perplexity-search-skill
Search the web using Perplexity Sonar AI for synthesized answers with citations, related questions, and optional images.
Best use case
perplexity-search-skill is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Search the web using Perplexity Sonar AI for synthesized answers with citations, related questions, and optional images.
Teams using perplexity-search-skill should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/perplexity-search-skill/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How perplexity-search-skill Compares
| Feature / Agent | perplexity-search-skill | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Search the web using Perplexity Sonar AI for synthesized answers with citations, related questions, and optional images.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Perplexity Search Skill
Search the web using Perplexity's Sonar AI models. Unlike traditional search engines that return links, Perplexity provides synthesized AI-generated answers with inline citations and source URLs.
## How It Works
This skill provides instructions and context. To execute searches, connect the **Perplexity Search** node to the agent's `input-tools` handle.
## perplexity_search Tool
Ask a question and get an AI-synthesized answer with citations.
### Schema Fields
| Field | Type | Required | Description |
|-------|------|----------|-------------|
| query | string | Yes | Question or search query to get AI-powered answer with citations |
### Node Parameters
Additional options configured on the node:
| Parameter | Default | Description |
|-----------|---------|-------------|
| model | sonar | Model: sonar (fast), sonar-pro (deeper research) |
| searchRecencyFilter | (empty) | Filter results by recency: month, week, day, hour |
| returnImages | false | Include relevant images in response |
| returnRelatedQuestions | false | Include follow-up question suggestions |
### Response Format
```json
{
"query": "What are the latest developments in quantum computing?",
"answer": "Recent developments in quantum computing include several significant breakthroughs. **Google's Willow chip** demonstrated error correction below the threshold needed for reliable quantum computation [1]. **IBM** released its 1,121-qubit Condor processor [2], while **Microsoft** announced a new topological qubit approach [3].\n\nKey areas of progress:\n- Error correction advances\n- Increased qubit counts\n- New materials and architectures\n- Growing commercial applications",
"citations": [
"https://blog.google/technology/research/quantum-computing-willow/",
"https://research.ibm.com/blog/condor-processor",
"https://azure.microsoft.com/en-us/blog/quantum/"
],
"results": [
{"url": "https://blog.google/technology/research/quantum-computing-willow/"},
{"url": "https://research.ibm.com/blog/condor-processor"},
{"url": "https://azure.microsoft.com/en-us/blog/quantum/"}
],
"model": "sonar",
"provider": "perplexity",
"images": [],
"related_questions": [
"What is quantum error correction?",
"How many qubits does a useful quantum computer need?"
]
}
```
### Examples
**Research question:**
```json
{
"query": "What are the latest developments in quantum computing?"
}
```
**Technical question:**
```json
{
"query": "How does React Server Components work in Next.js 15?"
}
```
**Current events:**
```json
{
"query": "What happened at the latest AI safety summit?"
}
```
**Comparison:**
```json
{
"query": "Compare PostgreSQL vs MySQL for large-scale applications"
}
```
## When to Use Perplexity Search
- **Research questions** - Get synthesized, well-structured answers with citations
- **Technical explanations** - Detailed answers about how things work
- **Current events analysis** - AI-summarized coverage of recent events
- **Comparison queries** - Side-by-side analysis of options
- **Complex topics** - Multi-faceted questions that benefit from AI synthesis
- **When you need citations** - Every claim backed by source URLs
## When NOT to Use Perplexity Search
- **Simple factual lookups** - Use Brave or Serper for quick facts
- **Image search** - Use Serper with searchType "images"
- **Local business search** - Use Serper with searchType "places"
- **High-volume queries** - Perplexity costs more per query ($0.005)
- **Simple calculations** - Use the calculator tool
## Key Differences from Traditional Search
| Aspect | Traditional Search (Brave/Serper) | Perplexity Sonar |
|--------|----------------------------------|-----------------|
| Output | List of links with snippets | AI-written answer with inline citations |
| Format | title + snippet + URL | Full markdown answer with [1][2] references |
| Best for | Quick lookups, browsing | Research, explanations, synthesis |
| Speed | Faster | Slightly slower (AI generation) |
| Cost | $0.001-0.003/query | $0.005/query |
## Answer Format
The `answer` field contains rich markdown:
- **Bold text** for emphasis
- Numbered citations [1][2] referencing the `citations` array
- Bullet points and lists for structured information
- Headers for organized sections
- Code blocks for technical content
## API Details
- **API**: POST `https://api.perplexity.ai/chat/completions`
- **Auth**: `Authorization: Bearer` header
- **Models**: `sonar` (fast, default), `sonar-pro` (deeper research)
- **Timeout**: 60 seconds (AI generation takes longer than traditional search)
- **Pricing**: ~$0.005 per request
## Setup Requirements
1. Connect this skill to the agent's `input-skill` handle
2. Connect the **Perplexity Search** node to the agent's `input-tools` handle
3. Add your Perplexity API key in Credentials > SearchRelated Skills
serper-search-skill
Search the web using Serper API for Google-powered search results including web, news, images, and places.
duckduckgo-search-skill
Search the web using DuckDuckGo for free, privacy-focused results with no API key required.
brave-search-skill
Search the web using Brave Search API for privacy-focused, independent search results with no tracking.
twitter-search-skill
Search for recent tweets on Twitter/X using keywords, hashtags, mentions, and advanced query operators. Returns rich tweet data with expanded URLs, author info, media, metrics, and referenced tweets.
fs-search-skill
Search the filesystem with ls (list directory), glob (pattern match), or grep (search file contents).
proxy-config-skill
Configure residential proxy providers and make proxied HTTP requests with geo-targeting.
http-request-skill
Make HTTP requests to external APIs and web services. Supports GET, POST, PUT, DELETE, PATCH methods with headers and JSON body.
crawlee-scraper-skill
Read and extract content from any web page URL.
browser-skill
Interactive browser automation - navigate, click, type, fill forms, take screenshots, get accessibility snapshots. Supports system Chrome/Edge via auto-detection.
apify-skill
Run web scrapers and extract data from websites and social media platforms using Apify actors. Supports Instagram, TikTok, Twitter/X, LinkedIn, Facebook, YouTube, Google Search, and general web crawling.
nearby-places-skill
Search for nearby places like restaurants, cafes, stores, and services using Google Places API. Find places by type and location.
shell-skill
Execute short-lived shell commands in a sandboxed environment. No PATH access -- use process_manager for npm/python/node commands.