perplexity-web-research
Scannednpx machina-cli add skill xpepper/perplexity-agent-skill/perplexity-web-research --openclawPerplexity Web Research
Use the llm CLI with Perplexity plugin for web research, deep reasoning, and independent validation.
Quick Start
llm -m sonar 'your question here'
Model Selection
| Model | Use Case | Context | Web Search |
|---|---|---|---|
sonar | Quick lookups, general questions (default) | 128k | ✓ |
sonar-pro | Thorough answers, detailed research | 200k | ✓ |
sonar-deep-research | Complex multi-faceted research | 128k | ✓ |
sonar-reasoning | Step-by-step reasoning questions | 128k | ✓ |
sonar-reasoning-pro | Advanced reasoning with detail | 128k | ✓ |
r1-1776 | Reasoning without web search | 128k | ✗ |
Start with sonar for most queries. Upgrade to other models when you need comprehensive coverage (sonar-pro), complex synthesis (sonar-deep-research), or logical reasoning (sonar-reasoning/sonar-reasoning-pro).
For detailed selection criteria, decision flowcharts, and examples, see references/model-selection-guide.md.
When to Use
Use this skill in three primary scenarios:
1. Current or External Knowledge
When you need up-to-date information and intentionally want to use Perplexity instead of standard web fetch tools:
- Documentation lookups: Current API docs, library references, framework guides
- Library versions: "What's the latest version of X?"
- Recent changes: "What changed in Y version 2.0?"
- Error research: Unfamiliar error messages or stack traces
- Technology verification: "Does X support Y feature?"
- Current events: Recent news, announcements, releases
- Fact checking: Information that may have changed since training cutoff
2. Second Opinions
When you want an independent perspective before committing to a decision or approach:
- Architecture decisions: "What are the trade-offs between approach A and B?"
- Technology choices: "Should I use X or Y for this use case?"
- Design validation: "Is this approach considered best practice?"
- Problem verification: "Am I thinking about this problem correctly?"
- Alternative perspectives: "What other ways could this be solved?"
3. Deep Reasoning Delegation
When you want to offload complex analytical thinking to specialized reasoning models (even when information doesn't need to be current):
- Complex problem solving: Multi-step reasoning requiring systematic analysis
- Algorithmic thinking: "How should I optimize this algorithm?"
- System design: "How should I architect this system?"
- Trade-off analysis: Comparing multiple options with many variables
- Mathematical reasoning: Calculations, proofs, or quantitative analysis
When NOT to Use
Do NOT use for:
- Local codebase questions: Use grep/read tools instead
- Information already in context: Use existing conversation context
- Simple factual questions: Use your training data when sufficient
- Tasks requiring file operations: Code analysis, refactoring, editing
This skill complements, not replaces, standard web tools (WebFetch, WebSearch). Choose based on whether you want Perplexity's specific capabilities (reasoning models, synthesis, alternative perspective).
Query Best Practices
Craft effective queries for better results:
- Be specific and detailed rather than vague
- Include time constraints for current information
- Specify output format when helpful
- Use domain-appropriate terminology
For comprehensive query strategies and examples, see references/query-strategies.md.
Usage Examples
Current Knowledge: Documentation Lookup
llm -m sonar 'Next.js App Router documentation 2026'
Current Knowledge: Error Research
llm -m sonar 'ECONNREFUSED error when connecting to PostgreSQL'
Current Knowledge: Library Version
llm -m sonar 'latest stable version of React 2026'
Current Knowledge: Complex Research
llm -m sonar-deep-research 'comparison of WebSocket vs Server-Sent Events for real-time updates in 2026'
Second Opinion: Architecture Decision
llm -m sonar-pro 'trade-offs between microservices and monolith for a team of 5 engineers'
Second Opinion: Technology Choice
llm -m sonar 'should I use PostgreSQL or MongoDB for a social media analytics platform'
Deep Reasoning: Algorithm Design
llm -m sonar-reasoning 'how to design an efficient caching strategy for a rate-limited API with bursty traffic patterns'
Deep Reasoning: System Architecture
llm -m sonar-reasoning-pro 'design a scalable job queue system that handles both immediate and scheduled tasks with priority levels'
Using as a Subagent
This skill can be invoked directly or spawned as a subagent for complex research tasks.
Use direct invocation for straightforward queries where results can be immediately incorporated.
Use subagent invocation when research requires multiple iterations, synthesis of findings, or can happen while you work on other tasks.
For detailed patterns and examples, see references/subagent-patterns.md.
Context Marker
When using this skill, add 🔍 as a start-of-message marker.
🔍 I will research Next.js App Router on Perplexity...
Setup
If the llm CLI or Perplexity plugin is not installed, see references/setup.md for installation instructions.
Relationship with Other Tools
This skill complements standard web tools (WebFetch, WebSearch), not replaces them.
Use Perplexity when you want:
- Specialized reasoning models for complex analysis
- Synthesis and perspective from multiple sources
- Independent validation or second opinions
- Specific Perplexity model capabilities
Use standard web tools when you want:
- Direct access to specific URLs or pages
- Raw content from known sources
- General web search without specialized reasoning
Source
git clone https://github.com/xpepper/perplexity-agent-skill/blob/main/perplexity-web-research/SKILL.mdView on GitHub Overview
Leverage the Perplexity CLI with the Perplexity plugin to perform web research, deep reasoning, and independent validation. This skill is ideal when you need current or external knowledge, want a second opinion before decisions, or want to delegate complex analytical thinking to specialized models. It complements standard web tools and can be invoked directly or spawned as a subagent.
How This Skill Works
You run queries through the llm CLI using the Perplexity plugin, selecting from models like sonar or deeper variants for thorough research and reasoning. The skill content guides when to use Perplexity versus traditional web tools and how to escalate to more capable models for complex synthesis or verification, including spawning as a subagent when needed.
When to Use It
- Current or external knowledge lookups (docs, versions, changes, error research, tech verification, current events, fact checking)
- Second opinions before architectural or technology decisions (trade-offs, design validation, evaluation of alternatives)
- Deep reasoning delegation for complex, multi-step analysis (system design, algorithmic thinking, mathematical reasoning)
- Complex problem solving requiring structured, multi-faceted reasoning
- Independent validation and synthesis of information from multiple sources
Quick Start
- Step 1: Ensure Perplexity API key and llm CLI are configured for use with the Perplexity plugin
- Step 2: Run a query with the sonar model, e.g., llm -m sonar 'your question here'
- Step 3: If needed, switch to a deeper model (sonar-pro, sonar-deep-research, or sonar-reasoning) for enhanced analysis
Best Practices
- Start with the sonar model for quick lookups and escalate to sonar-pro or sonar-deep-research for thorough research or deep reasoning
- Decompose complex questions into smaller, evaluable parts to maximize the quality of multi-step reasoning
- Explicitly request independent validation and cross-check results with other sources or tools
- Prioritize up-to-date information for API docs, library versions, and recent changes
- Clarify constraints and scope in your query to improve relevance and reduce extraneous results
Example Use Cases
- Lookup the latest API changes or library version for a project (e.g., 'What is the latest version of X?')
- Compare architecture options (e.g., microservices vs monolith) for a given workload and provide trade-offs
- Perform deep problem solving or system design (e.g., 'design a scalable event-driven architecture for Y')
- Investigate unfamiliar error messages or stack traces with potential causes and fixes
- Assess security or compliance considerations for a new feature using current guidance