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Get Started Free →Conduct comprehensive, systematic literature reviews using multiple academic databases (PubMed, arXiv, bioRxiv, Semantic Scholar, etc.). This skill should be used when conducting systematic literature reviews, meta-analyses, research synthesis, or comprehensive literature searches across biomedical, scientific, and technical domains. Creates professionally formatted markdown documents and PDFs with verified citations in multiple citation styles (APA, Nature, Vancouver, etc.).
.claude/skills/foryourhealth111-pixel-literature-review/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | -1% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 243% | 0% |
| case-15 | ✗→✓ | ▲ Improved | 352% | 0% |
| case-03 | ✓→✓ | = Same ✓ | 133% | 0% |
| case-04 | ✓→✓ | = Same ✓ | 242% | 0% |
Conduct systematic, comprehensive literature reviews following rigorous academic methodology. Search multiple literature sources, synthesize findings thematically, verify citations for accuracy, and generate professional output documents in markdown and PDF formats.
Use this skill when:
Literature reviews follow a structured, multi-phase workflow:
Select databases appropriate for the domain:
Biomedical & Life Sciences:
General Scientific Literature:
Specialized Databases:
markdown ## Search Strategy
### Database: PubMed
("CRISPR"[Title] OR "Cas9"[Title]) AND ("sickle cell"[MeSH] OR "SCD"[Title/Abstract]) AND 2015:2024[Publication Date]
Repeat for each database searched.
scripts/search_databases.py for post-processing:bash python search_databases.py combined_results.json \ --deduplicate \ --format markdown \ --output aggregated_results.md
bash python search_databases.py results.json --deduplicate --output unique_results.json
Initial search: n = X ├─ After deduplication: n = Y ├─ After title screening: n = Z ├─ After abstract screening: n = A └─ Included in review: n = B
bash cp assets/review_template.md my_literature_review.md
Example structure: markdown #### 3.3.1 Theme: CRISPR Delivery Methods
Multiple delivery approaches have been investigated for therapeutic gene editing. Viral vectors (AAV) were used in 15 studies^1-15^ and showed high transduction efficiency (65-85%) but raised immunogenicity concerns^3,7,12^. In contrast, lipid nanoparticles demonstrated lower efficiency (40-60%) but improved safety profiles^16-23^.
CRITICAL: All citations must be verified for accuracy before final submission.
bash python scripts/verify_citations.py my_literature_review.md
This script:
references/citation_styles.md)bash python scripts/generate_pdf.py my_literature_review.md \ --citation-style apa \ --output my_review.pdf
Options:
--citation-style: apa, nature, chicago, vancouver, ieee--no-toc: Disable table of contents--no-numbers: Disable section numbering--check-deps: Check if pandoc/xelatex are installedAccess via PubMed, PMC, or Entrez-compatible APIs:
bash# Search PubMed pubmed search "CRISPR gene editing" --limit 100 # Search with filters # Use PubMed Advanced Search Builder to construct complex queries # Then execute via PubMed/Entrez-compatible access
Search tips:
"sickle cell disease"[MeSH][Title], [Title/Abstract], [Author]2020:2024[Publication Date]Use preprint servers as literature sources inside the review workflow:
bashsearch biorxiv "CRISPR sickle cell" --limit 50
Important considerations:
Access via direct API or WebFetch:
python# Example search categories: # q-bio.QM (Quantitative Methods) # q-bio.GN (Genomics) # q-bio.MN (Molecular Networks) # cs.LG (Machine Learning) # stat.ML (Machine Learning Statistics) # Search format: category AND terms search_query = "cat:q-bio.QM AND ti:\"single cell sequencing\""
Access via direct API (requires API key, or use free tier):
Keep literature-review focused on paper search, screening, synthesis, and evidence extraction. Biomedical database lookup is a separate problem surface; use this section only to record which external evidence source a paper cites or which database a review protocol should query.
Expand search via citation networks:
Detailed formatting guidelines are in references/citation_styles.md. Quick reference:
Always verify citations with verify_citations.py before finalizing.
Always prioritize influential, highly-cited papers from reputable authors and top venues. Quality matters more than quantity in literature reviews.
Use citation counts to identify the most impactful papers:
| Paper Age | Citation Threshold | Classification | |-----------|-------------------|----------------| | 0-3 years | 20+ citations | Noteworthy | | 0-3 years | 100+ citations | Highly Influential | | 3-7 years | 100+ citations | Significant | | 3-7 years | 500+ citations | Landmark Paper | | 7+ years | 500+ citations | Seminal Work | | 7+ years | 1000+ citations | Foundational |
Prioritize papers from higher-tier venues:
Prefer papers from:
For any topic, identify foundational work by:
Complete workflow for a biomedical literature review:
bash# 1. Create review document from template cp assets/review_template.md crispr_sickle_cell_review.md # 2. Search multiple literature sources # - Use PubMed for curated biomedical literature # - Include bioRxiv/medRxiv preprints when recent non-peer-reviewed evidence matters # - Use direct API access for arXiv, Semantic Scholar # - Export results in JSON format # 3. Aggregate and process results python scripts/search_databases.py combined_results.json \ --deduplicate \ --rank citations \ --year-start 2015 \ --year-end 2024 \ --format markdown \ --output search_results.md \ --summary # 4. Screen results and extract data # - Manually screen titles, abstracts, full texts # - Extract key data into the review document # - Organize by themes # 5. Write the review following template structure # - Introduction with clear objectives # - Detailed methodology section # - Results organized thematically # - Critical discussion # - Clear conclusions # 6. Verify all citations python scripts/verify_citations.py crispr_sickle_cell_review.md # Review the citation report cat crispr_sickle_cell_review_citation_report.json # Fix any failed citations and re-verify python scripts/verify_citations.py crispr_sickle_cell_review.md # 7. Generate professional PDF python scripts/generate_pdf.py crispr_sickle_cell_review.md \ --citation-style nature \ --output crispr_sickle_cell_review.pdf # 8. Review final PDF and markdown outputs
Scripts:
scripts/verify_citations.py: Verify DOIs and generate formatted citationsscripts/generate_pdf.py: Convert markdown to professional PDFscripts/search_databases.py: Process, deduplicate, and format search resultsReferences:
references/citation_styles.md: Detailed citation formatting guide (APA, Nature, Vancouver, Chicago, IEEE)references/database_strategies.md: Comprehensive database search strategiesAssets:
assets/review_template.md: Complete literature review template with all sectionsGuidelines:
Tools:
Citation Styles:
bashpip install requests # For citation verification
bash# For PDF generation brew install pandoc # macOS apt-get install pandoc # Linux # For LaTeX (PDF generation) brew install --cask mactex # macOS apt-get install texlive-xetex # Linux
Check dependencies:
bashpython scripts/generate_pdf.py --check-deps
This literature-review skill provides:
Conduct thorough, rigorous literature reviews that meet academic standards and provide comprehensive synthesis of current knowledge in any domain.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 15,732 | 32,025 | +104% | 1 | 1 | 0% | 3,230 | 11,342 | +251% | 0 | 0 | — |
case-02 | fail→pass | 30,240 | 5,495 | -82% | 1 | 1 | 0% | 6,219 | 6,185 | -1% | 0 | 0 | — |
case-03 | pass→pass | 17,955 | 13,905 | -23% | 1 | 1 | 0% | 3,233 | 7,541 | +133% | 0 | 0 | — |
case-04 | pass→pass | 11,792 | 11,399 | -3% | 1 | 1 | 0% | 2,018 | 6,898 | +242% | 0 | 0 | — |
case-05 | fail→pass | 8,542 | 2,694 | -68% | 1 | 1 | 0% | 1,644 | 5,635 | +243% | 0 | 0 | — |
case-06 | pass→pass | 10,583 | 1,793 | -83% | 1 | 1 | 0% | 1,705 | 5,342 | +213% | 0 | 0 | — |
case-07 | pass→pass | 11,099 | 2,664 | -76% | 1 | 1 | 0% | 1,835 | 5,581 | +204% | 0 | 0 | — |
case-08 | pass→pass | 12,663 | 2,566 | -80% | 1 | 1 | 0% | 2,249 | 5,524 | +146% | 0 | 0 | — |
case-09 | pass→pass | 9,398 | 15,891 | +69% | 1 | 1 | 0% | 1,658 | 5,889 | +255% | 0 | 0 | — |
case-10 | pass→pass | 12,520 | 10,451 | -17% | 1 | 1 | 0% | 2,023 | 6,790 | +236% | 0 | 0 | — |
case-11 | pass→pass | 10,362 | 5,637 | -46% | 1 | 1 | 0% | 1,673 | 6,082 | +264% | 0 | 0 | — |
case-12 | pass→pass | 9,503 | 5,733 | -40% | 1 | 1 | 0% | 1,678 | 5,956 | +255% | 0 | 0 | — |
case-13 | pass→pass | 12,200 | 6,501 | -47% | 1 | 1 | 0% | 2,082 | 6,160 | +196% | 0 | 0 | — |
case-14 | pass→pass | 11,437 | 5,497 | -52% | 1 | 1 | 0% | 2,217 | 6,188 | +179% | 0 | 0 | — |
case-15 | fail→pass | 7,358 | 2,100 | -71% | 1 | 1 | 0% | 1,207 | 5,459 | +352% | 0 | 0 | — |
case-16 | pass→pass | 6,725 | 1,928 | -71% | 1 | 1 | 0% | 1,064 | 5,384 | +406% | 0 | 0 | — |
case-17 | pass→pass | 4,700 | 2,465 | -48% | 1 | 1 | 0% | 869 | 5,498 | +533% | 0 | 0 | — |
case-18 | pass→pass | 11,573 | 1,413 | -88% | 1 | 1 | 0% | 2,259 | 5,311 | +135% | 0 | 0 | — |
case-19 | pass→pass | 8,141 | 3,892 | -52% | 1 | 1 | 0% | 1,425 | 5,789 | +306% | 0 | 0 | — |
case-20 | fail→fail | 14,847 | 21,859 | +47% | 1 | 1 | 0% | 2,621 | 9,080 | +246% | 0 | 0 | — |
case-21 | fail→fail | 18,387 | 16,669 | -9% | 1 | 1 | 0% | 3,913 | 8,724 | +123% | 0 | 0 | — |
case-22 | fail→fail | 9,839 | 12,155 | +24% | 1 | 1 | 0% | 1,450 | 7,016 | +384% | 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 +14 percentage points is the difference between those two pass rates over the 22 comparable cases.
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.