Skill UI

This skill provides comprehensive guidance for building scalable, maintainable, and production-ready backend applications using Node.js. It covers modern architectural patterns, best practices, and implementation details for REST APIs, GraphQL servers, microservices, authentication, real-time WebSockets, and complex database integrations (SQL/NoSQL).

A comprehensive toolkit for implementing production-grade observability in Obsidian plugins. It provides structured logging with log levels and history, a robust metrics collector (supporting counters, gauges, and timers for performance tracking), and an error tracker with deduplication. Users can build a real-time debug panel to monitor plugin performance, diagnose issues, and set up comprehensive error reporting.

This guide provides comprehensive documentation and code examples for integrating OpenEvidence webhooks. Learn how to subscribe to real-time events (e.g., query completion, evidence updates, new citations) to keep clinical decision support systems fresh. Use these webhooks to trigger downstream audit workflows and ensure immediate data synchronization.

This skill provides comprehensive tools for implementing granular cost controls for OpenRouter API usage. It allows users to set hard spending caps via per-key limits, track real-time credit balances, and implement client-side middleware for budget enforcement. Use cases include preventing unexpected overspending, managing multi-key environments, and optimizing API calls using cost-saving model variants.

This skill provides comprehensive tools to monitor the real-time availability, status, and performance of various large language models (LLMs) accessed via OpenRouter. It supports active health probes, catalog checks, and scheduled monitoring via Python and Bash, ensuring robust fault detection and failover logic for production systems that rely on external AI APIs.

This skill provides comprehensive best practices and Python code examples for optimizing API calls to Large Language Models (LLMs) via OpenRouter. It covers benchmarking model latency, implementing streaming to achieve low Time-To-First-Token (TTFT), and processing requests concurrently using asynchronous programming (asyncio). Use this when developing real-time, high-throughput applications that require predictable and fast model responses.

This skill provides comprehensive guides on implementing real-time streaming responses using OpenRouter's Server-Sent Events (SSE) capability. It covers various languages (Python, TypeScript, FastAPI, JavaScript), enabling developers to build modern, fast-feeling chat interfaces. By streaming tokens as they are generated, it significantly reduces the time-to-first-token (TTFT) and improves user experience for long completions and chat applications.

A comprehensive utility designed to monitor, analyze, and predict blockchain gas costs across multiple major chains (e.g., Ethereum, Polygon, Arbitrum). It fetches real-time pricing, estimates transaction costs in both ETH and USD, and identifies the optimal time windows for executing DeFi or NFT transactions, ensuring maximum cost efficiency.

This workflow executes a single-query search using the Perplexity Sonar model, ensuring all returned answers are web-grounded and accompanied by inline citations. It is essential for building high-trust applications, fact-checking tools, or any system requiring verifiable, real-time information retrieval. The process includes API interaction, detailed response parsing (answer, citations, search results), and formatting the output into a usable, source-linked format.

Provides a minimal working example demonstrating how to integrate Perplexity's API for web-grounded answers. Learn to perform advanced searches, retrieve cited sources, and handle features like domain filtering and streaming using both TypeScript and Python. Ideal for integrating real-time, source-backed search functionality into applications.

This skill enables the development of robust, event-driven pipelines interacting with the Perplexity Sonar API. Since Perplexity lacks native webhooks, this system simulates event patterns using advanced techniques: Streaming Server-Sent Events (SSE) for real-time data, job queues (like BullMQ/SQS) for asynchronous batch research, and cron-triggered monitoring for scheduled news and trend alerts. It provides patterns for building complex, reliable, and scalable AI applications.

Phoenix is an open-source observability platform for tracing, evaluating, and monitoring LLM applications; use it to debug prompts, run dataset evaluations, build experiment pipelines, and watch production inference in real time.