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Comprehensive citation management for academic research. Search Google Scholar and PubMed for papers, extract accurate metadata, validate citations, and…
Citation Management
Overview
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.
When to Use This Skill
Use this skill when:
Searching for specific papers on Google Scholar or PubMed
Converting DOIs, PMIDs, or arXiv IDs to properly formatted BibTeX
Extracting complete metadata for citations (authors, title, journal, year, etc.)
Validating existing citations for accuracy
Cleaning and formatting BibTeX files
Finding highly cited papers in a specific field
Verifying that citation information matches the actual publication
Building a bibliography for a manuscript or thesis
Checking for duplicate citations
Ensuring consistent citation formatting
Visual Enhancement with Scientific Schematics
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:
Use the scientific-schematics skill to generate AI-powered publication-quality diagrams
Simply describe your desired diagram in natural language
Nano Banana Pro will automatically generate, review, and refine the schematic
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:
python scripts/generate_schematic.py "your diagram description" -o figures/output.png
The AI will automatically:
Create publication-quality images with proper formatting
Review and refine through multiple iterations
Ensure accessibility (colorblind-friendly, high contrast)
Save outputs in the figures/ directory
When to add schematics:
Citation workflow diagrams
Literature search methodology flowcharts
Reference management system architectures
Citation style decision trees
Database integration diagrams
Any complex concept that benefits from visualization
For detailed guidance on creating schematics, refer to the scientific-schematics skill documentation.
Core Workflow
Citation management follows a systematic process:
Phase 1: Paper Discovery and Search
Goal: Find relevant papers using academic search engines.
Google Scholar Search
Google Scholar provides the most comprehensive coverage across disciplines.
Basic Search:
# Search for papers on a topic
python scripts/search_google_scholar.py "CRISPR gene editing" \
--limit 50 \
--output results.json
# Search with year filter
python scripts/search_google_scholar.py "machine learning protein folding" \
--year-start 2020 \
--year-end 2024 \
--limit 100 \
--output ml_proteins.json
Advanced Search Strategies (see references/google_scholar_search.md):
Use quotation marks for exact phrases: "deep learning"
Search by author: author:LeCun
Search in title: intitle:"neural networks"
Exclude terms: machine learning -survey
Find highly cited papers using sort options
Filter by date ranges to get recent work
Best Practices:
Use specific, targeted search terms
Include key technical terms and acronyms
Filter by recent years for fast-moving fields
Check "Cited by" to find seminal papers
Export top results for further analysis
PubMed Search
PubMed specializes in biomedical and life sciences literature (35+ million citations).
Basic Search:
# 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
Advanced PubMed Queries (see references/pubmed_search.md):
Use MeSH terms: "Diabetes Mellitus"[MeSH]
Field tags: "cancer"[Title], "Smith J"[Author]
Boolean operators: AND, OR, NOT
Date filters: 2020:2024[Publication Date]
Publication types: "Review"[Publication Type]
Combine with E-utilities API for automation
Best Practices:
Use MeSH Browser to find correct controlled vocabulary
Construct complex queries in PubMed Advanced Search Builder first
Include multiple synonyms with OR
Retrieve PMIDs for easy metadata extraction
Export to JSON or directly to BibTeX
Phase 2: Metadata Extraction
Goal: Convert paper identifiers (DOI, PMID, arXiv ID) to complete, accurate metadata.
Quick DOI to BibTeX Conversion
For single DOIs, use the quick conversion tool:
# 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
Comprehensive Metadata Extraction
For DOIs, PMIDs, arXiv IDs, or URLs:
# 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
Metadata Sources (see references/metadata_extraction.md):
CrossRef API: Primary source for DOIs
Comprehensive metadata for journal articles
Publisher-provided information
Includes authors, title, journal, volume, pages, dates
Free, no API key required
PubMed E-utilities: Biomedical literature
Official NCBI metadata
Includes MeSH terms, abstracts
PMID and PMCID identifiers
Free, API key recommended for high volume
arXiv API: Preprints in physics, math, CS, q-bio
Complete metadata for preprints
Version tracking
Author affiliations
Free, open access
DataCite API: Research datasets, software, other resources
Metadata for non-traditional scholarly outputs
DOIs for datasets and code
Free access
What Gets Extracted:
Required fields: author, title, year
Journal articles: journal, volume, number, pages, DOI
Books: publisher, ISBN, edition
Conference papers: booktitle, conference location, pages
Preprints: repository (arXiv, bioRxiv), preprint ID
Additional: abstract, keywords, URL
Phase 3: BibTeX Formatting
Goal: Generate clean, properly formatted BibTeX entries.
Understanding BibTeX Entry Types
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, datasets
Required Fields by Type:
@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}
}
Formatting and Cleaning
Use the formatter to standardize BibTeX files:
# 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
Formatting Operations:
Standardize field order
Consistent indentation and spacing
Proper capitalization in titles (protected with {})
Standardized author name format
Consistent citation key format
Remove unnecessary fields
Fix common errors (missing commas, braces)
Phase 4: Citation Validation
Goal: Verify all citations are accurate and complete.
Comprehensive Validation
# 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
Validation Checks (see references/citation_validation.md):
DOI Verification:
DOI resolves correctly via doi.org
Metadata matches between BibTeX and CrossRef
No broken or invalid DOIs
Required Fields:
All required fields present for entry type
No empty or missing critical information
Author names properly formatted
Data Consistency:
Year is valid (4 digits, reasonable range)
Volume/number are numeric
Pages formatted correctly (e.g., 123--145)
URLs are accessible
Duplicate Detection:
Same DOI used multiple times
Similar titles (possible duplicates)
Same author/year/title combinations
Format Compliance:
Valid BibTeX syntax
Proper bracing and quoting
Citation keys are unique
Special characters handled correctly
Validation Output:
{
"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"
}
]
}
Phase 5: Integration with Writing Workflow
Building References for Manuscripts
Complete workflow for creating a bibliography:
# 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}
Integration with Literature Review Skill
This skill complements the literature-review skill:
Literature Review Skill → Systematic search and synthesis
Citation Management Skill → Technical citation handling
Combined Workflow:
Use literature-review for comprehensive multi-database search
Use citation-management to extract and validate all citations
Use literature-review to synthesize findings thematically
Use citation-management to verify final bibliography accuracy
# 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
Search Strategies
Google Scholar Best Practices
Finding Seminal and High-Impact Papers (CRITICAL):
Always prioritize papers based on citation count, venue quality, and author reputation:
Citation Count Thresholds:
Paper Age
Citations
Classification
0-3 years
20+
Noteworthy
0-3 years
100+
Highly Influential
3-7 years
100+
Significant
3-7 years
500+
Landmark Paper
7+ years
500+
Seminal Work
7+ years
1000+
Foundational
Venue Quality Tiers:
Tier 1 (Prefer): Nature, Science, Cell, NEJM, Lancet, JAMA, PNAS
Tier 2 (High Priority): Impact Factor >10, top conferences (NeurIPS, ICML, ICLR)
Tier 3 (Good): Specialized journals (IF 5-10)
Tier 4 (Sparingly): Lower-impact peer-reviewed venues
Author Reputation Indicators:
Senior researchers with h-index >40
Multiple publications in Tier-1 venues
Leadership at recognized institutions
Awards and editorial positions
Search Strategies for High-Impact Papers:
Sort by citation count (most cited first)
Look for review articles from Tier-1 journals for overview
Check "Cited by" for impact assessment and recent follow-up work
Use citation alerts for tracking new citations to key papers
Filter by top venues using source:Nature or source:Science
Search for papers by known field leaders using author:LastName
Advanced Operators (full list in references/google_scholar_search.md):
"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
Example Searches:
# 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
PubMed Best Practices
Using MeSH Terms:
MeSH (Medical Subject Headings) provides controlled vocabulary for precise searching.
Find MeSH terms at https://meshb.nlm.nih.gov/search
Use in queries: "Diabetes Mellitus, Type 2"[MeSH]
Combine with keywords for comprehensive coverage
Field Tags:
[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
Building Complex Queries:
# 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]
E-utilities for Automation:
The scripts use NCBI E-utilities API for programmatic access:
ESearch: Search and retrieve PMIDs
EFetch: Retrieve full metadata
ESummary: Get summary information
ELink: Find related articles
See references/pubmed_search.md for complete API documentation.
Tools and Scripts
search_google_scholar.py
Search Google Scholar and export results.
Features:
Automated searching with rate limiting
Pagination support
Year range filtering
Export to JSON or BibTeX
Citation count information
Usage:
# 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
search_pubmed.py
Search PubMed using E-utilities API.
Features:
Complex query support (MeSH, field tags, Boolean)
Date range filtering
Publication type filtering
Batch retrieval with metadata
Export to JSON or BibTeX
Usage:
# 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
extract_metadata.py
Extract complete metadata from paper identifiers.
Features:
Supports DOI, PMID, arXiv ID, URL
Queries CrossRef, PubMed, arXiv APIs
Handles multiple identifier types
Batch processing
Multiple output formats
Usage:
# 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
validate_citations.py
Validate BibTeX entries for accuracy and completeness.
Features:
DOI verification via doi.org and CrossRef
Required field checking
Duplicate detection
Format validation
Auto-fix common issues
Detailed reporting
Usage:
# 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
format_bibtex.py
Format and clean BibTeX files.
Features:
Standardize formatting
Sort entries (by key, year, author)
Remove duplicates
Validate syntax
Fix common errors
Enforce citation key conventions
Usage:
# 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
doi_to_bibtex.py
Quick DOI to BibTeX conversion.
Features:
Fast single DOI conversion
Batch processing
Multiple output formats
Clipboard support
Usage:
# 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
Best Practices
Search Strategy
Start broad, then narrow:
Begin with general terms to understand the field
Refine with specific keywords and filters
Use synonyms and related terms
Use multiple sources:
Google Scholar for comprehensive coverage
PubMed for biomedical focus
arXiv for preprints
Combine results for completeness
Leverage citations:
Check "Cited by" for seminal papers
Review references from key papers
Use citation networks to discover related work
Document your searches:
Save search queries and dates
Record number of results
Note any filters or restrictions applied
Metadata Extraction
Always use DOIs when available:
Most reliable identifier
Permanent link to the publication
Best metadata source via CrossRef
Verify extracted metadata:
Check author names are correct
Verify journal/conference names
Confirm publication year
Validate page numbers and volume
Handle edge cases:
Preprints: Include repository and ID
Preprints later published: Use published version
Conference papers: Include conference name and location
Book chapters: Include book title and editors
Maintain consistency:
Use consistent author name format
Standardize journal abbreviations
Use same DOI format (URL preferred)
BibTeX Quality
Follow conventions:
Use meaningful citation keys (FirstAuthor2024keyword)
Protect capitalization in titles with {}
Use -- for page ranges (not single dash)
Include DOI field for all modern publications
Keep it clean:
Remove unnecessary fields
No redundant information
Consistent formatting
Validate syntax regularly
Organize systematically:
Sort by year or topic
Group related papers
Use separate files for different projects
Merge carefully to avoid duplicates
Validation
Validate early and often:
Check citations when adding them
Validate complete bibliography before submission
Re-validate after any manual edits
Fix issues promptly:
Broken DOIs: Find correct identifier
Missing fields: Extract from original source
Duplicates: Choose best version, remove others
Format errors: Use auto-fix when safe
Manual review for critical citations:
Verify key papers cited correctly
Check author names match publication
Confirm page numbers and volume
Ensure URLs are current
Common Pitfalls to Avoid
Single source bias: Only using Google Scholar or PubMed
Solution: Search multiple databases for comprehensive coverage
Accepting metadata blindly: Not verifying extracted information
Solution: Spot-check extracted metadata against original sources
Ignoring DOI errors: Broken or incorrect DOIs in bibliography
Solution: Run validation before final submission
Inconsistent formatting: Mixed citation key styles, formatting
Solution: Use format_bibtex.py to standardize
Duplicate entries: Same paper cited multiple times with different keys
Solution: Use duplicate detection in validation
Missing required fields: Incomplete BibTeX entries
Solution: Validate and ensure all required fields present
Outdated preprints: Citing preprint when published version exists
Solution: Check if preprints have been published, update to journal version
Special character issues: Broken LaTeX compilation due to characters
Solution: Use proper escaping or Unicode in BibTeX
No validation before submission: Submitting with citation errors
Solution: Always run validation as final check
Manual BibTeX entry: Typing entries by hand
Solution: Always extract from metadata sources using scripts
Example Workflows
Example 1: Building a Bibliography for a Paper
# 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}
Example 2: Converting a List of DOIs
# 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
Example 3: Cleaning an Existing BibTeX File
# 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
Example 4: Finding and Citing Seminal Papers
# 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
Integration with Other Skills
Literature Review Skill
Citation Management provides the technical infrastructure for Literature Review:
Literature Review: Multi-database systematic search and synthesis
Citation Management: Metadata extraction and validation
Combined workflow:
Use literature-review for systematic search methodology
Use citation-management to extract and validate citations
Use literature-review to synthesize findings
Use citation-management to ensure bibliography accuracy
Scientific Writing Skill
Citation Management ensures accurate references for Scientific Writing:
Export validated BibTeX for use in LaTeX manuscripts
Verify citations match publication standards
Format references according to journal requirements
Venue Templates Skill
Citation Management works with Venue Templates for submission-ready manuscripts:
Different venues require different citation styles
Generate properly formatted references
Validate citations meet venue requirements
Resources
Bundled Resources
References (in references/):
google_scholar_search.md: Complete Google Scholar search guide
pubmed_search.md: PubMed and E-utilities API documentation
metadata_extraction.md: Metadata sources and field requirements
citation_validation.md: Validation criteria and quality checks
bibtex_formatting.md: BibTeX entry types and formatting rules
Scripts (in scripts/):
search_google_scholar.py: Google Scholar search automation
search_pubmed.py: PubMed E-utilities API client
extract_metadata.py: Universal metadata extractor
validate_citations.py: Citation validation and verification
format_bibtex.py: BibTeX formatter and cleaner
doi_to_bibtex.py: Quick DOI to BibTeX converter
Assets (in assets/):
bibtex_template.bib: Example BibTeX entries for all types
citation_checklist.md: Quality assurance checklist
External Resources
Search Engines:
Google Scholar: https://scholar.google.com/
PubMed: https://pubmed.ncbi.nlm.nih.gov/
PubMed Advanced Search: https://pubmed.ncbi.nlm.nih.gov/advanced/
Metadata APIs:
CrossRef API: https://api.crossref.org/
PubMed E-utilities: https://www.ncbi.nlm.nih.gov/books/NBK25501/
arXiv API: https://arxiv.org/help/api/
DataCite API: https://api.datacite.org/
Tools and Validators:
MeSH Browser: https://meshb.nlm.nih.gov/search
DOI Resolver: https://doi.org/
BibTeX Format: http://www.bibtex.org/Format/
Citation Styles:
BibTeX documentation: http://www.bibtex.org/
LaTeX bibliography management: https://www.overleaf.com/learn/latex/Bibliography_management
Dependencies
Required Python Packages
# 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
Optional Tools
# For advanced validation
pip install crossref-commons # Enhanced CrossRef API access
pip install pylatexenc # LaTeX special character handling
Summary
The citation-management skill provides:
Comprehensive search capabilities for Google Scholar and PubMed
Automated metadata extraction from DOI, PMID, arXiv ID, URLs
Citation validation with DOI verification and completeness checking
BibTeX formatting with standardization and cleaning tools
Quality assurance through validation and reporting
Integration with scientific writing workflow
Reproducibility through documented search and extraction methods
Use this skill to maintain accurate, complete citations throughout your research and ensure publication-ready bibliographies.
25:[don't have the plugin yet? install it then click "run inline in claude" again.