citation-management

foryourhealth111-pixel/Vibe-SkillsResearchAudit failedSnapshot 50285abf59c7

Summary

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.

SKILL.md

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:

# 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

# 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

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

"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

citation-management

Summary

Citation Management

Overview

When to Use This Skill

Core Workflow

Phase 1: Citation Source Lookup

Google Scholar Search

PubMed Search

Phase 2: Metadata Extraction

Quick DOI to BibTeX Conversion

Comprehensive Metadata Extraction

Phase 3: BibTeX Formatting

Understanding BibTeX Entry Types

Formatting and Cleaning

Phase 4: Citation Validation

Comprehensive Validation

Phase 5: Integration with Writing Workflow

Building References for Manuscripts

Citation Validation In Review Documents

Search Strategies

Google Scholar Best Practices

PubMed Best Practices

Tools and Scripts

search_google_scholar.py

search_pubmed.py

extract_metadata.py

validate_citations.py

format_bibtex.py

doi_to_bibtex.py

Best Practices

Search Strategy

Metadata Extraction

BibTeX Quality

Validation

Common Pitfalls to Avoid

Example Workflows

Example 1: Building a Bibliography for a Paper

Example 2: Converting a List of DOIs

Example 3: Cleaning an Existing BibTeX File

Example 4: Finding and Citing Seminal Papers

Citation Outputs

Resources

Bundled Resources

External Resources

Dependencies

Required Python Packages

Optional Tools

Summary

Related skills