Agent skill · Documentation

citation-management

Manage citations systematically throughout the research and writing process.

majiayu000github.com/majiayu000GitHub ↗
claude-codeMIT
Install
npx skills add majiayu000/claude-skill-registry --skill citation-management-sickn33-antigravity-awesome --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 2
SKILL.md size: 32 KB
Bundled scripts: none
Path: skills/analysis/citation-management-sickn33-antigravity-awesome/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 534
Language: HTML

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

Review
written from the skill's own SKILL.md · Aug 5, 2026

What it does

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.

How it works

Follows a core workflow with phases:

  • Phase 1: Paper Discovery and Search using Google Scholar and PubMed via Python scripts to perform basic and advanced searches, export results to JSON.
  • Phase 2: Metadata Extraction converts identifiers (DOI, PMID, arXiv ID) to complete metadata using scripts like extract_metadata.py and doi_to_bibtex.py, drawing from CrossRef, PubMed, arXiv, and DataCite APIs.
  • Phase 3: BibTeX Formatting generates and cleans BibTeX entries, applies formatting rules, sorts, deduplicates, and validates via format_bibtex.py.
  • Phase 4: Citation Validation runs validation scripts to verify DOIs, required fields, data consistency, duplicates, and format compliance, producing reports.
  • Phase 5: Integration with Writing Workflow shows end-to-end use, including adding citations to manuscripts and integrating with a literature-review skill.

Additionally, it prescribes adding schematics for visualization when applicable and describes generating publication-quality diagrams via a separate skill.

When to use it

Use when searching for papers, converting identifiers to BibTeX, extracting complete metadata, validating citations, cleaning BibTeX, finding highly cited papers, verifying publication matches, building a manuscript bibliography, checking duplicates, and ensuring consistent formatting.

What it can touch

Tools and scripts touched include: "python scripts/generate_schematic.py", "python scripts/search_google_scholar.py", "python scripts/search_pubmed.py", "python scripts/doi_to_bibtex.py", "python scripts/extract_metadata.py", "python scripts/format_bibtex.py", "python scripts/validate_citations.py". The workflow mentions interacting with CrossRef, PubMed, arXiv, and DataCite APIs for metadata extraction.

Caveats

License: MIT. Declared risk: unknown. Source: community. No explicit guarantees on outcomes; instructions emphasize generation and validation of bibliographic data rather than result quality guarantees.

From the SKILL.md

# 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

What's inside
Steps it walks through
  1. Overview
  2. When to Use This Skill
  3. Visual Enhancement with Scientific Schematics
  4. Core Workflow
  5. Phase 1: Paper Discovery and Search
  6. Phase 2: Metadata Extraction
  7. Phase 3: BibTeX Formatting
  8. Phase 4: Citation Validation
  9. Phase 5: Integration with Writing Workflow
  10. Search Strategies
  11. Google Scholar Best Practices
  12. PubMed Best Practices
  13. Tools and Scripts
  14. searchgooglescholar.py
Ships with 1 file
  • metadata.json
Commands it runs
python scripts/generate_schematic.py "your diagram description" -o figures/output.png
Search for papers on a topic
python scripts/search_google_scholar.py "CRISPR gene editing" \
Search with year filter
python scripts/search_google_scholar.py "machine learning protein folding" \
Search PubMed
python scripts/search_pubmed.py "Alzheimer's disease treatment" \
Search with MeSH terms and filters
python scripts/search_pubmed.py \
Convert single DOI
More from claude-skill-registry
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About this skill
What does the citation-management skill do?

Manage citations systematically throughout the research and writing process.

How do I install it?

Run `npx skills add majiayu000/claude-skill-registry --skill citation-management-sickn33-antigravity-awesome --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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