Search across all major scientific literature databases (PubMed, arXiv, bioRxiv, medRxiv) simultaneously using natural language queries powered by Valyu's semantic search API.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionliterature-searchExecute the skills CLI command in your project's root directory to begin installation:
Fetches literature-search from yorkeccak/scientific-skills 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-search. Access via /literature-search 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.
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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
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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.
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
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.
{
"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
}
# 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'
# Search across all sources for thorough review
scripts/search "mechanisms of cellular senescence" 100
# Find papers spanning multiple fields
scripts/search "quantum computing applications in drug discovery" 50
# Get latest preprints and publications
scripts/search "foundation models for protein folding" 30
# Search biomedical literature comprehensively
scripts/search "immunotherapy checkpoint inhibitors resistance" 40
All commands return JSON with success field:
{
"success": false,
"error": "Error message"
}
Exit codes:
0 - Success1 - Error (check JSON for details)https://api.valyu.ai/v1/searchscripts/
├── search # Bash wrapper
└── search.mjs # Node.js CLI
Direct API calls using Node.js built-in fetch(), zero external dependencies.
If you're building an AI project and want to integrate Literature Search directly into your application, use the Valyu SDK:
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]}...")
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.
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.
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parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
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I recommend literature-search for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
We added literature-search from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
literature-search has been reliable in day-to-day use. Documentation quality is above average for community skills.
Useful defaults in literature-search — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
literature-search fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
literature-search is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Registry listing for literature-search matched our evaluation — installs cleanly and behaves as described in the markdown.
literature-search has been reliable in day-to-day use. Documentation quality is above average for community skills.
Keeps context tight: literature-search is the kind of skill you can hand to a new teammate without a long onboarding doc.
literature-search is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
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