literature-search▌
yorkeccak/scientific-skills · updated Apr 8, 2026
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Search across all major scientific literature databases (PubMed, arXiv, bioRxiv, medRxiv) simultaneously using natural language queries powered by Valyu's semantic search API.
Literature Search
Search across all major scientific literature databases (PubMed, arXiv, bioRxiv, medRxiv) simultaneously using natural language queries powered by Valyu's semantic search API.
Why This Skill is Powerful
- No API Parameter Parsing: Just pass natural language queries directly - no need to construct complex search parameters
- Semantic Search: Understands the meaning of your query, not just keyword matching
- Full-Text Access: Returns complete article content, not just abstracts
- Image Links: Includes figures and images from papers
- Comprehensive Coverage: Search across PubMed, arXiv, bioRxiv, and medRxiv simultaneously
- Unified Results: Get results from all sources in a single query
Requirements
- Node.js 18+ (uses built-in fetch)
- Valyu API key from https://platform.valyu.ai ($10 free credits)
CRITICAL: Script Path Resolution
The scripts/search commands in this documentation are relative to this skill's installation directory.
Before running any command, locate the script using:
LITERATURE_SCRIPT=$(find ~/.claude/plugins/cache -name "search" -path "*/literature-search/*/scripts/*" -type f 2>/dev/null | head -1)
Then use the full path for all commands:
$LITERATURE_SCRIPT "CRISPR gene editing advances" 15
API Key Setup Flow
When you run a search and receive "setup_required": true, follow this flow:
-
Ask the user for their API key: "To search scientific literature, I need your Valyu API key. Get one free ($10 credits) at https://platform.valyu.ai"
-
Once the user provides the key, run:
scripts/search setup <api-key> -
Retry the original search.
When to Use This Skill
- Comprehensive literature reviews across all domains
- Finding all relevant research on a topic
- Cross-domain scientific discovery
- Combining biomedical, physics, and preprint literature
- Emerging research across disciplines
Output Format
{
"success": true,
"type": "literature_search",
"query": "CRISPR gene editing advances",
"result_count": 15,
"results": [
{
"title": "Article Title",
"url": "https://...",
"content": "Full article text with figures...",
"source": "pubmed|arxiv|biorxiv|medrxiv",
"relevance_score": 0.95,
"images": ["https://example.com/figure1.jpg"]
}
],
"cost": 0.025
}
Processing Results
With jq
# Get article titles
scripts/search "query" 20 | jq -r '.results[].title'
# Get URLs
scripts/search "query" 20 | jq -r '.results[].url'
# Extract full content
scripts/search "query" 20 | jq -r '.results[].content'
# Filter by source
scripts/search "query" 20 | jq -r '.results[] | select(.source == "arxiv") | .title'
Common Use Cases
Comprehensive Literature Review
# Search across all sources for thorough review
scripts/search "mechanisms of cellular senescence" 100
Cross-Disciplinary Research
# Find papers spanning multiple fields
scripts/search "quantum computing applications in drug discovery" 50
Recent Developments
# Get latest preprints and publications
scripts/search "foundation models for protein folding" 30
Medical Research
# Search biomedical literature comprehensively
scripts/search "immunotherapy checkpoint inhibitors resistance" 40
Error Handling
All commands return JSON with success field:
{
"success": false,
"error": "Error message"
}
Exit codes:
0- Success1- Error (check JSON for details)
API Endpoint
- Base URL:
https://api.valyu.ai/v1 - Endpoint:
/search - Authentication: X-API-Key header
Architecture
scripts/
├── search # Bash wrapper
└── search.mjs # Node.js CLI
Direct API calls using Node.js built-in fetch(), zero external dependencies.
Adding to Your Project
If you're building an AI project and want to integrate Literature Search directly into your application, use the Valyu SDK:
Python Integration
from valyu import Valyu
client = Valyu(api_key="your-api-key")
response = client.search(
query="your search query here",
included_sources=["valyu/valyu-pubmed", "valyu/valyu-arxiv", "valyu/valyu-biorxiv", "valyu/valyu-medrxiv"],
max_results=20
)
for result in response["results"]:
print(f"Title: {result['title']}")
print(f"URL: {result['url']}")
print(f"Content: {result['content'][:500]}...")
TypeScript Integration
import { Valyu } from "valyu-js";
const client = new Valyu("your-api-key");
const response = await client.search({
query: "your search query here",
includedSources: ["valyu/valyu-pubmed", "valyu/valyu-arxiv", "valyu/valyu-biorxiv", "valyu/valyu-medrxiv"],
maxResults: 20
});
response.results.forEach((result) => {
console.log(`Title: ${result.title}`);
console.log(`URL: ${result.url}`);
console.log(`Content: ${result.content.substring(0, 500)}...`);
});
See the Valyu docs for full integration examples and SDK reference.
How to use literature-search on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add literature-search
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches literature-search from GitHub repository yorkeccak/scientific-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate literature-search. Access the skill through slash commands (e.g., /literature-search) or your agent's skill management interface.
Security & Verification Notice
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 development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
User Story & Requirements Generation
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
Competitive Analysis
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
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
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
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ 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.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.8★★★★★32 reviews- ★★★★★Pratham Ware· Dec 28, 2024
I recommend literature-search for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Chaitanya Patil· Dec 24, 2024
We added literature-search from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Isabella Menon· Dec 8, 2024
literature-search has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Hassan Chawla· Nov 27, 2024
Useful defaults in literature-search — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Piyush G· Nov 15, 2024
literature-search fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Meera Malhotra· Oct 18, 2024
literature-search is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Shikha Mishra· Oct 6, 2024
Registry listing for literature-search matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Fatima Ramirez· Sep 5, 2024
literature-search has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Aditi Kim· Aug 24, 2024
Keeps context tight: literature-search is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Aditi Mensah· Jul 15, 2024
literature-search is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
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