Skill UI

Provides comprehensive expertise for building robust WordPress sites. Specializing in custom theme and plugin development, implementing modern Gutenberg blocks, extending WooCommerce functionality, and managing the WordPress REST API. Core focus areas include advanced security hardening (nonces, sanitization, capability checks), performance optimization (caching, query tuning), and adhering strictly to WordPress coding standards.

A comprehensive guide and methodology for identifying, measuring, and fixing performance bottlenecks across the entire stack. This skill covers optimizing database queries (e.g., N+1 problems, missing indexes), improving API efficiency (caching, parallel processing), and enhancing frontend performance (memoization, code splitting, image optimization). It emphasizes the critical 'Measure First' principle to ensure all optimizations are data-driven and effective.

A robust, production-grade REST API gateway designed to wrap ComfyUI servers. It provides comprehensive features including workflow templating, job queuing (with priorities), webhooks, result caching, authentication, and rate limiting. It transforms ComfyUI into a universal, scalable, and secure service for automated image generation pipelines.

A comprehensive guide detailing Next.js 16+ usage with Turbopack. Turbopack, written in Rust, is an incremental bundler designed to drastically speed up development startup and Hot Module Replacement (HMR) by utilizing file system caching. This document guides developers on best practices, differentiating between the default Turbopack usage and when to revert to Webpack for debugging or compatibility.

Guides security testers through identifying CDN caching layers, crafting deceptive static URLs, and exploiting path normalization and cache-key quirks to expose authenticated content via web cache deception attacks.

Guides authorized penetration testers through identifying caching layers, discovering unkeyed headers with Burp Suite and curl, and poisoning CDN or reverse proxy caches to assess cache-based vulnerabilities.

This utility ensures the prevention of silent floating-point mismatch bugs across all EVM chains. It covers runtime querying of decimals, chain-aware caching, handling precision drift in bridged tokens, and safe normalization for bots, dashboards, and DeFi tools. It is crucial for accurate cross-chain balance comparison.

This guide identifies and corrects the most common programming mistakes and anti-patterns when integrating with Adobe APIs. It covers critical topics such as migrating from deprecated JWT to OAuth, implementing token caching, handling asynchronous job polling, and secure file uploads. Essential reading for building robust, production-ready Adobe integrations.

A comprehensive guide for developers to optimize performance when integrating Adobe APIs (Firefly, PDF, Photoshop). Techniques covered include implementing token caching, parallelizing asynchronous job submissions, leveraging response caching (LRU), and optimizing connection handling to minimize latency and maximize throughput.

This comprehensive guide details advanced strategies for optimizing performance when interacting with blockchain data using the Alchemy SDK. Techniques covered include implementing TTL-based caching for data freshness, achieving parallel multi-chain fetches, utilizing batch API calls (e.g., NFT metadata) to reduce Compute Unit (CU) costs, and adopting WebSockets for real-time event subscriptions. Essential for scaling robust, low-latency dApps.

This skill provides actionable strategies and code patterns to significantly reduce Anima API costs. It focuses on optimizing usage through generation caching, detecting component changes for incremental generation, and implementing smart policies to minimize unnecessary API calls. Ideal for managing large-scale design-to-code workflows and controlling API budget.

This guide details advanced strategies for optimizing Claude API usage to reduce latency and cost. Techniques covered include prompt caching (significant cost reduction), strategic model selection (Haiku, Sonnet, Opus) based on task complexity, implementing streaming for perceived speed, and managing token limits. Use this guide when building high-performance, cost-efficient LLM applications that interact with large language models.