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Get Started Free →Manage citations systematically throughout the research and writing process.
.claude/skills/sickn33-citation-management/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | 121% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 335% | 0% |
| case-01 | ✗→✓ | ▲ Improved | 363% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 403% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 258% | 0% |
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.
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bash# 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}
bash# 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
bash# 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
bash# 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
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-04 | pass→pass | 13,364 | 28,454 | +113% | 1 | 1 | 0% | 2,161 | 13,314 | +516% | 0 | 0 | — |
case-02 | fail→pass | 21,625 | 5,957 | -72% | 1 | 1 | 0% | 4,466 | 9,852 | +121% | 0 | 0 | — |
case-03 | fail→pass | 11,338 | 8,643 | -24% | 1 | 1 | 0% | 2,429 | 10,561 | +335% | 0 | 0 | — |
case-01 | fail→pass | 11,385 | 7,601 | -33% | 1 | 1 | 0% | 2,216 | 10,250 | +363% | 0 | 0 | — |
case-05 | fail→fail | 4,232 | 7,763 | +83% | 1 | 1 | 0% | 795 | 10,007 | +1159% | 0 | 0 | — |
case-06 | pass→pass | 12,611 | 11,340 | -10% | 1 | 1 | 0% | 2,722 | 10,766 | +296% | 0 | 0 | — |
case-07 | fail→pass | 8,082 | 2,933 | -64% | 1 | 1 | 0% | 1,818 | 9,153 | +403% | 0 | 0 | — |
case-08 | fail→pass | 13,510 | 2,776 | -79% | 1 | 1 | 0% | 2,560 | 9,172 | +258% | 0 | 0 | — |
case-09 | fail→pass | 15,912 | 1,874 | -88% | 1 | 1 | 0% | 3,263 | 8,950 | +174% | 0 | 0 | — |
case-10 | fail→pass | 13,055 | 4,748 | -64% | 1 | 1 | 0% | 2,968 | 9,638 | +225% | 0 | 0 | — |
case-11 | fail→pass | 9,262 | 2,647 | -71% | 1 | 1 | 0% | 1,825 | 9,088 | +398% | 0 | 0 | — |
case-12 | fail→pass | 10,968 | 2,473 | -77% | 1 | 1 | 0% | 2,168 | 8,957 | +313% | 0 | 0 | — |
case-13 | pass→pass | 9,473 | 2,774 | -71% | 1 | 1 | 0% | 1,717 | 9,077 | +429% | 0 | 0 | — |
case-14 | pass→pass | 9,144 | 2,317 | -75% | 1 | 1 | 0% | 1,593 | 8,994 | +465% | 0 | 0 | — |
case-15 | pass→pass | 9,320 | 1,699 | -82% | 1 | 1 | 0% | 1,714 | 8,829 | +415% | 0 | 0 | — |
case-16 | fail→pass | 10,866 | 3,078 | -72% | 1 | 1 | 0% | 1,925 | 9,141 | +375% | 0 | 0 | — |
case-17 | fail→pass | 9,422 | 2,715 | -71% | 1 | 1 | 0% | 1,794 | 9,100 | +407% | 0 | 0 | — |
case-18 | fail→pass | 5,795 | 2,432 | -58% | 1 | 1 | 0% | 1,127 | 9,046 | +703% | 0 | 0 | — |
case-19 | pass→pass | 12,551 | 11,203 | -11% | 1 | 1 | 0% | 2,232 | 10,477 | +369% | 0 | 0 | — |
case-20 | pass→pass | 5,114 | 4,611 | -10% | 1 | 1 | 0% | 996 | 9,433 | +847% | 0 | 0 | — |
case-21 | pass→pass | 4,828 | 5,152 | +7% | 1 | 1 | 0% | 939 | 9,558 | +918% | 0 | 0 | — |
case-22 | fail→pass | 12,976 | 2,999 | -77% | 1 | 1 | 0% | 2,727 | 9,130 | +235% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted. The headline lift of +59 percentage points is the difference between those two pass rates over the 22 comparable cases.
The publisher has shipped newer versions since this run, so these numbers describe v1, not the version currently listed.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.