perplexity-search
Perform AI-powered web searches with real-time information using Perplexity models via LiteLLM and OpenRouter. This skill should be used when conducting web searches for current information, finding recent scientific literature, getting grounded answers with source citations, or accessing information beyond the model knowledge cutoff. Provides access to multiple Perplexity models including Sonar Pro, Sonar Pro Search (advanced agentic search), and Sonar Reasoning Pro through a single OpenRouter API key.
npx skills add LeonChaoX/qinyan-academic-skills --skill perplexity-search --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Perplexity Search ## Overview Perform AI-powered web searches using Perplexity models through LiteLLM and OpenRouter. Perplexity provides real-time, web-grounded answers with source citations, making it ideal for finding current information, recent scientific literature, and facts beyond the model's training data cutoff. This skill provides access to all Perplexity models through OpenRouter, requiring only a single API key (no separate Perplexity account needed). ## When to Use This Skill Use this skill when: - Searching for current information or recent developments (2024 and beyond) - Finding latest scientific publications and research - Getting real-time answers grounded in web sources - Verifying facts with source citations - Conducting literature searches across multiple domains - Accessing information beyond the model's knowledge cutoff - Performing domain-specific research (biomedical, technical, clinical) - Comparing current approaches or technologies **Do not use** for: - Simple calculations or logic problems (use directly) - Tasks requiring code execution (use standard tools) - Questions well within the model's training data (unless verification needed) ## Quick Start #
- Overview
- When to Use This Skill
- Quick Start
- Setup (One-time)
- Basic Usage
- Available Models
- Crafting Effective Queries
- Be Specific and Detailed
- Include Time Constraints
- Specify Domain and Sources
- Structure Complex Queries
- Common Use Cases
- Scientific Literature Search
- Technical Documentation
export OPENROUTER_API_KEY='sk-or-v1-your-key-here' python scripts/setup_env.py --api-key sk-or-v1-your-key-here uv pip install litellm python scripts/perplexity_search.py --check-setup python scripts/perplexity_search.py "What are the latest developments in CRISPR gene editing?" python scripts/perplexity_search.py "Recent CAR-T therapy clinical trials" --output results.json python scripts/perplexity_search.py "Compare mRNA and viral vector vaccines" --model sonar-pro-search python scripts/perplexity_search.py "Quantum computing for drug discovery" --verbose python scripts/perplexity_search.py \ Save to JSON
What does the perplexity-search skill do?
Perform AI-powered web searches with real-time information using Perplexity models via LiteLLM and OpenRouter. This skill should be used when conducting web searches for current information, finding recent scientific literature, getting grounded answers with source citations, or accessing information beyond the model knowledge cutoff. Provides access to multiple Perplexity models including Sonar Pro, Sonar Pro Search (advanced agentic search), and Sonar Reasoning Pro through a single OpenRouter API key.
How do I install it?
Run `npx skills add LeonChaoX/qinyan-academic-skills --skill perplexity-search --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From LeonChaoX/qinyan-academic-skills, a repository with 759 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.