| name | citation-management |
| description | Comprehensive citation management for academic research. Resolve bibliographic identifiers, extract accurate metadata, validate citations, deduplicate references, and generate properly formatted BibTeX entries. This skill should be used when you need to verify citation information, convert DOIs/PMIDs/arXiv IDs to BibTeX, or ensure reference accuracy in scientific writing. |
| allowed-tools | Read Write Edit Bash |
| license | MIT License |
| metadata | {"skill-author":"K-Dense Inc."} |
Citation Management
Overview
Manage citations systematically throughout the research and writing process. This skill provides tools and strategies for extracting accurate metadata from identifiers and bibliographic sources, validating citation information, cleaning duplicate references, and generating properly formatted BibTeX entries.
Critical for maintaining citation accuracy, avoiding reference errors, and ensuring reproducible research.
When to Use This Skill
Use this skill when:
- Resolving known or candidate papers from identifiers, titles, Google Scholar, or PubMed records
- 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
- Checking citation counts for known 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
Core Workflow
Citation management follows a systematic process:
Phase 1: Citation Source Lookup
Goal: Locate exact source records for known references or tightly scoped citation candidates.
Google Scholar Search
Google Scholar provides the most comprehensive coverage across disciplines.
Basic Search:
python scripts/search_google_scholar.py "CRISPR gene editing" \
--limit 50 \
--output results.json
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:
python scripts/search_pubmed.py "Alzheimer's disease treatment" \
--limit 100 \
--output alzheimers.json
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:
python scripts/doi_to_bibtex.py 10.1038/s41586-021-03819-2
python scripts/doi_to_bibtex.py --input dois.txt --output references.bib
python scripts/doi_to_bibtex.py 10.1038/nature12345 --format json
Comprehensive Metadata Extraction
For DOIs, PMIDs, arXiv IDs, or URLs:
python scripts/extract_metadata.py --doi 10.1038/s41586-021-03819-2
python scripts/extract_metadata.py --pmid 34265844
python scripts/extract_metadata.py --arxiv 2103.14030
python scripts/extract_metadata.py --url "https://www.nature.com/articles/s41586-021-03819-2"
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:
python scripts/format_bibtex.py references.bib \
--output formatted_references.bib
python scripts/format_bibtex.py references.bib \
--sort key \
--output sorted_references.bib
python scripts/format_bibtex.py references.bib \
--sort year \
--descending \
--output sorted_references.bib
python scripts/format_bibtex.py references.bib \
--deduplicate \
--output clean_references.bib
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
python scripts/validate_citations.py references.bib
python scripts/validate_citations.py references.bib \
--auto-fix \
--output validated_references.bib
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:
python scripts/search_pubmed.py \
'"CRISPR-Cas Systems"[MeSH] AND "Gene Editing"[MeSH]' \
--date-start 2020 \
--limit 200 \
--output crispr_papers.json
python scripts/extract_metadata.py \
--input crispr_papers.json \
--output crispr_refs.bib
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
python scripts/format_bibtex.py crispr_refs.bib \
--deduplicate \
--sort year \
--descending \
--output references.bib
python scripts/validate_citations.py references.bib \
--auto-fix \
--report validation.json \
--output final_references.bib
cat validation.json
Citation Validation In Review Documents
When a review document already exists, use this skill for the technical citation layer:
- Extract all cited identifiers and bibliography records
- Validate metadata against DOI, PMID, arXiv, CrossRef, or PubMed sources
- Normalize BibTeX keys and citation style fields
- Verify the final bibliography before manuscript build or submission
python scripts/validate_citations.py my_review_references.bib --report review_validation.json
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:
"Diabetes Mellitus, Type 2"[MeSH] AND "Drug Therapy"[MeSH]
AND "Clinical Trial"[Publication Type] AND 2020:2024[Publication Date]
"CRISPR-Cas Systems"[MeSH] AND "Nature"[Journal] AND "Review"[Publication Type]
"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:
python scripts/search_google_scholar.py "quantum computing"
python scripts/search_google_scholar.py "quantum computing" \
--year-start 2020 \
--year-end 2024 \
--limit 100 \
--sort-by citations \
--output quantum_papers.json
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:
python scripts/search_pubmed.py "CRISPR gene editing"
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
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:
python scripts/extract_metadata.py --doi 10.1038/s41586-021-03819-2
python scripts/extract_metadata.py --pmid 34265844
python scripts/extract_metadata.py --arxiv 2103.14030
python scripts/extract_metadata.py \
--url "https://www.nature.com/articles/s41586-021-03819-2"
python scripts/extract_metadata.py \
--input paper_ids.txt \
--output references.bib
python scripts/extract_metadata.py \
--doi 10.1038/nature12345 \
--format json
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:
python scripts/validate_citations.py references.bib
python scripts/validate_citations.py references.bib \
--auto-fix \
--output fixed_references.bib
python scripts/validate_citations.py references.bib \
--report validation_report.json \
--verbose
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:
python scripts/format_bibtex.py references.bib
python scripts/format_bibtex.py references.bib \
--sort year \
--descending \
--output sorted_refs.bib
python scripts/format_bibtex.py references.bib \
--deduplicate \
--output clean_refs.bib
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:
python scripts/doi_to_bibtex.py 10.1038/s41586-021-03819-2
python scripts/doi_to_bibtex.py \
10.1038/nature12345 \
10.1126/science.abc1234 \
10.1016/j.cell.2023.01.001
python scripts/doi_to_bibtex.py --input dois.txt --output references.bib
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
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
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
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
cat transformers.bib medical.bib specific.bib > combined.bib
python scripts/format_bibtex.py combined.bib \
--deduplicate \
--sort year \
--descending \
--output formatted.bib
python scripts/validate_citations.py formatted.bib \
--auto-fix \
--report validation.json \
--output final_references.bib
cat validation.json | grep -A 3 '"errors"'
Example 2: Converting a List of DOIs
python scripts/doi_to_bibtex.py --input dois.txt --output references.bib
python scripts/validate_citations.py references.bib --verbose
Example 3: Cleaning an Existing BibTeX File
python scripts/format_bibtex.py messy_references.bib \
--output step1_formatted.bib
python scripts/format_bibtex.py step1_formatted.bib \
--deduplicate \
--output step2_deduplicated.bib
python scripts/validate_citations.py step2_deduplicated.bib \
--auto-fix \
--output step3_validated.bib
python scripts/format_bibtex.py step3_validated.bib \
--sort year \
--descending \
--output clean_references.bib
python scripts/validate_citations.py clean_references.bib \
--report final_validation.json \
--verbose
cat final_validation.json
Example 4: Finding and Citing Seminal Papers
python scripts/search_google_scholar.py "AlphaFold protein structure" \
--year-start 2020 \
--year-end 2024 \
--sort-by citations \
--limit 20 \
--output alphafold_seminal.json
python scripts/extract_metadata.py \
--input alphafold_seminal.json \
--output alphafold_refs.bib
Citation Outputs
Citation management outputs are reusable in writing and submission workflows:
- Export validated BibTeX for LaTeX manuscripts
- Verify citations match publication standards
- Format references according to journal requirements
- Keep bibliography files deduplicated and reproducible
- Generate properly formatted references
- Validate citations meet venue requirements
Resources
Bundled Resources
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 rules
Scripts (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 converter
Assets (in assets/):
bibtex_template.bib: Example BibTeX entries for all typescitation_checklist.md: Quality assurance checklist
External Resources
Search Engines:
Metadata APIs:
Tools and Validators:
Citation Styles:
Dependencies
Required Python Packages
pip install requests
pip install bibtexparser
pip install biopython
pip install scholarly
pip install selenium
Optional Tools
pip install crossref-commons
pip install pylatexenc
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.
