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using-perplexity-platform

Generate search-augmented responses with real-time web search and citations.

Updated Jan 13, 2026
Search Tags:#api#llm#search#real-time#citations#perplexity
One-click install
npx skills add https://github.com/FortiumPartners/ensemble-vnext --skill using-perplexity-platform
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: using-perplexity-platform
Source: https://github.com/FortiumPartners/ensemble-vnext/tree/main/packages/skills/using-perplexity-platform
Command: npx skills add https://github.com/FortiumPartners/ensemble-vnext --skill using-perplexity-platform

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill empowers developers to build AI applications that leverage real-time web search, providing up-to-date information and source citations, overcoming the limitations of static knowledge cutoffs.

Core Features & Use Cases

  • Search-Augmented Generation: Generate responses grounded in current web data.
  • Real-Time Web Search: Access live information for any query.
  • Automatic Citations: Automatically attribute information to its sources.
  • Use Case: Build a research assistant that can answer questions about recent events, a news summarizer that pulls from live articles, or a fact-checking tool that provides verifiable sources.

Quick Start

Use the using-perplexity-platform skill to get the latest developments in AI.

Frequently Asked Questions about using-perplexity-platform

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
Can I use Python and TypeScript clients for search-augmented generation?▼

Search-augmented generation overcomes static LLM knowledge cutoffs by querying live web data. It grounds AI responses in current information and automatically attributes facts to their original sources.

How do automatic citations work with real-time web search APIs?▼

Automatic citations attribute information to its original sources during search-augmented generation. The API returns source attribution alongside the generated response, enabling verifiable fact-checking and research.

What are the best use cases for real-time search-augmented AI?▼

You can build a research assistant for recent events, a live news summarizer, or a fact-checking tool. These applications use real-time web search to provide up-to-date information with source citations.

Do I need external dependencies to configure advanced search in my API?▼

You need a Python or TypeScript environment and access to an OpenAI-compatible API. The API handles web search, streaming, and citation generation, so no external dependencies are required.

Why does search-augmented generation provide better answers for recent events?▼

Search-augmented AI provides live information and source citations, overcoming static knowledge cutoffs. It grounds responses in current web data rather than relying solely on pre-trained model weights.

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