💡 Summary
This skill enables natural language searches across multiple scientific literature databases with full-text access.
🎯 Target Audience
🤖 AI Roast: “Powerful, but the setup might scare off the impatient.”
Risk: Medium. Review: shell/CLI command execution; outbound network access (SSRF, data egress); API keys/tokens handling and storage. Run with least privilege and audit before enabling in production.
Scientific Skills for Claude

Natural language scientific literature search skills with semantic search over PubMed, arXiv, ChEMBL, DrugBank, bioRxiv, medRxiv, clinical trials, and more with zero API complexity.
Why These Skills Are Powerful
- No API Parameter Parsing: Just pass natural language queries - no complex search syntax or parameters needed
- Semantic Search: Understands meaning and context, not just keyword matching
- Full-Text Access: Returns complete article content, not just abstracts
- Image Links: Includes figures and images from papers
- Comprehensive Coverage: Access to ALL scientific literature across multiple databases
Example


Available Skills
Individual Data Sources (9 skills)
| Skill | Data Source | Coverage |
|-------|-------------|----------|
| pubmed-search | PubMed | Biomedical and life sciences literature |
| arxiv-search | arXiv | Physics, mathematics, computer science, quantitative biology preprints |
| biorxiv-search | bioRxiv | Biology preprints |
| medrxiv-search | medRxiv | Medical and health sciences preprints |
| chembl-search | ChEMBL | Bioactive molecules with drug-like properties |
| drugbank-search | DrugBank | Comprehensive drug and drug target database |
| clinical-trials-search | ClinicalTrials.gov | Clinical trials registry |
| drug-labels-search | FDA Drug Labels | Official FDA drug information |
| open-targets-search | Open Targets | Drug targets and disease associations |
| patents-search | Patent Databases | Global patent filings |
Aggregated Skills (3 skills)
| Skill | Combined Sources | Best For |
|-------|------------------|----------|
| literature-search | PubMed + arXiv + bioRxiv + medRxiv | General scientific literature review |
| biomedical-search | PubMed + bioRxiv + medRxiv + ClinicalTrials + Drug Labels | Medical and clinical research |
| drug-discovery-search | ChEMBL + DrugBank + Drug Labels + Open Targets | Drug discovery and development |
Installation
Via skills.sh (Recommended)
npx skills i yorkeccak/scientific-skills
Or browse individual skills at skills.sh - search for "yorkeccak"
Example: skills.sh/yorkeccak/scientific-skills/biomedical-search
Supports installation to multiple agents:
- Antigravity
- Claude Code
- Clawdbot
- Codex
- Cursor
- Droid
- Gemini CLI
- GitHub Copilot
- Goose
- Kilo Code
- Kiro CLI
- OpenCode
- Roo Code
- Trae
- Windsurf
Via Claude Plugin
/plugin install yorkeccak/scientific-skills
Manual Installation
# Clone the repository cd ~/dev git clone https://github.com/yorkeccak/scientific-skills.git # Add as a local plugin /plugin add ~/dev/scientific-skills
Quick Start
1. Get an API Key for the Valyu search
There is $10 free credits: platform.valyu.ai
2. First Use (Automatic Setup)
Just start using any skill! Claude will automatically:
- Detect that no API key is configured
- Ask you to paste your API key
- Save it to
~/.valyu/config.json - Run your search
You: Search PubMed for CRISPR advances in 2024
Claude: "To search PubMed, I need your Valyu API key from https://platform.valyu.ai"
You: val_abc123...
Claude: [saves key and runs search automatically]
3. Manual Setup (Optional)
If you prefer to set up manually:
Environment Variable (recommended):
# For Zsh (macOS default) echo 'export VALYU_API_KEY="your-api-key-here"' >> ~/.zshrc source ~/.zshrc # For Bash echo 'export VALYU_API_KEY="your-api-key-here"' >> ~/.bashrc source ~/.bashrc
Config File:
mkdir -p ~/.valyu echo '{"apiKey": "your-api-key-here"}' > ~/.valyu/config.json
Output Format
All skills return consistent JSON output:
{ "success": true, "type": "pubmed_search", "query": "your query", "result_count": 10, "results": [ { "title": "Article Title", "url": "https://...", "content": "Full article text with figures...", "source": "pubmed", "relevance_score": 0.95, "images": ["https://example.com/figure1.jpg"] } ], "cost": 0.025 }
Processing Results with jq
# Get article titles scripts/search "query" 10 | jq -r '.results[].title' # Get URLs scripts/search "query" 10 | jq -r '.results[].url' # Extract content scripts/search "query" 10 | jq -r '.results[].content' # Filter by relevance score scripts/search "query" 20 | jq '.results[] | select(.relevance_score > 0.9)'
Common Use Cases
Literature Review
Search across all scientific literature:
cd ~/dev/scientific-skills/skills/literature-search scripts/search "epigenetic modifications in cancer" 50
Drug Discovery
Find compounds and targets:
cd ~/dev/scientific-skills/skills/drug-discovery-search scripts/search "kinase inhibitors for melanoma" 30
Clinical Research
Find trials and patient outcomes:
cd ~/dev/scientific-skills/skills/biomedical-search scripts/search "checkpoint inhibitor combinations trials" 40
Patent Research
Search for prior art:
cd ~/dev/scientific-skills/skills/patents-search scripts/search "CRISPR delivery methods" 25
Gene Function Research
Understand gene roles:
cd ~/dev/scientific-skills/skills/pubmed-search scripts/search "TP53 tumor suppression mechanisms" 20
Requirements
- Node.js 18+ (uses built-in fetch API)
- Valyu API key from platform.valyu.ai
- No npm packages required - zero external dependencies
Technical Details
Architecture
Each skill consists of:
skill-name-search/
├── SKILL.md # Skill documentation
└── scripts/
├── search # Bash wrapper
└── search.mjs # Node.js implementation
FAQ
What's the difference between individual and aggregated skills?
Individual skills search a single data source (e.g., only PubMed). Aggregated skills search multiple related sources simultaneously for more comprehensive results.
How is this different from regular PubMed search?
- Semantic understanding: Understands query meaning, not just keywords
- Full-text access: Returns complete articles, not just abstracts
- Natural language: No need to learn search syntax
- Unified interface: Same approach across all scientific databases
Can I customize the search parameters?
The skills use natural language queries by design to eliminate API complexity. For advanced filtering, you can:
- Be more specific in your natural language query
- Adjust the
maxResultsparameter - Filter results using jq after receiving them
Comparison to Traditional Search
| Feature | Traditional Search | These Skills | |---------|-------------------|--------------| | Query Format | Complex syntax, Boolean operators | Natural language | | Search Type | Keyword matching | Semantic understanding | | Content Returned | Abstracts, metadata | Full-text with images | | API Complexity | High - learn each API | Zero - same interface | | Setup Time | Hours per database | Minutes for all | | Multiple Sources | Write separate integrations | Single unified approach |
Contributing
Welcome all PRs!
License
MIT License - See individual skill files for details.
Pros
- No API complexity with natural language queries
- Semantic search for better results
- Access to full articles and images
- Wide coverage across scientific databases
Cons
- Requires an API key for usage
- Dependent on external services for data
- Limited to the capabilities of the Valyu API
- Setup may be complex for non-technical users
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Disclaimer: This content is sourced from GitHub open source projects for display and rating purposes only.
Copyright belongs to the original author yorkeccak.
