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.

Ray Train orchestrates distributed PyTorch, TensorFlow, and HuggingFace workloads from laptops to clusters, automating GPU placement, fault tolerance, checkpointing, and hyperparameter sweeps so teams can scale massive model training with minimal code changes.

This guide details essential patterns for building fault-tolerant and highly available Replit applications. It covers handling cold starts, implementing graceful shutdowns (via SIGTERM), and crucially, migrating persistent application state from ephemeral local filesystems to robust external storage (like Replit KV or Object Storage). Essential for production-grade deployments.

Execute controlled chaos experiments to proactively test system resilience, fault tolerance, and recovery capabilities. This toolkit simulates real-world failures, such as network latency, service crashes, resource exhaustion, and dependency outages, ensuring that microservices degrade gracefully and recover automatically. It is essential for modern distributed systems validation.

This guide details advanced patterns for building fault-tolerant Sentry integrations. It addresses critical real-world failure points, such as SDK initialization failures, network outages, and abrupt process shutdowns. By implementing graceful degradation, circuit breakers, and durable offline queuing, applications can ensure core functionality remains online and stable, even when the external error tracking service is unreachable or degraded.

A specialized methodology for reviewing APIs, configuration schemas, and interfaces to identify 'footguns'—design choices where the path of least resistance leads to insecurity. This analysis focuses on making secure usage the default, covering topics like dangerous defaults, primitive vs. semantic type safety, and insecure algorithm selection. It is crucial for architects and reviewers to prevent developer misuse at the design level.

This comprehensive guide outlines the best practices and structured methodology for developing specialized skills for AI coding agents. It provides detailed instructions on integrating Azure SDKs and Microsoft Foundry services, covering crucial topics like secure authentication patterns (DefaultAzureCredential), modular workflow design, and maintaining concise, effective knowledge context for optimal agent performance.

Master advanced techniques for building fault-tolerant applications integrated with Supabase. This guide implements critical resilience patterns, including circuit breakers, offline queuing (using IndexedDB), graceful degradation, exponential backoff retries, and dual-write strategies. Use this skill to handle network outages, API failures, and transient errors, ensuring your application remains highly available and reliable in production environments.

A guide to the Swift 6.2 concurrency model, which mandates default single-threaded execution to eliminate accidental data races. Learn how to use MainActor for isolated consistency and utilize the @concurrent attribute for explicitly offloading CPU-intensive tasks to background threads, ensuring safe and predictable application architecture.

A comprehensive checklist for deploying large-scale, production-ready GPU workloads on Vast.ai. It covers critical lifecycle stages including account authentication, instance validation, data encryption, persistent checkpointing strategies, fault tolerance for spot instances, robust monitoring (utilization & cost), and strict cost controls, ensuring reliable and stable ML/AI deployments.

This guide details the production-grade reference architecture for running complex GPU compute workflows on Vast.ai. It covers the foundational three-tier pattern—Orchestrator, GPU Workers, and Artifact Storage—providing blueprints for job queue management, fault-tolerant training pipelines, and robust checkpointing. Use this when designing scalable and reliable ML/AI training systems.

A comprehensive guide to implementing fault-tolerant patterns for Vercel serverless functions. Learn how to build resilient APIs using Circuit Breakers to handle external dependency failures, implementing Exponential Backoff for retries, and achieving Graceful Degradation by serving stale cache data. Essential for production-grade, highly available serverless architecture.