Skill UI

This guide implements comprehensive guardrails for Perplexity Sonar API. It covers crucial aspects like query content moderation, PII sanitization, enforcing user usage quotas, selecting appropriate models based on user tiers, and scoring citation reliability to ensure safe, ethical, and cost-effective AI usage.

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.

This skill specializes in designing, optimizing, and evaluating prompts for Large Language Models (LLMs). It allows users to generate optimized prompt templates, structured output schemas (e.g., JSON mode, function calling), and comprehensive test suites. Use this skill when building new LLM applications, implementing complex reasoning patterns like Chain-of-Thought, defining system guardrails, or building robust evaluation frameworks to measure and improve model performance.

Pyvene is a declarative framework designed for performing causal interventions on PyTorch neural networks. It allows researchers to conduct advanced experiments such as activation patching, causal tracing (ROME-style), and interchange intervention training (IIT). Use this library when you need to test causal hypotheses about model behavior, deeply interpret model components, or ensure reproducibility in advanced AI research.

This comprehensive workflow guides the end-to-end implementation of Retrieval-Augmented Generation (RAG) systems. It covers critical stages, including selecting optimal embedding models, setting up vector databases, designing chunking strategies, implementing hybrid retrieval, integrating Large Language Models (LLMs), and performing rigorous evaluation. Use this when building knowledge-grounded AI applications or advanced semantic search systems.

Automate Replicate AI workflows via the Composio MCP integration to inspect model schemas, run predictions (sync or async), upload input files, list versions, and manage prediction history and related assets.

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's advanced voice agent and telephony features. It is designed for developers building automated systems that require handling complex voice interactions, making scheduled phone calls, or managing conversational AI workflows within a CI/CD or automation environment.

This workflow guides users through building advanced AI voice agents using the Retell AI SDK. It demonstrates how to implement complex call flows, integrate function calling for tasks like booking appointments, and configure voice behavior for professional telephony applications.

This skill provides implementation patterns for cost tuning within the Retell AI platform, specifically tailored for voice agents and complex telephony workflows. It helps developers build, monitor, and optimize the operational costs of AI-powered voice automation systems, ensuring efficient resource management when integrating real-time voice services.

This bundle provides implementation patterns and debugging utilities for AI voice agents and telephony functions using Retell AI. It is ideal for developers integrating voice-based conversational AI into phone call systems, customer service platforms, or any telephony workflow, covering SDK usage and common error handling.