Skill UI

Enables Claude to examine EXPLAIN plans, spot slow queries or missing indexes, and deliver concrete optimization steps—ideal for improving database query speed and resource usage when performance issues arise.

A comprehensive expert resource for building high-performance, async-first APIs using FastAPI, SQLAlchemy 2.0, and Pydantic V2. Covers advanced topics like microservices architecture, WebSockets, advanced security (OAuth2, RBAC), robust testing strategies (pytest), performance optimization, and modern DevOps practices (Docker, Kubernetes). Ideal for mastering scalable, production-ready Python web services.

A comprehensive guide and expert resource for mastering Julia 1.10+ development. Covers advanced topics including multiple dispatch, performance optimization, type stability, and modern tooling. Ideal for building production-ready applications in scientific computing, machine learning, data science, and web development.

A comprehensive optimization guide maintained by Vercel, covering 45 rules for React and Next.js. Use this resource when building new components, implementing complex data fetching (client or server-side), refactoring code, or systematically improving bundle size and overall load times for high-performance web applications.

This comprehensive guide provides a detailed framework for optimizing spending across Replit services. Learn how to conduct a thorough billing audit, right-size deployment resources (Autoscale vs. Reserved VM), manage data egress costs, optimize team seat licenses, and clean up idle resources. Essential for controlling cloud spending and ensuring efficient resource allocation in any development workflow.

Master advanced techniques for scaling Supabase projects to handle high production load. This skill covers six critical layers: implementing read replicas for analytics, optimizing connectivity using connection pooling (Supavisor), upgrading compute resources, utilizing CDN caching for Storage, deploying Edge Functions regionally, and performing database table partitioning. Ideal for engineers preparing for traffic spikes or optimizing large-scale database architectures.

Analyzes IaC files and live Azure resources to validate SKUs, usage, and costs, then generates GitHub issues (one per recommendation plus an EPIC) so teams can track implementation of prioritized savings.

This guide provides techniques to significantly boost performance when integrating with the Hex API. It covers essential optimization strategies such as implementing LRU caching for project lists, running multiple independent jobs in parallel, and adopting adaptive polling intervals to manage API latency and resource usage effectively. Use this when facing slow data retrieval or requiring high-throughput data processing.

This skill guides the setup and configuration of the CAST AI Workload Autoscaler, enabling dynamic right-sizing of pod resource requests (CPU and Memory). It automatically adjusts resources based on actual usage, significantly reducing cloud over-provisioning costs. Use this guide to implement workload scaling policies, configure resource limits via Kubernetes annotations, and monitor scaling events in Kubernetes environments.

A set of production-ready wrappers and patterns for interacting with the CAST AI REST API. This package provides robust client implementations in TypeScript and Python, handling critical features such as typed responses, singleton management, automatic retry logic with backoff, and managing cloud resources (e.g., Kubernetes clusters, node optimization). Use it when building reusable, reliable clients for cost optimization and infrastructure management.

This skill provides comprehensive strategies to optimize resource usage and mitigate rate limits when integrating with the OneNote Graph API. It focuses on reducing the operational 'cost' of making API calls by implementing metadata caching (TTL), utilizing JSON batch requests, and performing delta synchronization instead of full list fetches. Essential for high-volume, stable OneNote enterprise integrations.

A comprehensive guide for diagnosing and reducing high memory consumption in Qdrant. This resource is crucial when dealing with reports of 'memory leak', 'out of memory', or excessive RAM growth. It details how Qdrant manages resident memory and OS page cache, and provides advanced techniques such as quantization, using float16/int8 data types, and strategically storing components on disk (like HNSW indexes or sparse vectors) to minimize the memory footprint and ensure stable, scalable operation.