Systematic literature reviews across multiple academic databases with verified citations and professional formatting.
Works with
Searches PubMed, bioRxiv, arXiv, Semantic Scholar, and specialized databases (ChEMBL, KEGG, UniProt) via integrated skills; aggregates and deduplicates results across sources
Structures reviews through seven phases: planning, multi-database search, screening, data extraction, thematic synthesis, citation verification, and PDF generation
Verifies all DOIs and citations
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionliterature-reviewExecute the skills CLI command in your project's root directory to begin installation:
Fetches literature-review from davila7/claude-code-templates and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate literature-review. Access via /literature-review in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
1
total installs
1
this week
24.2K
GitHub stars
0
upvotes
Run in your terminal
1
installs
1
this week
24.2K
stars
Conduct systematic, comprehensive literature reviews following rigorous academic methodology. Search multiple literature databases, synthesize findings thematically, verify all citations for accuracy, and generate professional output documents in markdown and PDF formats.
This skill integrates with multiple scientific skills for database access (gget, bioservices, datacommons-client) and provides specialized tools for citation verification, result aggregation, and document generation.
Use this skill when:
⚠️ MANDATORY: Every literature review MUST include at least 1-2 AI-generated figures using the scientific-schematics skill.
This is not optional. Literature reviews without visual elements are incomplete. Before finalizing any document:
How to generate figures:
How to generate schematics:
python scripts/generate_schematic.py "your diagram description" -o figures/output.png
The AI will automatically:
When to add schematics:
For detailed guidance on creating schematics, refer to the scientific-schematics skill documentation.
Literature reviews follow a structured, multi-phase workflow:
Define Research Question: Use PICO framework (Population, Intervention, Comparison, Outcome) for clinical/biomedical reviews
Establish Scope and Objectives:
Develop Search Strategy:
Set Inclusion/Exclusion Criteria:
Multi-Database Search:
Select databases appropriate for the domain:
Biomedical & Life Sciences:
gget skill: gget search pubmed "search terms" for PubMed/PMCgget skill: gget search biorxiv "search terms" for preprintsbioservices skill for ChEMBL, KEGG, UniProt, etc.General Scientific Literature:
Specialized Databases:
gget alphafold for protein structuresgget cosmic for cancer genomicsdatacommons-client for demographic/statistical dataDocument Search Parameters:
## Search Strategy
### Database: PubMed
- **Date searched**: 2024-10-25
- **Date range**: 2015-01-01 to 2024-10-25
- **Search string**:
("CRISPR"[Title] OR "Cas9"[Title]) AND ("sickle cell"[MeSH] OR "SCD"[Title/Abstract]) AND 2015:2024[Publication Date]
- **Results**: 247 articles
Repeat for each database searched.
Export and Aggregate Results:
scripts/search_databases.py for post-processing:
python search_databases.py combined_results.json \
--deduplicate \
--format markdown \
--output aggregated_results.md
Deduplication:
python search_databases.py results.json --deduplicate --output unique_results.json
Title Screening:
Abstract Screening:
Full-Text Screening:
Create PRISMA Flow Diagram:
Initial search: n = X
├─ After deduplication: n = Y
├─ After title screening: n = Z
├─ After abstract screening: n = A
└─ Included in review: n = B
Extract Key Data from each included study:
Assess Study Quality:
Organize by Themes:
Create Review Document from template:
cp assets/review_template.md my_literature_review.md
Write Thematic Synthesis (NOT study-by-study summaries):
Example structure:
#### 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 Analysis:
Write Discussion:
CRITICAL: All citations must be verified for accuracy before final submission.
Verify All DOIs:
python scripts/verify_citations.py my_literature_review.md
This script:
Review Verification Report:
Format Citations Consistently:
references/citation_styles.md)Generate PDF:
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 installedReview Final Output:
Quality Checklist:
Access via gget skill:
# Search PubMed
gget search pubmed "CRISPR gene editing" -l 100
# Search with filters
# Use PubMed Advanced Search Builder to construct complex queries
# Then execute via gget or direct Entrez API
Search tips:
"sickle cell disease"[MeSH][Title], [Title/Abstract], [Author]2020:2024[Publication Date]Access via gget skill:
gget search biorxiv "CRISPR sickle cell" -l 50
Important considerations:
Access via direct API or WebFetch:
# 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):
Use appropriate skills:
bioservices skill for chemical bioactivitygget or bioservices skill for protein informationbioservices skill for pathways and genesgget skill for cancer mutationsgget alphafold for protein structuresgget or direct API for experimental structuresExpand search via citation networks:
Forward citations (papers citing key papers):
Backward citations (references from key papers):
Detailed formatting guidelines are in references/citation_styles.md. Quick reference:
Always verify citations with verify_citations.py before finalizing.
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
davila7/claude-code-templates
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
Solid pick for teams standardizing on skills: literature-review is focused, and the summary matches what you get after install.
Solid pick for teams standardizing on skills: literature-review is focused, and the summary matches what you get after install.
literature-review fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
literature-review is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
literature-review has been reliable in day-to-day use. Documentation quality is above average for community skills.
literature-review reduced setup friction for our internal harness; good balance of opinion and flexibility.
We added literature-review from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
I recommend literature-review for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
We added literature-review from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
I recommend literature-review for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
showing 1-10 of 57