researching-web
Web research via Perplexity AI. Use when user asks to research topics, compare technologies (X vs Y), find best practices, lookup industry standards, or get current information. Triggers on "research", "compare", "vs", "best practice", "pros and cons", "which is better".
When & Why to Use This Skill
This Claude skill leverages Perplexity AI to perform high-speed and deep-dive web research directly within your workflow. It is designed to provide real-time information, technical comparisons, and industry-standard best practices, offering both a quick lookup mode and a context-aware 'Deep Mode' for complex, multi-source synthesis.
Use Cases
- Technology Comparisons: Conducting detailed 'X vs Y' analysis (e.g., Redis vs Memcached) to make informed architectural decisions based on current performance data.
- Best Practices & Standards: Retrieving up-to-date industry standards, security guidelines like OWASP, or language-specific coding patterns (e.g., Go 1.25 error handling).
- Documentation Lookup: Finding and synthesizing documentation for specific library versions or APIs when local context is insufficient.
- Deep Contextual Research: Running background tasks to research improvements for existing codebases, fetching and analyzing multiple cited sources to provide comprehensive recommendations.
| name | researching-web |
|---|---|
| description | Web research via Perplexity AI. Use for technical comparisons (X vs Y), best practices, industry standards, documentation. Triggers on "research", "compare", "vs", "best practice", "which is better", "pros and cons". |
| context | fork |
Web Research with Perplexity
Two modes: quick (MCP direct) or deep (Task-based with context).
Best For
- Technology comparisons (X vs Y)
- Best practices, industry standards
- OWASP, security guidelines
- Documentation references
- Stable technical content
Quick Mode (Simple Queries)
Use MCP directly for fast, simple lookups:
mcp__perplexity-ask__perplexity_ask({
"messages": [{ "role": "user", "content": "Your research question" }]
})
Deep Mode (Context-Aware Research)
For codebase-aware research, spawn the perplexity-researcher agent.
Foreground (blocking)
Task(subagent_type="perplexity-researcher", prompt="Research: <topic>")
Background (recommended for context efficiency)
Run in background to avoid polluting main context:
Task(
subagent_type="perplexity-researcher",
prompt="Research: <topic>",
run_in_background=true
)
Retrieve results when ready:
TaskOutput(task_id="<agent_id>", block=true)
Use background mode when:
- Running multiple research queries in parallel
- Main task can continue while research runs
- Want to keep main context clean
When to Use Deep Mode
- User asks "best way to do X" (needs to compare with current code)
- Researching improvements to existing code
- Need to understand if recommendations apply to current stack
Query Formulation Tips
- Be specific: "Go 1.25 error handling best practices 2025"
- Include context: "Redis vs Memcached for session storage in Go services"
- Ask comparisons: "Pros and cons of gRPC vs REST for microservices"
- Include year: "Claude Code context optimization 2025"
Reference Following (Deep Research)
After Perplexity returns results with citations:
- Review all cited URLs in the response
- WebFetch top 2-3 most relevant sources for deeper context
- Synthesize comprehensive answer combining all sources
# After Perplexity response with citations
WebFetch(url="<cited-url-1>", prompt="Extract key details about <topic>")
WebFetch(url="<cited-url-2>", prompt="Extract implementation examples")
Use reference following when:
- Initial answer is high-level and needs specifics
- User asks "tell me more" or "dig deeper"
- Implementing something that needs detailed guidance
Output Structure
## Summary
[Key findings - 2-3 sentences]
## Details
[Organized findings by topic]
## Recommendations
[Actionable items for the project]
## Sources
- [Source](url) - [what was learned]