{"id":"43c2d9ff-b930-4b28-bc65-9c309ede13cd","shortId":"rd6pHb","kind":"skill","title":"citation-management","tagline":"Manage citations systematically throughout the research and writing process.","description":"# Citation Management\n\n## Overview\n\nManage 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.\n\nCritical for maintaining citation accuracy, avoiding reference errors, and ensuring reproducible research. Integrates seamlessly with the literature-review skill for comprehensive research workflows.\n\n## When to Use This Skill\n\nUse this skill when:\n- Searching for specific papers on Google Scholar or PubMed\n- Converting DOIs, PMIDs, or arXiv IDs to properly formatted BibTeX\n- Extracting complete metadata for citations (authors, title, journal, year, etc.)\n- Validating existing citations for accuracy\n- Cleaning and formatting BibTeX files\n- Finding highly cited papers in a specific field\n- Verifying that citation information matches the actual publication\n- Building a bibliography for a manuscript or thesis\n- Checking for duplicate citations\n- Ensuring consistent citation formatting\n\n## Visual Enhancement with Scientific Schematics\n\n**When creating documents with this skill, always consider adding scientific diagrams and schematics to enhance visual communication.**\n\nIf your document does not already contain schematics or diagrams:\n- Use the **scientific-schematics** skill to generate AI-powered publication-quality diagrams\n- Simply describe your desired diagram in natural language\n- Nano Banana Pro will automatically generate, review, and refine the schematic\n\n**For new documents:** Scientific schematics should be generated by default to visually represent key concepts, workflows, architectures, or relationships described in the text.\n\n**How to generate schematics:**\n```bash\npython scripts/generate_schematic.py \"your diagram description\" -o figures/output.png\n```\n\nThe AI will automatically:\n- Create publication-quality images with proper formatting\n- Review and refine through multiple iterations\n- Ensure accessibility (colorblind-friendly, high contrast)\n- Save outputs in the figures/ directory\n\n**When to add schematics:**\n- Citation workflow diagrams\n- Literature search methodology flowcharts\n- Reference management system architectures\n- Citation style decision trees\n- Database integration diagrams\n- Any complex concept that benefits from visualization\n\nFor detailed guidance on creating schematics, refer to the scientific-schematics skill documentation.\n\n---\n\n## Core Workflow\n\nCitation management follows a systematic process:\n\n### Phase 1: Paper Discovery and Search\n\n**Goal**: Find relevant papers using academic search engines.\n\n#### Google Scholar Search\n\nGoogle Scholar provides the most comprehensive coverage across disciplines.\n\n**Basic Search**:\n```bash\n# Search for papers on a topic\npython scripts/search_google_scholar.py \"CRISPR gene editing\" \\\n  --limit 50 \\\n  --output results.json\n\n# Search with year filter\npython scripts/search_google_scholar.py \"machine learning protein folding\" \\\n  --year-start 2020 \\\n  --year-end 2024 \\\n  --limit 100 \\\n  --output ml_proteins.json\n```\n\n**Advanced Search Strategies** (see `references/google_scholar_search.md`):\n- Use quotation marks for exact phrases: `\"deep learning\"`\n- Search by author: `author:LeCun`\n- Search in title: `intitle:\"neural networks\"`\n- Exclude terms: `machine learning -survey`\n- Find highly cited papers using sort options\n- Filter by date ranges to get recent work\n\n**Best Practices**:\n- Use specific, targeted search terms\n- Include key technical terms and acronyms\n- Filter by recent years for fast-moving fields\n- Check \"Cited by\" to find seminal papers\n- Export top results for further analysis\n\n#### PubMed Search\n\nPubMed specializes in biomedical and life sciences literature (35+ million citations).\n\n**Basic Search**:\n```bash\n# Search PubMed\npython scripts/search_pubmed.py \"Alzheimer's disease treatment\" \\\n  --limit 100 \\\n  --output alzheimers.json\n\n# Search with MeSH terms and filters\npython scripts/search_pubmed.py \\\n  --query '\"Alzheimer Disease\"[MeSH] AND \"Drug Therapy\"[MeSH]' \\\n  --date-start 2020 \\\n  --date-end 2024 \\\n  --publication-types \"Clinical Trial,Review\" \\\n  --output alzheimers_trials.json\n```\n\n**Advanced PubMed Queries** (see `references/pubmed_search.md`):\n- Use MeSH terms: `\"Diabetes Mellitus\"[MeSH]`\n- Field tags: `\"cancer\"[Title]`, `\"Smith J\"[Author]`\n- Boolean operators: `AND`, `OR`, `NOT`\n- Date filters: `2020:2024[Publication Date]`\n- Publication types: `\"Review\"[Publication Type]`\n- Combine with E-utilities API for automation\n\n**Best Practices**:\n- Use MeSH Browser to find correct controlled vocabulary\n- Construct complex queries in PubMed Advanced Search Builder first\n- Include multiple synonyms with OR\n- Retrieve PMIDs for easy metadata extraction\n- Export to JSON or directly to BibTeX\n\n### Phase 2: Metadata Extraction\n\n**Goal**: Convert paper identifiers (DOI, PMID, arXiv ID) to complete, accurate metadata.\n\n#### Quick DOI to BibTeX Conversion\n\nFor single DOIs, use the quick conversion tool:\n\n```bash\n# Convert single DOI\npython scripts/doi_to_bibtex.py 10.1038/s41586-021-03819-2\n\n# Convert multiple DOIs from a file\npython scripts/doi_to_bibtex.py --input dois.txt --output references.bib\n\n# Different output formats\npython scripts/doi_to_bibtex.py 10.1038/nature12345 --format json\n```\n\n#### Comprehensive Metadata Extraction\n\nFor DOIs, PMIDs, arXiv IDs, or URLs:\n\n```bash\n# Extract from DOI\npython scripts/extract_metadata.py --doi 10.1038/s41586-021-03819-2\n\n# Extract from PMID\npython scripts/extract_metadata.py --pmid 34265844\n\n# Extract from arXiv ID\npython scripts/extract_metadata.py --arxiv 2103.14030\n\n# Extract from URL\npython scripts/extract_metadata.py --url \"https://www.nature.com/articles/s41586-021-03819-2\"\n\n# Batch extraction from file (mixed identifiers)\npython scripts/extract_metadata.py --input identifiers.txt --output citations.bib\n```\n\n**Metadata Sources** (see `references/metadata_extraction.md`):\n\n1. **CrossRef API**: Primary source for DOIs\n   - Comprehensive metadata for journal articles\n   - Publisher-provided information\n   - Includes authors, title, journal, volume, pages, dates\n   - Free, no API key required\n\n2. **PubMed E-utilities**: Biomedical literature\n   - Official NCBI metadata\n   - Includes MeSH terms, abstracts\n   - PMID and PMCID identifiers\n   - Free, API key recommended for high volume\n\n3. **arXiv API**: Preprints in physics, math, CS, q-bio\n   - Complete metadata for preprints\n   - Version tracking\n   - Author affiliations\n   - Free, open access\n\n4. **DataCite API**: Research datasets, software, other resources\n   - Metadata for non-traditional scholarly outputs\n   - DOIs for datasets and code\n   - Free access\n\n**What Gets Extracted**:\n- **Required fields**: author, title, year\n- **Journal articles**: journal, volume, number, pages, DOI\n- **Books**: publisher, ISBN, edition\n- **Conference papers**: booktitle, conference location, pages\n- **Preprints**: repository (arXiv, bioRxiv), preprint ID\n- **Additional**: abstract, keywords, URL\n\n### Phase 3: BibTeX Formatting\n\n**Goal**: Generate clean, properly formatted BibTeX entries.\n\n#### Understanding BibTeX Entry Types\n\nSee `references/bibtex_formatting.md` for complete guide.\n\n**Common Entry Types**:\n- `@article`: Journal articles (most common)\n- `@book`: Books\n- `@inproceedings`: Conference papers\n- `@incollection`: Book chapters\n- `@phdthesis`: Dissertations\n- `@misc`: Preprints, software, datasets\n\n**Required Fields by Type**:\n\n```bibtex\n@article{citationkey,\n  author  = {Last1, First1 and Last2, First2},\n  title   = {Article Title},\n  journal = {Journal Name},\n  year    = {2024},\n  volume  = {10},\n  number  = {3},\n  pages   = {123--145},\n  doi     = {10.1234/example}\n}\n\n@inproceedings{citationkey,\n  author    = {Last, First},\n  title     = {Paper Title},\n  booktitle = {Conference Name},\n  year      = {2024},\n  pages     = {1--10}\n}\n\n@book{citationkey,\n  author    = {Last, First},\n  title     = {Book Title},\n  publisher = {Publisher Name},\n  year      = {2024}\n}\n```\n\n#### Formatting and Cleaning\n\nUse the formatter to standardize BibTeX files:\n\n```bash\n# Format and clean BibTeX file\npython scripts/format_bibtex.py references.bib \\\n  --output formatted_references.bib\n\n# Sort entries by citation key\npython scripts/format_bibtex.py references.bib \\\n  --sort key \\\n  --output sorted_references.bib\n\n# Sort by year (newest first)\npython scripts/format_bibtex.py references.bib \\\n  --sort year \\\n  --descending \\\n  --output sorted_references.bib\n\n# Remove duplicates\npython scripts/format_bibtex.py references.bib \\\n  --deduplicate \\\n  --output clean_references.bib\n\n# Validate and report issues\npython scripts/format_bibtex.py references.bib \\\n  --validate \\\n  --report validation_report.txt\n```\n\n**Formatting Operations**:\n- Standardize field order\n- Consistent indentation and spacing\n- Proper capitalization in titles (protected with {})\n- Standardized author name format\n- Consistent citation key format\n- Remove unnecessary fields\n- Fix common errors (missing commas, braces)\n\n### Phase 4: Citation Validation\n\n**Goal**: Verify all citations are accurate and complete.\n\n#### Comprehensive Validation\n\n```bash\n# Validate BibTeX file\npython scripts/validate_citations.py references.bib\n\n# Validate and fix common issues\npython scripts/validate_citations.py references.bib \\\n  --auto-fix \\\n  --output validated_references.bib\n\n# Generate detailed validation report\npython scripts/validate_citations.py references.bib \\\n  --report validation_report.json \\\n  --verbose\n```\n\n**Validation Checks** (see `references/citation_validation.md`):\n\n1. **DOI Verification**:\n   - DOI resolves correctly via doi.org\n   - Metadata matches between BibTeX and CrossRef\n   - No broken or invalid DOIs\n\n2. **Required Fields**:\n   - All required fields present for entry type\n   - No empty or missing critical information\n   - Author names properly formatted\n\n3. **Data Consistency**:\n   - Year is valid (4 digits, reasonable range)\n   - Volume/number are numeric\n   - Pages formatted correctly (e.g., 123--145)\n   - URLs are accessible\n\n4. **Duplicate Detection**:\n   - Same DOI used multiple times\n   - Similar titles (possible duplicates)\n   - Same author/year/title combinations\n\n5. **Format Compliance**:\n   - Valid BibTeX syntax\n   - Proper bracing and quoting\n   - Citation keys are unique\n   - Special characters handled correctly\n\n**Validation Output**:\n```json\n{\n  \"total_entries\": 150,\n  \"valid_entries\": 145,\n  \"errors\": [\n    {\n      \"citation_key\": \"Smith2023\",\n      \"error_type\": \"missing_field\",\n      \"field\": \"journal\",\n      \"severity\": \"high\"\n    },\n    {\n      \"citation_key\": \"Jones2022\",\n      \"error_type\": \"invalid_doi\",\n      \"doi\": \"10.1234/broken\",\n      \"severity\": \"high\"\n    }\n  ],\n  \"warnings\": [\n    {\n      \"citation_key\": \"Brown2021\",\n      \"warning_type\": \"possible_duplicate\",\n      \"duplicate_of\": \"Brown2021a\",\n      \"severity\": \"medium\"\n    }\n  ]\n}\n```\n\n### Phase 5: Integration with Writing Workflow\n\n#### Building References for Manuscripts\n\nComplete workflow for creating a bibliography:\n\n```bash\n# 1. Search for papers on your topic\npython scripts/search_pubmed.py \\\n  '\"CRISPR-Cas Systems\"[MeSH] AND \"Gene Editing\"[MeSH]' \\\n  --date-start 2020 \\\n  --limit 200 \\\n  --output crispr_papers.json\n\n# 2. Extract DOIs from search results and convert to BibTeX\npython scripts/extract_metadata.py \\\n  --input crispr_papers.json \\\n  --output crispr_refs.bib\n\n# 3. Add specific papers by DOI\npython scripts/doi_to_bibtex.py 10.1038/nature12345 >> crispr_refs.bib\npython scripts/doi_to_bibtex.py 10.1126/science.abcd1234 >> crispr_refs.bib\n\n# 4. Format and clean the BibTeX file\npython scripts/format_bibtex.py crispr_refs.bib \\\n  --deduplicate \\\n  --sort year \\\n  --descending \\\n  --output references.bib\n\n# 5. Validate all citations\npython scripts/validate_citations.py references.bib \\\n  --auto-fix \\\n  --report validation.json \\\n  --output final_references.bib\n\n# 6. Review validation report and fix any remaining issues\ncat validation.json\n\n# 7. Use in your LaTeX document\n# \\bibliography{final_references}\n```\n\n#### Integration with Literature Review Skill\n\nThis skill complements the `literature-review` skill:\n\n**Literature Review Skill** → Systematic search and synthesis\n**Citation Management Skill** → Technical citation handling\n\n**Combined Workflow**:\n1. Use `literature-review` for comprehensive multi-database search\n2. Use `citation-management` to extract and validate all citations\n3. Use `literature-review` to synthesize findings thematically\n4. Use `citation-management` to verify final bibliography accuracy\n\n```bash\n# After completing literature review\n# Verify all citations in the review document\npython scripts/validate_citations.py my_review_references.bib --report review_validation.json\n\n# Format for specific citation style if needed\npython scripts/format_bibtex.py my_review_references.bib \\\n  --style nature \\\n  --output formatted_refs.bib\n```\n\n## Search Strategies\n\n### Google Scholar Best Practices\n\n**Finding Seminal and High-Impact Papers** (CRITICAL):\n\nAlways prioritize papers based on citation count, venue quality, and author reputation:\n\n**Citation Count Thresholds:**\n| Paper Age | Citations | Classification |\n|-----------|-----------|----------------|\n| 0-3 years | 20+ | Noteworthy |\n| 0-3 years | 100+ | Highly Influential |\n| 3-7 years | 100+ | Significant |\n| 3-7 years | 500+ | Landmark Paper |\n| 7+ years | 500+ | Seminal Work |\n| 7+ years | 1000+ | Foundational |\n\n**Venue Quality Tiers:**\n- **Tier 1 (Prefer):** Nature, Science, Cell, NEJM, Lancet, JAMA, PNAS\n- **Tier 2 (High Priority):** Impact Factor >10, top conferences (NeurIPS, ICML, ICLR)\n- **Tier 3 (Good):** Specialized journals (IF 5-10)\n- **Tier 4 (Sparingly):** Lower-impact peer-reviewed venues\n\n**Author Reputation Indicators:**\n- Senior researchers with h-index >40\n- Multiple publications in Tier-1 venues\n- Leadership at recognized institutions\n- Awards and editorial positions\n\n**Search Strategies for High-Impact Papers:**\n- Sort by citation count (most cited first)\n- Look for review articles from Tier-1 journals for overview\n- Check \"Cited by\" for impact assessment and recent follow-up work\n- Use citation alerts for tracking new citations to key papers\n- Filter by top venues using `source:Nature` or `source:Science`\n- Search for papers by known field leaders using `author:LastName`\n\n**Advanced Operators** (full list in `references/google_scholar_search.md`):\n```\n\"exact phrase\"           # Exact phrase matching\nauthor:lastname          # Search by author\nintitle:keyword          # Search in title only\nsource:journal           # Search specific journal\n-exclude                 # Exclude terms\nOR                       # Alternative terms\n2020..2024              # Year range\n```\n\n**Example Searches**:\n```\n# Find recent reviews on a topic\n\"CRISPR\" intitle:review 2023..2024\n\n# Find papers by specific author on topic\nauthor:Church \"synthetic biology\"\n\n# Find highly cited foundational work\n\"deep learning\" 2012..2015 sort:citations\n\n# Exclude surveys and focus on methods\n\"protein folding\" -survey -review intitle:method\n```\n\n### PubMed Best Practices\n\n**Using MeSH Terms**:\nMeSH (Medical Subject Headings) provides controlled vocabulary for precise searching.\n\n1. **Find MeSH terms** at https://meshb.nlm.nih.gov/search\n2. **Use in queries**: `\"Diabetes Mellitus, Type 2\"[MeSH]`\n3. **Combine with keywords** for comprehensive coverage\n\n**Field Tags**:\n```\n[Title]              # Search in title only\n[Title/Abstract]     # Search in title or abstract\n[Author]             # Search by author name\n[Journal]            # Search specific journal\n[Publication Date]   # Date range\n[Publication Type]   # Article type\n[MeSH]              # MeSH term\n```\n\n**Building Complex Queries**:\n```bash\n# Clinical trials on diabetes treatment published recently\n\"Diabetes Mellitus, Type 2\"[MeSH] AND \"Drug Therapy\"[MeSH] \nAND \"Clinical Trial\"[Publication Type] AND 2020:2024[Publication Date]\n\n# Reviews on CRISPR in specific journal\n\"CRISPR-Cas Systems\"[MeSH] AND \"Nature\"[Journal] AND \"Review\"[Publication Type]\n\n# Specific author's recent work\n\"Smith AB\"[Author] AND cancer[Title/Abstract] AND 2022:2024[Publication Date]\n```\n\n**E-utilities for Automation**:\nThe scripts use NCBI E-utilities API for programmatic access:\n- **ESearch**: Search and retrieve PMIDs\n- **EFetch**: Retrieve full metadata\n- **ESummary**: Get summary information\n- **ELink**: Find related articles\n\nSee `references/pubmed_search.md` for complete API documentation.\n\n## Tools and Scripts\n\n### search_google_scholar.py\n\nSearch Google Scholar and export results.\n\n**Features**:\n- Automated searching with rate limiting\n- Pagination support\n- Year range filtering\n- Export to JSON or BibTeX\n- Citation count information\n\n**Usage**:\n```bash\n# Basic search\npython scripts/search_google_scholar.py \"quantum computing\"\n\n# Advanced search with filters\npython scripts/search_google_scholar.py \"quantum computing\" \\\n  --year-start 2020 \\\n  --year-end 2024 \\\n  --limit 100 \\\n  --sort-by citations \\\n  --output quantum_papers.json\n\n# Export directly to BibTeX\npython scripts/search_google_scholar.py \"machine learning\" \\\n  --limit 50 \\\n  --format bibtex \\\n  --output ml_papers.bib\n```\n\n### search_pubmed.py\n\nSearch PubMed using E-utilities API.\n\n**Features**:\n- Complex query support (MeSH, field tags, Boolean)\n- Date range filtering\n- Publication type filtering\n- Batch retrieval with metadata\n- Export to JSON or BibTeX\n\n**Usage**:\n```bash\n# Simple keyword search\npython scripts/search_pubmed.py \"CRISPR gene editing\"\n\n# Complex query with filters\npython scripts/search_pubmed.py \\\n  --query '\"CRISPR-Cas Systems\"[MeSH] AND \"therapeutic\"[Title/Abstract]' \\\n  --date-start 2020-01-01 \\\n  --date-end 2024-12-31 \\\n  --publication-types \"Clinical Trial,Review\" \\\n  --limit 200 \\\n  --output crispr_therapeutic.json\n\n# Export to BibTeX\npython scripts/search_pubmed.py \"Alzheimer's disease\" \\\n  --limit 100 \\\n  --format bibtex \\\n  --output alzheimers.bib\n```\n\n### extract_metadata.py\n\nExtract complete metadata from paper identifiers.\n\n**Features**:\n- Supports DOI, PMID, arXiv ID, URL\n- Queries CrossRef, PubMed, arXiv APIs\n- Handles multiple identifier types\n- Batch processing\n- Multiple output formats\n\n**Usage**:\n```bash\n# Single DOI\npython scripts/extract_metadata.py --doi 10.1038/s41586-021-03819-2\n\n# Single PMID\npython scripts/extract_metadata.py --pmid 34265844\n\n# Single arXiv ID\npython scripts/extract_metadata.py --arxiv 2103.14030\n\n# From URL\npython scripts/extract_metadata.py \\\n  --url \"https://www.nature.com/articles/s41586-021-03819-2\"\n\n# Batch processing (file with one identifier per line)\npython scripts/extract_metadata.py \\\n  --input paper_ids.txt \\\n  --output references.bib\n\n# Different output formats\npython scripts/extract_metadata.py \\\n  --doi 10.1038/nature12345 \\\n  --format json  # or bibtex, yaml\n```\n\n### validate_citations.py\n\nValidate BibTeX entries for accuracy and completeness.\n\n**Features**:\n- DOI verification via doi.org and CrossRef\n- Required field checking\n- Duplicate detection\n- Format validation\n- Auto-fix common issues\n- Detailed reporting\n\n**Usage**:\n```bash\n# Basic validation\npython scripts/validate_citations.py references.bib\n\n# With auto-fix\npython scripts/validate_citations.py references.bib \\\n  --auto-fix \\\n  --output fixed_references.bib\n\n# Detailed validation report\npython scripts/validate_citations.py references.bib \\\n  --report validation_report.json \\\n  --verbose\n\n# Only check DOIs\npython scripts/validate_citations.py references.bib \\\n  --check-dois-only\n```\n\n### format_bibtex.py\n\nFormat and clean BibTeX files.\n\n**Features**:\n- Standardize formatting\n- Sort entries (by key, year, author)\n- Remove duplicates\n- Validate syntax\n- Fix common errors\n- Enforce citation key conventions\n\n**Usage**:\n```bash\n# Basic formatting\npython scripts/format_bibtex.py references.bib\n\n# Sort by year (newest first)\npython scripts/format_bibtex.py references.bib \\\n  --sort year \\\n  --descending \\\n  --output sorted_refs.bib\n\n# Remove duplicates\npython scripts/format_bibtex.py references.bib \\\n  --deduplicate \\\n  --output clean_refs.bib\n\n# Complete cleanup\npython scripts/format_bibtex.py references.bib \\\n  --deduplicate \\\n  --sort year \\\n  --validate \\\n  --auto-fix \\\n  --output final_refs.bib\n```\n\n### doi_to_bibtex.py\n\nQuick DOI to BibTeX conversion.\n\n**Features**:\n- Fast single DOI conversion\n- Batch processing\n- Multiple output formats\n- Clipboard support\n\n**Usage**:\n```bash\n# Single DOI\npython scripts/doi_to_bibtex.py 10.1038/s41586-021-03819-2\n\n# Multiple DOIs\npython scripts/doi_to_bibtex.py \\\n  10.1038/nature12345 \\\n  10.1126/science.abc1234 \\\n  10.1016/j.cell.2023.01.001\n\n# From file (one DOI per line)\npython scripts/doi_to_bibtex.py --input dois.txt --output references.bib\n\n# Copy to clipboard\npython scripts/doi_to_bibtex.py 10.1038/nature12345 --clipboard\n```\n\n## Best Practices\n\n### Search Strategy\n\n1. **Start broad, then narrow**:\n   - Begin with general terms to understand the field\n   - Refine with specific keywords and filters\n   - Use synonyms and related terms\n\n2. **Use multiple sources**:\n   - Google Scholar for comprehensive coverage\n   - PubMed for biomedical focus\n   - arXiv for preprints\n   - Combine results for completeness\n\n3. **Leverage citations**:\n   - Check \"Cited by\" for seminal papers\n   - Review references from key papers\n   - Use citation networks to discover related work\n\n4. **Document your searches**:\n   - Save search queries and dates\n   - Record number of results\n   - Note any filters or restrictions applied\n\n### Metadata Extraction\n\n1. **Always use DOIs when available**:\n   - Most reliable identifier\n   - Permanent link to the publication\n   - Best metadata source via CrossRef\n\n2. **Verify extracted metadata**:\n   - Check author names are correct\n   - Verify journal/conference names\n   - Confirm publication year\n   - Validate page numbers and volume\n\n3. **Handle edge cases**:\n   - Preprints: Include repository and ID\n   - Preprints later published: Use published version\n   - Conference papers: Include conference name and location\n   - Book chapters: Include book title and editors\n\n4. **Maintain consistency**:\n   - Use consistent author name format\n   - Standardize journal abbreviations\n   - Use same DOI format (URL preferred)\n\n### BibTeX Quality\n\n1. **Follow conventions**:\n   - Use meaningful citation keys (FirstAuthor2024keyword)\n   - Protect capitalization in titles with {}\n   - Use -- for page ranges (not single dash)\n   - Include DOI field for all modern publications\n\n2. **Keep it clean**:\n   - Remove unnecessary fields\n   - No redundant information\n   - Consistent formatting\n   - Validate syntax regularly\n\n3. **Organize systematically**:\n   - Sort by year or topic\n   - Group related papers\n   - Use separate files for different projects\n   - Merge carefully to avoid duplicates\n\n### Validation\n\n1. **Validate early and often**:\n   - Check citations when adding them\n   - Validate complete bibliography before submission\n   - Re-validate after any manual edits\n\n2. **Fix issues promptly**:\n   - Broken DOIs: Find correct identifier\n   - Missing fields: Extract from original source\n   - Duplicates: Choose best version, remove others\n   - Format errors: Use auto-fix when safe\n\n3. **Manual review for critical citations**:\n   - Verify key papers cited correctly\n   - Check author names match publication\n   - Confirm page numbers and volume\n   - Ensure URLs are current\n\n## Common Pitfalls to Avoid\n\n1. **Single source bias**: Only using Google Scholar or PubMed\n   - **Solution**: Search multiple databases for comprehensive coverage\n\n2. **Accepting metadata blindly**: Not verifying extracted information\n   - **Solution**: Spot-check extracted metadata against original sources\n\n3. **Ignoring DOI errors**: Broken or incorrect DOIs in bibliography\n   - **Solution**: Run validation before final submission\n\n4. **Inconsistent formatting**: Mixed citation key styles, formatting\n   - **Solution**: Use format_bibtex.py to standardize\n\n5. **Duplicate entries**: Same paper cited multiple times with different keys\n   - **Solution**: Use duplicate detection in validation\n\n6. **Missing required fields**: Incomplete BibTeX entries\n   - **Solution**: Validate and ensure all required fields present\n\n7. **Outdated preprints**: Citing preprint when published version exists\n   - **Solution**: Check if preprints have been published, update to journal version\n\n8. **Special character issues**: Broken LaTeX compilation due to characters\n   - **Solution**: Use proper escaping or Unicode in BibTeX\n\n9. **No validation before submission**: Submitting with citation errors\n   - **Solution**: Always run validation as final check\n\n10. **Manual BibTeX entry**: Typing entries by hand\n    - **Solution**: Always extract from metadata sources using scripts\n\n## Example Workflows\n\n### Example 1: Building a Bibliography for a Paper\n\n```bash\n# Step 1: Find key papers on your topic\npython scripts/search_google_scholar.py \"transformer neural networks\" \\\n  --year-start 2017 \\\n  --limit 50 \\\n  --output transformers_gs.json\n\npython scripts/search_pubmed.py \"deep learning medical imaging\" \\\n  --date-start 2020 \\\n  --limit 50 \\\n  --output medical_dl_pm.json\n\n# Step 2: Extract metadata from search results\npython scripts/extract_metadata.py \\\n  --input transformers_gs.json \\\n  --output transformers.bib\n\npython scripts/extract_metadata.py \\\n  --input medical_dl_pm.json \\\n  --output medical.bib\n\n# Step 3: Add specific papers you already know\npython scripts/doi_to_bibtex.py 10.1038/s41586-021-03819-2 >> specific.bib\npython scripts/doi_to_bibtex.py 10.1126/science.aam9317 >> specific.bib\n\n# Step 4: Combine all BibTeX files\ncat transformers.bib medical.bib specific.bib > combined.bib\n\n# Step 5: Format and deduplicate\npython scripts/format_bibtex.py combined.bib \\\n  --deduplicate \\\n  --sort year \\\n  --descending \\\n  --output formatted.bib\n\n# Step 6: Validate\npython scripts/validate_citations.py formatted.bib \\\n  --auto-fix \\\n  --report validation.json \\\n  --output final_references.bib\n\n# Step 7: Review any issues\ncat validation.json | grep -A 3 '\"errors\"'\n\n# Step 8: Use in LaTeX\n# \\bibliography{final_references}\n```\n\n### Example 2: Converting a List of DOIs\n\n```bash\n# You have a text file with DOIs (one per line)\n# dois.txt contains:\n# 10.1038/s41586-021-03819-2\n# 10.1126/science.aam9317\n# 10.1016/j.cell.2023.01.001\n\n# Convert all to BibTeX\npython scripts/doi_to_bibtex.py --input dois.txt --output references.bib\n\n# Validate the result\npython scripts/validate_citations.py references.bib --verbose\n```\n\n### Example 3: Cleaning an Existing BibTeX File\n\n```bash\n# You have a messy BibTeX file from various sources\n# Clean it up systematically\n\n# Step 1: Format and standardize\npython scripts/format_bibtex.py messy_references.bib \\\n  --output step1_formatted.bib\n\n# Step 2: Remove duplicates\npython scripts/format_bibtex.py step1_formatted.bib \\\n  --deduplicate \\\n  --output step2_deduplicated.bib\n\n# Step 3: Validate and auto-fix\npython scripts/validate_citations.py step2_deduplicated.bib \\\n  --auto-fix \\\n  --output step3_validated.bib\n\n# Step 4: Sort by year\npython scripts/format_bibtex.py step3_validated.bib \\\n  --sort year \\\n  --descending \\\n  --output clean_references.bib\n\n# Step 5: Final validation report\npython scripts/validate_citations.py clean_references.bib \\\n  --report final_validation.json \\\n  --verbose\n\n# Review report\ncat final_validation.json\n```\n\n### Example 4: Finding and Citing Seminal Papers\n\n```bash\n# Find highly cited papers on a topic\npython scripts/search_google_scholar.py \"AlphaFold protein structure\" \\\n  --year-start 2020 \\\n  --year-end 2024 \\\n  --sort-by citations \\\n  --limit 20 \\\n  --output alphafold_seminal.json\n\n# Extract the top 10 by citation count\n# (script will have included citation counts in JSON)\n\n# Convert to BibTeX\npython scripts/extract_metadata.py \\\n  --input alphafold_seminal.json \\\n  --output alphafold_refs.bib\n\n# The BibTeX file now contains the most influential papers\n```\n\n## Integration with Other Skills\n\n### Literature Review Skill\n\n**Citation Management** provides the technical infrastructure for **Literature Review**:\n\n- **Literature Review**: Multi-database systematic search and synthesis\n- **Citation Management**: Metadata extraction and validation\n\n**Combined workflow**:\n1. Use literature-review for systematic search methodology\n2. Use citation-management to extract and validate citations\n3. Use literature-review to synthesize findings\n4. Use citation-management to ensure bibliography accuracy\n\n### Scientific Writing Skill\n\n**Citation Management** ensures accurate references for **Scientific Writing**:\n\n- Export validated BibTeX for use in LaTeX manuscripts\n- Verify citations match publication standards\n- Format references according to journal requirements\n\n### Venue Templates Skill\n\n**Citation Management** works with **Venue Templates** for submission-ready manuscripts:\n\n- Different venues require different citation styles\n- Generate properly formatted references\n- Validate citations meet venue requirements\n\n## Resources\n\n### Bundled Resources\n\n**References** (in `references/`):\n- `google_scholar_search.md`: Complete Google Scholar search guide\n- `pubmed_search.md`: PubMed and E-utilities API documentation\n- `metadata_extraction.md`: Metadata sources and field requirements\n- `citation_validation.md`: Validation criteria and quality checks\n- `bibtex_formatting.md`: BibTeX entry types and formatting rules\n\n**Scripts** (in `scripts/`):\n- `search_google_scholar.py`: Google Scholar search automation\n- `search_pubmed.py`: PubMed E-utilities API client\n- `extract_metadata.py`: Universal metadata extractor\n- `validate_citations.py`: Citation validation and verification\n- `format_bibtex.py`: BibTeX formatter and cleaner\n- `doi_to_bibtex.py`: Quick DOI to BibTeX converter\n\n**Assets** (in `assets/`):\n- `bibtex_template.bib`: Example BibTeX entries for all types\n- `citation_checklist.md`: Quality assurance checklist\n\n### External Resources\n\n**Search Engines**:\n- Google Scholar: https://scholar.google.com/\n- PubMed: https://pubmed.ncbi.nlm.nih.gov/\n- PubMed Advanced Search: https://pubmed.ncbi.nlm.nih.gov/advanced/\n\n**Metadata APIs**:\n- CrossRef API: https://api.crossref.org/\n- PubMed E-utilities: https://www.ncbi.nlm.nih.gov/books/NBK25501/\n- arXiv API: https://arxiv.org/help/api/\n- DataCite API: https://api.datacite.org/\n\n**Tools and Validators**:\n- MeSH Browser: https://meshb.nlm.nih.gov/search\n- DOI Resolver: https://doi.org/\n- BibTeX Format: http://www.bibtex.org/Format/\n\n**Citation Styles**:\n- BibTeX documentation: http://www.bibtex.org/\n- LaTeX bibliography management: https://www.overleaf.com/learn/latex/Bibliography_management\n\n## Dependencies\n\n### Required Python Packages\n\n```bash\n# Core dependencies\npip install requests  # HTTP requests for APIs\npip install bibtexparser  # BibTeX parsing and formatting\npip install biopython  # PubMed E-utilities access\n\n# Optional (for Google Scholar)\npip install scholarly  # Google Scholar API wrapper\n# or\npip install selenium  # For more robust Scholar scraping\n```\n\n### Optional Tools\n\n```bash\n# For advanced validation\npip install crossref-commons  # Enhanced CrossRef API access\npip install pylatexenc  # LaTeX special character handling\n```\n\n## Summary\n\nThe citation-management skill provides:\n\n1. **Comprehensive search capabilities** for Google Scholar and PubMed\n2. **Automated metadata extraction** from DOI, PMID, arXiv ID, URLs\n3. **Citation validation** with DOI verification and completeness checking\n4. **BibTeX formatting** with standardization and cleaning tools\n5. **Quality assurance** through validation and reporting\n6. **Integration** with scientific writing workflow\n7. **Reproducibility** through documented search and extraction methods\n\nUse this skill to maintain accurate, complete citations throughout your research and ensure publication-ready bibliographies.\n\n## Limitations\n- Use this skill only when the task clearly matches the scope described above.\n- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.\n- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are 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