/citation-management

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**:

# 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`):

1. **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

2. **PubMed E-utilities**: Biomedical literature

• Official NCBI metadata
• Includes MeSH terms, abstracts
• PMID and PMCID identifiers
• Free, API key recommended for high volume

3. **arXiv API**: Preprints in physics, math, CS, q-bio

• Complete metadata for preprints
• Version tracking
• Author affiliations
• Free, open access

4. **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**: jour