Skill UI

Aggregates performance metrics from applications, systems, databases, caches, queues, and external services into a central platform, helping teams define a consistent taxonomy, choose aggregation tools, and create dashboards and alerts for faster monitoring and troubleshooting.

Automates guidance for caching API responses within integrations, offering best-practice patterns, webhook and SDK support, and production-ready validation when users ask about api response cacher.

Expert in developing complete, high-performance firmware and peripheral drivers for ARM Cortex-M platforms (STM32, Teensy, nRF52). Specializes in low-level hardware interaction, including implementing drivers for I²C, SPI, UART, and managing complex concurrency patterns using FreeRTOS/Zephyr. Provides critical guidance on optimizing for determinism, cache coherency, DMA transfers, and memory barriers to ensure reliable embedded system operation.

This SDK provides full management plane capabilities for Azure Cache for Redis within the .NET ecosystem. It allows developers to provision, configure, and manage various aspects of the Redis resource using the Azure Resource Manager (ARM) framework. Key functions include creating and updating cache instances, managing network firewall rules, rotating access keys, scheduling patch updates, and performing data import/export operations.

A comprehensive guide to modern backend development patterns for scalable server-side applications using Node.js, Express, and Next.js. Covers API design (RESTful), data access patterns (Repository, Service Layer), database optimization (N+1 prevention, Transactions), and advanced caching strategies (Redis, Cache-Aside). Essential for building robust, high-performance APIs.

A comprehensive guide to enhancing Clerk authentication performance within a Next.js application. Techniques covered include optimizing middleware routing, implementing React's cache() and unstable_cache() for data deduplication, optimizing token handling, utilizing dynamic imports for lazy loading, and leveraging Edge Runtime for faster cold starts. Ideal for reducing latency and improving user experience during authentication flows.

Implements a robust caching strategy by using the SHA-256 hash of file content as the primary key, instead of the file path. This pattern is essential for high-cost file processing pipelines (like OCR, PDF parsing) because it ensures cache hits survive file renaming or moving, while still automatically invalidating entries when the content changes. It adheres to the Single Responsibility Principle (SRP) by isolating caching logic into a service layer wrapper.

This skill details advanced techniques for enhancing the effective context capacity of LLMs. It outlines four core strategies—Compaction, Observation Masking, KV-Cache Optimization, and Context Partitioning—to prevent context limits from constraining task complexity. By strategically managing token usage, users can reduce operational costs, minimize latency, and implement robust, long-running agent systems for handling large documents or conversations.

A comprehensive guide to diagnosing and resolving common codebase indexing issues in Cursor IDE. Covers problems like stuck indexing, empty search results (@Codebase), stale data, and performance bottlenecks in large monorepos. Includes steps for cache clearing, configuration management, and optimizing resource limits.

Detects memory usage issues by scanning code for common leak patterns, such as lingering listeners, closures holding references, or runaway caches, and provides recommendations to fix them before performance degrades.

Guides AWS users through Elasticache configuration workflows, offering best-practice patterns, production-ready snippets, and validation checks whenever “elasticache config” is mentioned.

A comprehensive guide to implementing Exa neural search at various scales. This resource details three distinct architectures—Direct Search, Cached Search, and full RAG Pipeline—allowing developers to choose the optimal blueprint based on traffic volume, required latency, and complexity. Use this when designing scalable Exa integrations, from simple UI search to advanced AI-powered research agents.