Skill UI

This skill provides comprehensive guidance on implementing high-performance, non-blocking applications in Python. It utilizes asyncio, async/await, and advanced concurrency patterns to handle I/O-bound workloads efficiently. Ideal for building async web APIs, real-time systems, and concurrent data processing.

A comprehensive Java SDK client for securely and efficiently sending custom application logs to Azure Monitor. It utilizes Data Collection Rules (DCR) and Data Collection Endpoints (DCE) to manage the ingestion process. The client supports batch uploads, concurrent processing, and robust error handling, making it ideal for integrating custom logging into Azure Log Analytics workspaces.

A comprehensive guide and implementation pattern for managing rate limits, throttling, and ensuring idempotency when interacting with the Databricks API. This skill demonstrates techniques like exponential backoff with jitter, token-bucket rate limiting, and using idempotency tokens to make bulk and concurrent API calls reliable and efficient, minimizing HTTP 429 errors.

Dispatch parallel agents to tackle independent failures concurrently so each investigator stays focused on one domain; review their summaries, check for conflicts, and run the full suite to integrate fixes faster than sequential debugging.

This skill provides expert guidance for building robust, scalable, and fault-tolerant applications using Elixir. It specializes in mastering OTP patterns, Phoenix LiveView, concurrent programming, and distributed system design. Use it for complex Elixir development tasks requiring best practices and high reliability.

A comprehensive guide to production-grade concurrency patterns in Go. It details the proper use of goroutines, channels, sync primitives, and context management. Use this skill when building highly concurrent applications, implementing worker pools, managing goroutine lifecycles, or debugging race conditions in Go.

Comprehensive Go guidance for browser automation and web scraping using Rod’s direct Chrome DevTools Protocol integration plus stealth anti-detection patterns, ideal for headless SPA testing, concurrent scraping, and request interception.

This skill simulates a senior Go developer, specializing in writing high-performance, production-grade Go applications. It covers advanced topics like concurrent programming using goroutines and channels, designing robust microservices with gRPC/REST, and optimizing code using pprof and benchmarks. It enforces idiomatic Go patterns, context propagation, and strict testing practices (table-driven, fuzzing, race detection).

This comprehensive guide outlines essential, production-grade patterns for building robust LangChain applications. Learn best practices for implementing type-safe structured output using Zod, handling provider fallbacks, processing inputs concurrently with batching, optimizing user experience with streaming, and ensuring reliability through caching and retries.

A comprehensive client library pattern for interacting with rate-limited APIs. This solution implements exponential backoff, robust retry logic (handling 429/5xx errors), concurrent request queuing, and cursor-based pagination. It ensures high throughput and reliable data fetching without exceeding API limits.

This guide provides advanced techniques for optimizing Mistral AI API usage, focusing on latency reduction and throughput maximization. Learn to implement robust caching strategies, utilize streaming responses (SSE), select the optimal model for specific use cases (e.g., Mistral Small for chat, Codestral for code), manage concurrent requests using rate limiting, and efficiently use the Batch API. Essential for building high-performance, production-grade AI applications.

This guide provides production-ready patterns for integrating the Mistral AI SDK in both TypeScript and Python. It covers essential best practices such as singleton client management, handling structured JSON output, implementing streaming responses, concurrent asynchronous processing, and robust error handling with exponential backoff. Use this when establishing team coding standards or refactoring Mistral API usage.