drchronx/ai-agent-research-starter-kit/citation-management

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):

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

1. PubMed E-utilities: Biomedical literature

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

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

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

1. 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 2.5: Metadata Enrichment via Web Search (MANDATORY)

Goal: Detect and fill in any missing metadata fields using web search. This phase runs AFTER extraction and BEFORE formatting to ensure every BibTeX entry is complete.

Why This Is Critical: Metadata extraction from APIs (CrossRef, PubMed, arXiv) sometimes returns incomplete records — missing volume, pages, issue number, or DOI. These gaps must be filled before the bibliography is considered ready.

Step 1: Scan for Incomplete Entries

After extracting metadata, scan the BibTeX file for entries missing key fields:

Fields to check per entry type:

Any @article entry missing volume, pages, or doi is considered incomplete and must be enriched.

Step 2: Web Search for Missing Metadata

For each incomplete entry, search for the missing information:

Option A — Search by title and author (best for finding DOI):

python scripts/parallel_web.py search \
  "FIRST_AUTHOR TITLE JOURNAL_NAME volume pages DOI" \
  -o sources/search_YYYYMMDD_HHMMSS_citation_CITATIONKEY.md

Option B — Extract from DOI page (best when DOI is known but volume/pages missing):

python scripts/parallel_web.py extract \
  "https://doi.org/10.XXXX/YYYY" \
  --objective "extract complete citation metadata: volume, issue, pages, publication date" \
  -o sources/extract_YYYYMMDD_HHMMSS_doi_CITATIONKEY.md

Option C — Search CrossRef API directly (programmatic, fast):

python scripts/parallel_web.py search \
  "crossref DOI metadata FIRST_AUTHOR TITLE" \
  -o sources/search_YYYYMMDD_HHMMSS_crossref_CITATIONKEY.md

Option D — Search Google Scholar (fallback for hard-to-find papers):

python scripts/parallel_web.py search \
  "google scholar FIRST_AUTHOR TITLE YEAR complete citation" \
  -o sources/search_YYYYMMDD_HHMMSS_scholar_CITATIONKEY.md

Step 3: Update BibTeX Entries

After finding the missing metadata:

1. Open references.bib
2. Add the missing fields to the incomplete entry
3. Verify the found metadata is consistent with existing fields (same author, title, year)
4. Log each fix:

`` [HH:MM:SS] METADATA ENRICHED: [CitationKey] - added volume={X}, pages={Y--Z}, doi={10.XXX/YYY} ✅ ``

Step 4: Handle Unfindable Metadata

If metadata genuinely cannot be found after web search (very old paper, obscure conference, etc.):

1. Add a note field to the BibTeX entry explaining the gap:

``bibtex note = {Volume and pages not available — published online only} ``

1. Log the exception:

`` [HH:MM:SS] METADATA INCOMPLETE: [CitationKey] - pages unavailable (online-only publication) ⚠️ ``

1. These exceptions should be rare — most modern papers have complete metadata findable via web search.

Quick Reference: Common Missing Fields and Where to Find Them

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