Fetches citation-management from davila7/claude-code-templates and configures it for Cursor.
3
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4
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Confirm successful installation by checking the skill directory location:
.cursor/skills/citation-management
Restart Cursor to activate citation-management. Access via /citation-management in your agent's command palette.
⚠
Security Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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.
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 topicpython scripts/search_google_scholar.py "CRISPR gene editing"\--limit50\--output results.json
# Search with year filterpython scripts/search_google_scholar.py "machine learning protein folding"\ --year-start 2020\ --year-end 2024\--limit100\--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 PubMedpython scripts/search_pubmed.py "Alzheimer's disease treatment"\--limit100\--output alzheimers.json
# Search with MeSH terms and filterspython 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 DOIpython scripts/doi_to_bibtex.py 10.1038/s41586-021-03819-2
# Convert multiple DOIs from a filepython scripts/doi_to_bibtex.py --input dois.txt --output references.bib
# Different output formatspython scripts/doi_to_bibtex.py 10.1038/nature12345 --format json
Comprehensive Metadata Extraction
For DOIs, PMIDs, arXiv IDs, or URLs:
# Extract from DOIpython scripts/extract_metadata.py --doi10.1038/s41586-021-03819-2
# Extract from PMIDpython scripts/extract_metadata.py --pmid34265844# Extract from arXiv IDpython scripts/extract_metadata.py --arxiv2103.14030# Extract from URLpython 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
›Access to product documentation and roadmap tools (Jira, Notion, etc.)
›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Steps
1Install product management skill
2Start with user story generation for known feature
3Progress to competitive analysis: research 2-3 competitors
4Use for roadmap prioritization: apply RICE/ICE scoring
5Draft stakeholder communications and refine based on feedback
6Build template library for recurring PM tasks
7Share effective prompts with product team
Common Pitfalls
⚠Not validating competitive research—verify facts before sharing
⚠Accepting user stories without involving engineering team
⚠Over-relying on frameworks without qualitative judgment
⚠Not customizing outputs to company culture and communication style
⚠Skipping stakeholder validation of generated requirements
Best Practices
✓ Do
+Validate research and competitive analysis with real data
+Collaborate with engineering when generating technical requirements
+Customize frameworks and templates to your company context
+Use skill for first drafts, refine with stakeholder input
+Document successful prompt patterns for PM tasks
+Combine AI efficiency with human judgment and intuition
✗ Don't
−Don't publish competitive analysis without fact-checking
−Don't finalize user stories without engineering review
−Don't make prioritization decisions solely on AI scoring
−Don't skip customer validation of generated requirements
−Don't ignore company-specific context and culture
💡 Pro Tips
★Provide context: company goals, constraints, customer feedback
★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
★Use skill for 70% generation + 30% customization to company needs
When to Use This
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path
1Basic: user stories, feature specs, status updates
