citation-management
Manage citations systematically throughout the research and writing process.
- License 许可
- MIT
- 最近更新
- 2026-10-25
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01 前言 Front Matter
- name
- citation-management
- risk
- critical
- source
- community
- license
- MIT License
- metadata
- { "skill-author": "K-Dense Inc." }
- date_added
- 2026-09-04
- description
- Manage citations systematically throughout the research and writing process.
02 资源文件 1
- 参考文档references/detailed-guide.md27.9 KB
03 SKILL.md 正文
Citation Management
Detailed Guide
Read the detailed guide before executing this skill. It retains the complete procedure and reference material. Treat its safety, prerequisites, and validation requirements as mandatory. For focused work, load the relevant sections; for end-to-end work, read the guide completely.
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
Example Workflows
Example 1: Building a Bibliography for a Paper
# Step 1: Find key papers on your topic
python scripts/search_google_scholar.py "transformer neural networks" \
--year-start 2017 \
--limit 50 \
--output transformers_gs.json
python scripts/search_pubmed.py "deep learning medical imaging" \
--date-start 2020 \
--limit 50 \
--output medical_dl_pm.json
# Step 2: Extract metadata from search results
python scripts/extract_metadata.py \
--input transformers_gs.json \
--output transformers.bib
python scripts/extract_metadata.py \
--input medical_dl_pm.json \
--output medical.bib
# Step 3: Add specific papers you already know
python scripts/doi_to_bibtex.py 10.1038/s41586-021-03819-2 >> specific.bib
python scripts/doi_to_bibtex.py 10.1126/science.aam9317 >> specific.bib
# Step 4: Combine all BibTeX files
cat transformers.bib medical.bib specific.bib > combined.bib
# Step 5: Format and deduplicate
python scripts/format_bibtex.py combined.bib \
--deduplicate \
--sort year \
--descending \
--output formatted.bib
# Step 6: Validate
python scripts/validate_citations.py formatted.bib \
--auto-fix \
--report validation.json \
--output final_references.bib
# Step 7: Review any issues
cat validation.json | grep -A 3 '"errors"'
# Step 8: Use in LaTeX
# \bibliography{final_references}
Example 2: Converting a List of DOIs
# You have a text file with DOIs (one per line)
# dois.txt contains:
# 10.1038/s41586-021-03819-2
# 10.1126/science.aam9317
# 10.1016/j.cell.2023.01.001
# Convert all to BibTeX
python scripts/doi_to_bibtex.py --input dois.txt --output references.bib
# Validate the result
python scripts/validate_citations.py references.bib --verbose
Example 3: Cleaning an Existing BibTeX File
# You have a messy BibTeX file from various sources
# Clean it up systematically
# Step 1: Format and standardize
python scripts/format_bibtex.py messy_references.bib \
--output step1_formatted.bib
# Step 2: Remove duplicates
python scripts/format_bibtex.py step1_formatted.bib \
--deduplicate \
--output step2_deduplicated.bib
# Step 3: Validate and auto-fix
python scripts/validate_citations.py step2_deduplicated.bib \
--auto-fix \
--output step3_validated.bib
# Step 4: Sort by year
python scripts/format_bibtex.py step3_validated.bib \
--sort year \
--descending \
--output clean_references.bib
# Step 5: Final validation report
python scripts/validate_citations.py clean_references.bib \
--report final_validation.json \
--verbose
# Review report
cat final_validation.json
Example 4: Finding and Citing Seminal Papers
# Find highly cited papers on a topic
python scripts/search_google_scholar.py "AlphaFold protein structure" \
--year-start 2020 \
--year-end 2024 \
--sort-by citations \
--limit 20 \
--output alphafold_seminal.json
# Extract the top 10 by citation count
# (script will have included citation counts in JSON)
# Convert to BibTeX
python scripts/extract_metadata.py \
--input alphafold_seminal.json \
--output alphafold_refs.bib
# The BibTeX file now contains the most influential papers
Limitations
- Use this skill only when the task clearly matches the scope described above.
- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.
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