Skill UI

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.

A comprehensive technical audit checklist designed to validate the production readiness of OpenRouter API integrations. It covers critical areas including security practices (e.g., secret management, key rotation), system reliability (e.g., fallbacks, retries, timeouts), and observability requirements (e.g., structured logging, cost tracking). Use this guide before launching or during major operational reviews to ensure robust, scalable, and compliant deployments.

This skill provides comprehensive reference architectures for building production-grade AI applications using OpenRouter as a unified LLM gateway. It covers three scaling patterns: simple single-service, standard microservice (with caching and budget enforcement), and enterprise event-driven systems (with message queues and workers). Use this when planning system design, reviewing complex AI pipelines, or ensuring scalability and cost control in your LLM integration.

A comprehensive workflow bundle designed for system administrators and developers. It guides users through the entire lifecycle of system management and shell scripting across Linux, macOS, and Windows. Key phases include environment assessment, writing robust Bash scripts, advanced debugging techniques, linting with ShellCheck, and rigorous test automation using Bats, ensuring scalable and reliable automation.

Guide for automating Payhip workflows through Composio’s toolkit by first discovering available tools, ensuring the Rube MCP connection is active, and executing schema-compliant operations with RUBE_MULTI_EXECUTE_TOOL.

Use Rube MCP to run Composio's PDF co toolkit: discover current tool schemas, ensure connections, and execute RUBE_MULTI_EXECUTE_TOOL sequences for reliable PDF collaboration automation.

This guide provides a comprehensive overview of PDF manipulation techniques using both Python libraries (like pypdf, pdfplumber, reportlab) and command-line tools (like pdftotext, qpdf). It covers core functionalities such as merging, splitting, rotating, extracting text and structured tables, creating new PDFs, and performing OCR on scanned documents, making it essential for automation workflows.

Automates Peopledatalabs tasks through Composio’s Rube MCP toolkit by discovering current tool schemas, ensuring active connections, and executing schema-compliant workflows.

This skill simulates an expert performance engineer capable of diagnosing complex bottlenecks across the entire technology stack. It covers modern observability techniques (OpenTelemetry, APM), advanced profiling (CPU, Memory, I/O), and sophisticated load testing strategies (k6, Chaos Engineering). Use this when optimizing scalability, reducing latency, improving throughput, or architecting highly reliable, performant distributed systems in cloud environments.

Guides engineers through bundle analysis, lazy loading, image optimization, caching headers, virtualized lists, and Core Web Vitals monitoring so web apps stay fast and scalable for production traffic.

This skill provides comprehensive patterns for building highly reliable applications that interact with external Large Language Model (LLM) APIs, such as Perplexity Sonar. It implements critical reliability techniques including Circuit Breakers, Model Fallback, Streaming Timeout handling, and Caching layers. Use this to ensure your application maintains high availability and graceful degradation even when external services experience intermittent failures or variable performance.

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.