SKILL.md
$2a
The scripts/search commands in this documentation are relative to this skill's installation directory.
Before running any command, locate the script using:
ARXIV_SCRIPT=$(find ~/.claude/plugins/cache -name "search" -path "*/arxiv-search/*/scripts/*" -type f 2>/dev/null | head -1)
Then use the full path for all commands:
$ARXIV_SCRIPT "quantum entanglement" 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 arXiv, 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.
Example Flow:
User: Search arXiv for transformer architecture papers
→ Response: {"success": false, "setup_required": true, ...}
→ Claude asks: "Please provide your Valyu API key from https://platform.valyu.ai"
→ User: "val_abc123..."
→ Claude runs: scripts/search setup val_abc123...
→ Response: {"success": true, "type": "setup", ...}
→ Claude retries: scripts/search "transformer architecture papers" 10
→ Success!
When to Use This Skill
- Searching preprints across physics, mathematics, and computer science
- Finding research before peer review publication
- Cross-disciplinary research combining fields
- Staying current with rapid developments in AI and theoretical physics
- Prior art searching for new ideas
- Tracking emerging research trends
Output Format
{
"success": true,
"type": "arxiv_search",
"query": "quantum entanglement",
"result_count": 10,
"results": [
{
"title": "Article Title",
"url": "https://arxiv.org/abs/...",
"content": "Full article text with figures...",
"source": "arxiv",
"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 full content
scripts/search "query" 10 | jq -r '.results[].content'
Common Use Cases
AI/ML Research
# Find recent machine learning papers
scripts/search "large language model architectures" 50
Physics Research
# Search for quantum physics papers
scripts/search "topological quantum computation" 20
Mathematics
# Find math papers
scripts/search "representation theory and Lie algebras" 15
Computer Science
# Search for CS theory papers
scripts/search "distributed systems consensus algorithms" 25
Error Handling
All commands return JSON with success field:
{
"success": false,
"error": "Error message"
}
Exit codes:
0- Success
1- 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 arXiv 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-arxiv"],
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-arxiv"],
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.