web-search
Search the Web with AI-Powered Tools
Also available from: inference-sh-9,inference-sh,inferen-sh,Cain96
Enhance your AI agents with real-time web search and content extraction capabilities. This skill provides access to Tavily and Exa APIs through inference.sh CLI for research, fact-checking, and RAG pipelines.
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Using "web-search". Search for the latest developments in quantum computing
Expected outcome:
Found 5 recent sources. Key developments include IBM's 1000-qubit processor breakthrough, Google's quantum error correction improvements, and new quantum cryptography standards. Sources: Nature, MIT Technology Review, ArXiv.
Using "web-search". Extract and summarize the content from https://example.com/research-paper
Expected outcome:
Extracted 4,200 words from the research paper. Summary: The paper presents a new approach to transformer architecture that reduces computational complexity by 40% while maintaining accuracy benchmarks.
Using "web-search". What is the current population of Tokyo according to official sources?
Expected outcome:
According to the Tokyo Metropolitan Government statistics (2024), the population of Tokyo is approximately 14.1 million people in the prefecture, with 9.8 million in the 23 special wards.
Security Audit
SafeAll 38 static analysis findings are false positives from documentation code blocks and URL references. The skill legitimately uses Bash tool with inference.sh CLI for web search capabilities. No actual command injection, credential exfiltration, or weak cryptography detected.
Low Risk Issues (3)
Risk Factors
⚙️ External commands (1)
🌐 Network access (1)
Quality Score
What You Can Build
Research Agent with Source Citations
Build an AI agent that searches the web for current information on any topic, extracts relevant content, and provides answers with proper source citations. Useful for students, researchers, and analysts who need accurate, up-to-date information.
Fact-Checking Assistant
Verify claims and statements by searching for authoritative sources. The skill can extract content from multiple URLs and cross-reference information to determine accuracy. Ideal for journalists, editors, and content moderators.
RAG Pipeline Data Enrichment
Enhance retrieval-augmented generation systems by fetching fresh web content to supplement static knowledge bases. Search for recent developments, extract relevant articles, and feed them into LLM prompts for more accurate responses.
Try These Prompts
Search the web for information about [your topic]. Use Tavily Search Assistant to find relevant, recent sources.
Search for recent developments in [topic/industry]. Extract content from the top 5 most relevant URLs and summarize the key trends.
Verify this claim: [specific claim or statement]. Search for authoritative sources that confirm or refute this information.
First search for latest information about [topic], then use those results to answer: [specific question]. Include sources in your response.
Best Practices
- Always verify search results by checking multiple sources, especially for factual claims
- Use specific, targeted queries rather than broad questions to get more relevant results
- Chain search operations with LLM analysis to synthesize information from multiple sources
- Respect rate limits and implement caching for frequently searched queries
Avoid
- Do not use web search as the primary source for sensitive or critical decisions without human verification
- Avoid searching for very broad topics without specific constraints, as this may return irrelevant results
- Do not extract content from websites that prohibit scraping in their robots.txt or terms of service
- Never rely solely on AI-generated answers without reviewing the cited sources for accuracy