--- 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**: ```bash # 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**: ```bash # 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:

```bash # 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:

```bash # 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**: 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 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**:

```bibtex @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:

```bash # Format and clean BibTeX file python scripts/format_bibtex.py references.bib \ --output formatted_references.bib

# Sort entries by citation key python scripts/format_bibtex.py references.bib \ --sort key \ --output sorted_references.bib

# Sort by year (newest first) python scripts/format_bibtex.py references.bib \ --sort year \ --descending \ --output sorted_references.bib

# Remove duplicates python scripts/format_bibtex.py references.bib \ --deduplicate \ --output clean_references.bib

# Validate and report issues python scripts/format_bibtex.py references.bib \ --validate \ --report validation_report.txt ```

**Formatting Operations**: - Standardize field order - Consistent indentation and spacing - Proper capitalization in titles (protected with {}) - Standardized author name format - Consistent citation key format - Remove unnecessary fields - Fix common errors (missing commas, braces)

### Phase 4: Citation Validation

**Goal**: Verify all citations are accurate and complete.

#### Comprehensive Validation

```bash # Validate BibTeX file python scripts/validate_citations.py references.bib

# Validate and fix common issues python scripts/validate_citations.py references.bib \ --auto-fix \ --output validated_references.bib

# Generate detailed validation report python scripts/validate_citations.py references.bib \ --report validation_report.json \ --verbose ```

**Validation Checks** (see `references/citation_validation.md`):

1. **DOI Verification**: - DOI resolves correctly via doi.org - Metadata matches between BibTeX and CrossRef - No broken or invalid DOIs

2. **Required Fields**: - All required fields present for entry type - No empty or missing critical information - Author names properly formatted

3. **Data Consistency**: - Year is valid (4 digits, reasonable range) - Volume/number are numeric - Pages formatted correctly (e.g., 123--145) - URLs are accessible

4. **Duplicate Detection**: - Same DOI used multiple times - Similar titles (possible duplicates) - Same author/year/title combinations

5. **Format Compliance**: - Valid BibTeX syntax - Proper bracing and quoting - Citation keys are unique - Special characters handled correctly

**Validation Output**: ```json { "total_entries": 150, "valid_entries": 145, "errors": [ { "citation_key": "Smith2023", "error_type": "missing_field", "field": "journal", "severity": "high" }, { "citation_key": "Jones2022", "error_type": "invalid_doi", "doi": "10.1234/broken", "severity": "high" } ], "warnings": [ { "citation_key": "Brown2021", "warning_type": "possible_duplicate", "duplicate_of": "Brown2021a", "severity": "medium" } ] } ```

### Phase 5: Integration with Writing Workflow

#### Building References for Manuscripts

Complete workflow for creating a bibliography:

```bash # 1. Search for papers on your topic python scripts/search_pubmed.py \ '"CRISPR-Cas Systems"[MeSH] AND "Gene Editing"[MeSH]' \ --date-start 2020 \ --limit 200 \ --output crispr_papers.json

# 2. Extract DOIs from search results and convert to BibTeX python scripts/extract_metadata.py \ --input crispr_papers.json \ --output crispr_refs.bib

# 3. Add specific papers by DOI python scripts/doi_to_bibtex.py 10.1038/nature12345 >> crispr_refs.bib python scripts/doi_to_bibtex.py 10.1126/science.abcd1234 >> crispr_refs.bib

# 4. Format and clean the BibTeX file python scripts/format_bibtex.py crispr_refs.bib \ --deduplicate \ --sort year \ --descending \ --output references.bib

# 5. Validate all citations python scripts/validate_citations.py references.bib \ --auto-fix \ --report validation.json \ --output final_references.bib

# 6. Review validation report and fix any remaining issues cat validation.json

# 7. Use in your LaTeX document # \bibliography{final_references} ```

#### Citation Validation In Review Documents

When a review document already exists, use this skill for the technical citation layer:

1. Extract all cited identifiers and bibliography records 2. Validate metadata against DOI, PMID, arXiv, CrossRef, or PubMed sources 3. Normalize BibTeX keys and citation style fields 4. Verify the final bibliography before manuscript build or submission

```bash # After completing literature review # Verify all citations in the review document python scripts/validate_citations.py my_review_references.bib --report review_validation.json

# Format for specific citation style if needed python scripts/format_bibtex.py my_review_references.bib \ --style nature

--- 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**: ```bash # 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**: ```bash # 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:

```bash # 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:

```bash # 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**: 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 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**:

```bibtex @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:

```bash # Format and clean BibTeX file python scripts/format_bibtex.py references.bib \ --output formatted_references.bib

# Sort entries by citation key python scripts/format_bibtex.py references.bib \ --sort key \ --output sorted_references.bib

# Sort by year (newest first) python scripts/format_bibtex.py references.bib \ --sort year \ --descending \ --output sorted_references.bib

# Remove duplicates python scripts/format_bibtex.py references.bib \ --deduplicate \ --output clean_references.bib

# Validate and report issues python scripts/format_bibtex.py references.bib \ --validate \ --report validation_report.txt ```

**Formatting Operations**: - Standardize field order - Consistent indentation and spacing - Proper capitalization in titles (protected with {}) - Standardized author name format - Consistent citation key format - Remove unnecessary fields - Fix common errors (missing commas, braces)

### Phase 4: Citation Validation

**Goal**: Verify all citations are accurate and complete.

#### Comprehensive Validation

```bash # Validate BibTeX file python scripts/validate_citations.py references.bib

# Validate and fix common issues python scripts/validate_citations.py references.bib \ --auto-fix \ --output validated_references.bib

# Generate detailed validation report python scripts/validate_citations.py references.bib \ --report validation_report.json \ --verbose ```

**Validation Checks** (see `references/citation_validation.md`):

1. **DOI Verification**: - DOI resolves correctly via doi.org - Metadata matches between BibTeX and CrossRef - No broken or invalid DOIs

2. **Required Fields**: - All required fields present for entry type - No empty or missing critical information - Author names properly formatted

3. **Data Consistency**: - Year is valid (4 digits, reasonable range) - Volume/number are numeric - Pages formatted correctly (e.g., 123--145) - URLs are accessible

4. **Duplicate Detection**: - Same DOI used multiple times - Similar titles (possible duplicates) - Same author/year/title combinations

5. **Format Compliance**: - Valid BibTeX syntax - Proper bracing and quoting - Citation keys are unique - Special characters handled correctly

**Validation Output**: ```json { "total_entries": 150, "valid_entries": 145, "errors": [ { "citation_key": "Smith2023", "error_type": "missing_field", "field": "journal", "severity": "high" }, { "citation_key": "Jones2022", "error_type": "invalid_doi", "doi": "10.1234/broken", "severity": "high" } ], "warnings": [ { "citation_key": "Brown2021", "warning_type": "possible_duplicate", "duplicate_of": "Brown2021a", "severity": "medium" } ] } ```

### Phase 5: Integration with Writing Workflow

#### Building References for Manuscripts

Complete workflow for creating a bibliography:

```bash # 1. Search for papers on your topic python scripts/search_pubmed.py \ '"CRISPR-Cas Systems"[MeSH] AND "Gene Editing"[MeSH]' \ --date-start 2020 \ --limit 200 \ --output crispr_papers.json

# 2. Extract DOIs from search results and convert to BibTeX python scripts/extract_metadata.py \ --input crispr_papers.json \ --output crispr_refs.bib

# 3. Add specific papers by DOI python scripts/doi_to_bibtex.py 10.1038/nature12345 >> crispr_refs.bib python scripts/doi_to_bibtex.py 10.1126/science.abcd1234 >> crispr_refs.bib

# 4. Format and clean the BibTeX file python scripts/format_bibtex.py crispr_refs.bib \ --deduplicate \ --sort year \ --descending \ --output references.bib

# 5. Validate all citations python scripts/validate_citations.py references.bib \ --auto-fix \ --report validation.json \ --output final_references.bib

# 6. Review validation report and fix any remaining issues cat validation.json

# 7. Use in your LaTeX document # \bibliography{final_references} ```

#### Citation Validation In Review Documents

When a review document already exists, use this skill for the technical citation layer:

1. Extract all cited identifiers and bibliography records 2. Validate metadata against DOI, PMID, arXiv, CrossRef, or PubMed sources 3. Normalize BibTeX keys and citation style fields 4. Verify the final bibliography before manuscript build or submission

```bash # After completing literature review # Verify all citations in the review document python scripts/validate_citations.py my_review_references.bib --report review_validation.json

# Format for specific citation style if needed python scripts/format_bibtex.py my_review_references.bib \ --style nature

--- 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**: ```bash # 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**: ```bash # 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:

```bash # 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:

```bash # 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**: 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 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**:

```bibtex @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:

```bash # Format and clean BibTeX file python scripts/format_bibtex.py references.bib \ --output formatted_references.bib

# Sort entries by citation key python scripts/format_bibtex.py references.bib \ --sort key \ --output sorted_references.bib

# Sort by year (newest first) python scripts/format_bibtex.py references.bib \ --sort year \ --descending \ --output sorted_references.bib

# Remove duplicates python scripts/format_bibtex.py references.bib \ --deduplicate \ --output clean_references.bib

# Validate and report issues python scripts/format_bibtex.py references.bib \ --validate \ --report validation_report.txt ```

**Formatting Operations**: - Standardize field order - Consistent indentation and spacing - Proper capitalization in titles (protected with {}) - Standardized author name format - Consistent citation key format - Remove unnecessary fields - Fix common errors (missing commas, braces)

### Phase 4: Citation Validation

**Goal**: Verify all citations are accurate and complete.

#### Comprehensive Validation

```bash # Validate BibTeX file python scripts/validate_citations.py references.bib

# Validate and fix common issues python scripts/validate_citations.py references.bib \ --auto-fix \ --output validated_references.bib

# Generate detailed validation report python scripts/validate_citations.py references.bib \ --report validation_report.json \ --verbose ```

**Validation Checks** (see `references/citation_validation.md`):

1. **DOI Verification**: - DOI resolves correctly via doi.org - Metadata matches between BibTeX and CrossRef - No broken or invalid DOIs

2. **Required Fields**: - All required fields present for entry type - No empty or missing critical information - Author names properly formatted

3. **Data Consistency**: - Year is valid (4 digits, reasonable range) - Volume/number are numeric - Pages formatted correctly (e.g., 123--145) - URLs are accessible

4. **Duplicate Detection**: - Same DOI used multiple times - Similar titles (possible duplicates) - Same author/year/title combinations

5. **Format Compliance**: - Valid BibTeX syntax - Proper bracing and quoting - Citation keys are unique - Special characters handled correctly

**Validation Output**: ```json { "total_entries": 150, "valid_entries": 145, "errors": [ { "citation_key": "Smith2023", "error_type": "missing_field", "field": "journal", "severity": "high" }, { "citation_key": "Jones2022", "error_type": "invalid_doi", "doi": "10.1234/broken", "severity": "high" } ], "warnings": [ { "citation_key": "Brown2021", "warning_type": "possible_duplicate", "duplicate_of": "Brown2021a", "severity": "medium" } ] } ```

### Phase 5: Integration with Writing Workflow

#### Building References for Manuscripts

Complete workflow for creating a bibliography:

```bash # 1. Search for papers on your topic python scripts/search_pubmed.py \ '"CRISPR-Cas Systems"[MeSH] AND "Gene Editing"[MeSH]' \ --date-start 2020 \ --limit 200 \ --output crispr_papers.json

# 2. Extract DOIs from search results and convert to BibTeX python scripts/extract_metadata.py \ --input crispr_papers.json \ --output crispr_refs.bib

# 3. Add specific papers by DOI python scripts/doi_to_bibtex.py 10.1038/nature12345 >> crispr_refs.bib python scripts/doi_to_bibtex.py 10.1126/science.abcd1234 >> crispr_refs.bib

# 4. Format and clean the BibTeX file python scripts/format_bibtex.py crispr_refs.bib \ --deduplicate \ --sort year \ --descending \ --output references.bib

# 5. Validate all citations python scripts/validate_citations.py references.bib \ --auto-fix \ --report validation.json \ --output final_references.bib

# 6. Review validation report and fix any remaining issues cat validation.json

# 7. Use in your LaTeX document # \bibliography{final_references} ```

#### Citation Validation In Review Documents

When a review document already exists, use this skill for the technical citation layer:

1. Extract all cited identifiers and bibliography records 2. Validate metadata against DOI, PMID, arXiv, CrossRef, or PubMed sources 3. Normalize BibTeX keys and citation style fields 4. Verify the final bibliography before manuscript build or submission

```bash # After completing literature review # Verify all citations in the review document python scripts/validate_citations.py my_review_references.bib --report review_validation.json

# Format for specific citation style if needed python scripts/format_bibtex.py my_review_references.bib \ --style nature

--- 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**: ```bash # 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**: ```bash # 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:

```bash # 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:

```bash # 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**: 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 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**:

```bibtex @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:

```bash # Format and clean BibTeX file python scripts/format_bibtex.py references.bib \ --output formatted_references.bib

# Sort entries by citation key python scripts/format_bibtex.py references.bib \ --sort key \ --output sorted_references.bib

# Sort by year (newest first) python scripts/format_bibtex.py references.bib \ --sort year \ --descending \ --output sorted_references.bib

# Remove duplicates python scripts/format_bibtex.py references.bib \ --deduplicate \ --output clean_references.bib

# Validate and report issues python scripts/format_bibtex.py references.bib \ --validate \ --report validation_report.txt ```

**Formatting Operations**: - Standardize field order - Consistent indentation and spacing - Proper capitalization in titles (protected with {}) - Standardized author name format - Consistent citation key format - Remove unnecessary fields - Fix common errors (missing commas, braces)

### Phase 4: Citation Validation

**Goal**: Verify all citations are accurate and complete.

#### Comprehensive Validation

```bash # Validate BibTeX file python scripts/validate_citations.py references.bib

# Validate and fix common issues python scripts/validate_citations.py references.bib \ --auto-fix \ --output validated_references.bib

# Generate detailed validation report python scripts/validate_citations.py references.bib \ --report validation_report.json \ --verbose ```

**Validation Checks** (see `references/citation_validation.md`):

1. **DOI Verification**: - DOI resolves correctly via doi.org - Metadata matches between BibTeX and CrossRef - No broken or invalid DOIs

2. **Required Fields**: - All required fields present for entry type - No empty or missing critical information - Author names properly formatted

3. **Data Consistency**: - Year is valid (4 digits, reasonable range) - Volume/number are numeric - Pages formatted correctly (e.g., 123--145) - URLs are accessible

4. **Duplicate Detection**: - Same DOI used multiple times - Similar titles (possible duplicates) - Same author/year/title combinations

5. **Format Compliance**: - Valid BibTeX syntax - Proper bracing and quoting - Citation keys are unique - Special characters handled correctly

**Validation Output**: ```json { "total_entries": 150, "valid_entries": 145, "errors": [ { "citation_key": "Smith2023", "error_type": "missing_field", "field": "journal", "severity": "high" }, { "citation_key": "Jones2022", "error_type": "invalid_doi", "doi": "10.1234/broken", "severity": "high" } ], "warnings": [ { "citation_key": "Brown2021", "warning_type": "possible_duplicate", "duplicate_of": "Brown2021a", "severity": "medium" } ] } ```

### Phase 5: Integration with Writing Workflow

#### Building References for Manuscripts

Complete workflow for creating a bibliography:

```bash # 1. Search for papers on your topic python scripts/search_pubmed.py \ '"CRISPR-Cas Systems"[MeSH] AND "Gene Editing"[MeSH]' \ --date-start 2020 \ --limit 200 \ --output crispr_papers.json

# 2. Extract DOIs from search results and convert to BibTeX python scripts/extract_metadata.py \ --input crispr_papers.json \ --output crispr_refs.bib

# 3. Add specific papers by DOI python scripts/doi_to_bibtex.py 10.1038/nature12345 >> crispr_refs.bib python scripts/doi_to_bibtex.py 10.1126/science.abcd1234 >> crispr_refs.bib

# 4. Format and clean the BibTeX file python scripts/format_bibtex.py crispr_refs.bib \ --deduplicate \ --sort year \ --descending \ --output references.bib

# 5. Validate all citations python scripts/validate_citations.py references.bib \ --auto-fix \ --report validation.json \ --output final_references.bib

# 6. Review validation report and fix any remaining issues cat validation.json

# 7. Use in your LaTeX document # \bibliography{final_references} ```

#### Citation Validation In Review Documents

When a review document already exists, use this skill for the technical citation layer:

1. Extract all cited identifiers and bibliography records 2. Validate metadata against DOI, PMID, arXiv, CrossRef, or PubMed sources 3. Normalize BibTeX keys and citation style fields 4. Verify the final bibliography before manuscript build or submission

```bash # After completing literature review # Verify all citations in the review document python scripts/validate_citations.py my_review_references.bib --report review_validation.json

# Format for specific citation style if needed python scripts/format_bibtex.py my_review_references.bib \ --style nature