibTeX
python scripts/search_pubmed.py "Alzheimer's disease" \
--limit 100 \
--format bibtex \
--output alzheimers.bib# Single DOI
python scripts/extract_metadata.py --doi 10.1038/s41586-021-03819-2
# Single PMID
python scripts/extract_metadata.py --pmid 34265844
# Single arXiv ID
python scripts/extract_metadata.py --arxiv 2103.14030
# From URL
python scripts/extract_metadata.py \
--url "https://www.nature.com/articles/s41586-021-03819-2"
# Batch processing (file with one identifier per line)
python scripts/extract_metadata.py \
--input paper_ids.txt \
--output references.bib
# Different output formats
python scripts/extract_metadata.py \
--doi 10.1038/nature12345 \
--format json # or bibtex, yaml# Basic validation
python scripts/validate_citations.py references.bib
# With auto-fix
python scripts/validate_citations.py references.bib \
--auto-fix \
--output fixed_references.bib
# Detailed validation report
python scripts/validate_citations.py references.bib \
--report validation_report.json \
--verbose
# Only check DOIs
python scripts/validate_citations.py references.bib \
--check-dois-only# Basic formatting
python scripts/format_bibtex.py references.bib
# Sort by year (newest first)
python scripts/format_bibtex.py references.bib \
--sort year \
--descending \
--output sorted_refs.bib
# Remove duplicates
python scripts/format_bibtex.py references.bib \
--deduplicate \
--output clean_refs.bib
# Complete cleanup
python scripts/format_bibtex.py references.bib \
--deduplicate \
--sort year \
--validate \
--auto-fix \
--output final_refs.bib# Single DOI
python scripts/doi_to_bibtex.py 10.1038/s41586-021-03819-2
# Multiple DOIs
python scripts/doi_to_bibtex.py \
10.1038/nature12345 \
10.1126/science.abc1234 \
10.1016/j.cell.2023.01.001
# From file (one DOI per line)
python scripts/doi_to_bibtex.py --input dois.txt --output references.bib
# Copy to clipboard
python scripts/doi_to_bibtex.py 10.1038/nature12345 --clipboard# Step 1: Find key papers on your topic
python scripts/search_google_scholar.py "transformer neural networks" \
--year-start 2017 \
--limit 50 \
--output transformers_gs.json
python scripts/search_pubmed.py "deep learning medical imaging" \
--date-start 2020 \
--limit 50 \
--output medical_dl_pm.json
# Step 2: Extract metadata from search results
python scripts/extract_metadata.py \
--input transformers_gs.json \
--output transformers.bib
python scripts/extract_metadata.py \
--input medical_dl_pm.json \
--output medical.bib
# Step 3: Add specific papers you already know
python scripts/doi_to_bibtex.py 10.1038/s41586-021-03819-2 >> specific.bib
python scripts/doi_to_bibtex.py 10.1126/science.aam9317 >> specific.bib
# Step 4: Combine all BibTeX files
cat transformers.bib medical.bib specific.bib > combined.bib
# Step 5: Format and deduplicate
python scripts/format_bibtex.py combined.bib \
--deduplicate \
--sort year \
--descending \
--output formatted.bib
# Step 6: Validate
python scripts/validate_citations.py formatted.bib \
--auto-fix \
--report validation.json \
--output final_references.bib
# Step 7: Review any issues
cat validation.json | grep -A 3 '"errors"'
# Step 8: Use in LaTeX
# \bibliography{final_references}# You have a text file with DOIs (one per line)
# dois.txt contains:
# 10.1038/s41586-021-03819-2
# 10.1126/science.aam9317
# 10.1016/j.cell.2023.01.001
# Convert all to BibTeX
python scripts/doi_to_bibtex.py --input dois.txt --output references.bib
# Validate the result
python scripts/validate_citations.py references.bib --verbose# You have a messy BibTeX file from various sources
# Clean it up systematically
# Step 1: Format and standardize
python scripts/format_bibtex.py messy_references.bib \
--output step1_formatted.bib
# Step 2: Remove duplicates
python scripts/format_bibtex.py step1_formatted.bib \
--deduplicate \
--output step2_deduplicated.bib
# Step 3: Validate and auto-fix
python scripts/validate_citations.py step2_deduplicated.bib \
--auto-fix \
--output step3_validated.bib
# Step 4: Sort by year
python scripts/format_bibtex.py step3_validated.bib \
--sort year \
--descending \
--output clean_references.bib
# Step 5: Final validation report
python scripts/validate_citations.py clean_references.bib \
--report final_validation.json \
--verbose
# Review report
cat final_validation.json# Find highly cited papers on a topic
python scripts/search_google_scholar.py "AlphaFold protein structure" \
--year-start 2020 \
--year-end 2024 \
--sort-by citations \
--limit 20 \
--output alphafold_seminal.json
# Extract the top 10 by citation count
# (script will have included citation counts in JSON)
# Convert to BibTeX
python scripts/extract_metadata.py \
--input alphafold_seminal.json \
--output alphafold_refs.bib
# The BibTeX file now contains the most influential papers# Core dependencies
pip install requests # HTTP requests for APIs
pip install bibtexparser # BibTeX parsing and formatting
pip install biopython # PubMed E-utilities access
# Optional (for Google Scholar)
pip install scholarly # Google Scholar API wrapper
# or
pip install selenium # For more robust Scholar scraping# For advanced validation
pip install crossref-commons # Enhanced CrossRef API access
pip install pylatexenc # LaTeX special character handling