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ZanderRuss
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Website𝕏@mathew_fullerGitHub

perplexity-search

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18|2|Updated Jan 10, 2026
Search Tags:#research#information retrieval#search#web scraping#document analysis#perplexity
One-click install
npx skills add https://github.com/ZanderRuss/obsidian-claude --skill perplexity-search-zanderruss
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: perplexity-search
Source: https://github.com/ZanderRuss/obsidian-claude/tree/main/.claude/skills/perplexity-search
Command: npx skills add https://github.com/ZanderRuss/obsidian-claude --skill perplexity-search-zanderruss

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires requests, python-dotenv, litellm, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill provides advanced AI-powered web search capabilities, allowing users to access up-to-date information, scholarly articles, and analyze documents directly, overcoming the limitations of static knowledge cutoffs.

Core Features & Use Cases

  • Real-time Web Search: Get current information on any topic.
  • Academic Search: Prioritize peer-reviewed scholarly sources.
  • Document Analysis: Analyze PDFs and other documents directly.
  • Use Case: A researcher needs to find the latest clinical trial results for a specific drug published in the last month. This Skill can perform an academic search, filter by date, and provide summarized findings with citations.

Quick Start

Use the perplexity-search skill to find the latest developments in quantum computing.

Frequently Asked Questions about perplexity-search

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

FAQPage Schema
How do I get real-time web search results for current information beyond a model's training data?▼

AI-powered web search uses Perplexity models to retrieve real-time information, bypassing static knowledge cutoffs by querying live web data and returning summarized findings with citations.

Can I search for peer-reviewed scholarly articles and filter by date or domain?▼

Yes, academic search mode prioritizes peer-reviewed scholarly sources while applying date and domain filtering to narrow down recent peer-reviewed literature with multi-step reasoning.

Does this approach support direct PDF analysis and document extraction?▼

Direct PDF analysis is supported through API integration, enabling document content extraction and multi-step reasoning over specific file data during information retrieval.

Do I need an API key and Python environment to run AI-powered web searches?▼

Yes, executing AI-powered web searches requires a Python environment with requests, python-dotenv, and litellm installed, plus an active API key configured via environment variables.

What is the best way to find the latest clinical trial results using AI search?▼

The best way to find recent clinical trial results is using academic search mode with date filtering to retrieve summarized, peer-reviewed findings with direct citations.

Are there limitations when using litellm for multi-step reasoning and web scraping?▼

Limitations include dependency on external API rate limits and the accuracy of retrieved web scraping data, which can impact the reliability of multi-step reasoning outputs.

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