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Web Search & Extraction

web-search

Web search and content extraction with Tavily and Exa via inference.sh CLI. Apps: Tavily Search, Tavily Extract, Exa Search, Exa Answer, Exa Extract. Capabilities: AI-powered search, content extraction, direct answers, research. Use for: research, RAG pipelines, fact-checking, content aggregation, agents. Triggers: web search, tavily, exa, search api, content extraction, research, internet search, ai search, search assistant, web scraping, rag, perplexity alternative

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SKILL.md

Full skill instructions

Install the belt CLI skill: npx skills add belt-sh/​cli

Web Search & Extraction

Search the web and extract content via inference.sh CLI.

Web Search & Extraction

Quick Start

Requires inference.sh CLI (belt). Install instructions

belt login

# Search the web
belt app run tavily/​search-assistant --input '{"query": "latest AI developments 2024"}'

Available Apps

Tavily

AppApp IDDescription
Search Assistanttavily/​search-assistantAI-powered search with answers
Extracttavily/​extractExtract content from URLs

Exa

AppApp IDDescription
Searchexa/​searchSmart web search with AI
Answerexa/​answerDirect factual answers
Extractexa/​extractExtract and analyze web content

Examples

Tavily Search

belt app run tavily/​search-assistant --input '{
  "query": "What are the best practices for building AI agents?"
}'

Returns AI-generated answers with sources and images.

Tavily Extract

belt app run tavily/​extract --input '{
  "urls": ["https://example.com/​article1", "https://example.com/​article2"]
}'

Extracts clean text and images from multiple URLs.

Exa Search

belt app run exa/​search --input '{
  "query": "machine learning frameworks comparison"
}'

Returns highly relevant links with context.

Exa Answer

belt app run exa/​answer --input '{
  "question": "What is the population of Tokyo?"
}'

Returns direct factual answers.

Exa Extract

belt app run exa/​extract --input '{
  "url": "https://example.com/​research-paper"
}'

Extracts and analyzes web page content.

Workflow: Research + LLM

# 1. Search for information
belt app run tavily/​search-assistant --input '{
  "query": "latest developments in quantum computing"
}' > search_results.json

# 2. Analyze with Claude
belt app run openrouter/​claude-sonnet-45 --input '{
  "prompt": "Based on this research, summarize the key trends: <search-results>"
}'

Workflow: Extract + Summarize

# 1. Extract content from URL
belt app run tavily/​extract --input '{
  "urls": ["https://example.com/​long-article"]
}' > content.json

# 2. Summarize with LLM
belt app run openrouter/​claude-haiku-45 --input '{
  "prompt": "Summarize this article in 3 bullet points: <content>"
}'

Use Cases

  • Research: Gather information on any topic
  • RAG: Retrieval-augmented generation
  • Fact-checking: Verify claims with sources
  • Content aggregation: Collect data from multiple sources
  • Agents: Build research-capable AI agents

Related Skills

# Full platform skill (all apps)
npx skills add inference-sh/​skills@infsh-cli

# LLM models (combine with search for RAG)
npx skills add inference-sh/​skills@llm-models

# Image generation
npx skills add inference-sh/​skills@ai-image-generation

Browse all apps: belt app list

Documentation

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