How does this skill help reduce Perplexity API latency?

It implements advanced caching strategies and connection pooling, which reuses active connections and stores frequent responses to avoid unnecessary network round-trips.

Does this skill provide monitoring for API calls?

Yes, it includes a measured call wrapper that tracks the duration of operations, helping you establish latency baselines and identify performance regressions.

Can I use this for production-grade enterprise applications?

Yes, it includes distributed caching patterns using Redis and request batching via DataLoader, both of which are standard for high-scale production environments.

What tools are required to use these optimization patterns?

The patterns utilize common Node.js libraries like lru-cache, ioredis, and dataloader, alongside standard HTTPS agents for connection management.

Perplexity Performance Tuning

Name: Perplexity Performance Tuning
Author: Brmbobo

byBrmbobo

Desarrollo de API

Optimizes Perplexity API throughput and reduces latency using advanced caching, batching, and connection pooling strategies.

Acerca de

This skill empowers Claude with specialized patterns for fine-tuning Perplexity API integrations, addressing common bottlenecks like slow response times and high latency. It provides production-ready implementations for LRU and Redis caching, request batching via DataLoader, and HTTP connection pooling with keep-alive configurations. By applying these best practices, developers can ensure their AI-powered applications remain responsive and scalable while effectively managing API rate limits and reducing redundant network overhead.

Características Principales

HTTP connection pooling configuration to minimize TCP handshake overhead.
Real-time performance monitoring wrappers for tracking P50, P95, and P99 latency.
Multi-layer caching support using both LRU (in-memory) and Redis (distributed) strategies.
Async generator patterns for optimized pagination and large dataset handling.
0 GitHub stars
Request batching with DataLoader to combine multiple queries into optimized batches.

Casos de Uso

Reducing search latency in real-time AI chat applications.
Scaling Perplexity integrations for high-traffic enterprise SaaS platforms.
Managing API costs and rate limits through intelligent request deduplication.

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add brmbobo/web2podcast perplexity-performance-tuning

For use in Claude.ai and ChatGPT

Download Skill

GitHub