Skill UI

This guide outlines the comprehensive reference architecture for building advanced, AI-powered search and research applications, modeled after Perplexity. It covers three core tiers: a searchable service with model routing and caching, a citation pipeline for validation and rendering, and an orchestrator for multi-query research synthesis. Use this when designing complex, production-grade, search-augmented LLM integrations or reviewing advanced system patterns.

This skill provides production-ready patterns for integrating with the Perplexity Sonar API in both TypeScript and Python. It covers best practices for creating robust wrappers around the standard OpenAI client, including dedicated handling for citations, search results, and related questions. It also includes essential utilities like implementing exponential backoff for reliable retries. Use this when setting up complex search-augmented generation workflows or standardizing team coding practices around Perplexity usage.

Prediction Monitor Assistant automates machine learning deployment monitoring tasks, offering step-by-step guidance, best-practice patterns, production-ready code and configuration generation, and validation to keep serving and inference workflows reliable.

Enables Claude to invoke the computer-vision-processor plugin, auto-generating Python calls to run object detection, classification, and segmentation on user-provided images for detailed analysis.

Covers prompt and response caching plus Cache Augmented Generation patterns for LLMs such as Claude and OpenAI to reuse stable context, reduce repeat work, and cut latency/cost on recurring queries.

Document describes structured prompts, chain-of-thought guidance, few-shot examples, system prompt design, and tooling advice for building reliable prompt engineering workflows in LLM-augmented products.

A comprehensive collection of battle-tested, structured prompts designed to elevate interactions with Large Language Models (LLMs). Covers advanced use cases including expert coding reviews, system architecture design, performance analysis, technical writing, and creative brainstorming, ensuring high-quality, actionable AI output.

This skill provides architectural patterns and implementation guides for developing advanced voice agents and automating phone calls using the Retell AI platform. It covers SDK integration, API best practices, and troubleshooting common telephony errors (like 401/429), enabling developers to build robust, real-world voice interaction systems.

This skill provides implementation patterns for integrating Retell AI into voice agents and telephony platforms. It enables the automation of complex phone calls and voice interactions using the Retell SDK, making it essential for building sophisticated AI customer service systems and automated call centers.

This skill provides comprehensive patterns and best practices for integrating Retell AI into voice agents and automated phone call systems. It covers the SDK usage, common error handling (like 401 Unauthorized or 429 Rate Limited), and necessary implementation steps to ensure robust and reliable telephony automation solutions.

This skill provides implementation patterns for building advanced voice agents and automating phone call workflows using the Retell AI platform. It covers SDK initialization, agent listing, and includes robust error handling for common telephony issues (e.g., unauthorized access, rate limiting), ensuring reliable integration into your enterprise applications.

This skill provides implementation patterns for achieving observability in AI voice agents and telephony platforms built with Retell AI. It guides users on how to monitor the performance, status, and interactions of automated voice agents and phone calls. It also includes essential guidance on common API error handling (e.g., 401 Unauthorized, 429 Rate Limited), ensuring robust and reliable system operation.