Skill UI

This guide provides comprehensive metrics, instrumentation code (TypeScript), health check logic, and alerting rules for monitoring a SaaS platform like Juicebox. It covers critical metrics such as search latency (p95), data ingestion rates, API error rates, and quota usage, ensuring the maintenance of an optimal user experience and data integrity. Use this to establish robust operational monitoring dashboards and alerts.

Helps Claude manage Kubernetes network policies and firewall rules by generating or updating YAML configurations based on infrastructure needs, ensuring secure pod-to-pod communication and compliance with least-privilege principles.

Memory provides persistent context across multiple coding sessions. It stores crucial project decisions, user preferences (like preferred package managers or specific libraries), and architectural conventions in a local memory file. This ensures that the AI maintains consistency, recalls past choices automatically, and applies project-specific rules without needing constant reminders, significantly improving workflow continuity and reliability.

Delivers end-to-end monitoring setups—structured logging, Prometheus/Grafana dashboards, alerting rules, distributed tracing, and load/profile testing—so teams can observe services, debug production issues, and plan capacity.

Documents setting up OpenTelemetry tracing/metrics, Prometheus/Grafana monitoring, structured logging, and alerting rules so teams can instrument services, track performance, and respond to incidents.

Guides writing Python code inside n8n Code nodes, covering recommended use cases, helper data access patterns, Beta vs Native modes, and webhook/data handling rules so you can safely script workflow transformations with Python.

Hands-on lab guide to configuring HTTP, HTTPS, SNMP, and SMB services for pentesting, covering setup, firewall rules, enumeration tools, and log inspection in controlled environments.

This skill implements a sophisticated, config-driven rules engine for OpenRouter API calls. It dynamically determines the optimal large language model (LLM) by evaluating a comprehensive context, including user subscription tier, remaining cost budget, specific task type (code, chat), required tools, and latency requirements. This is essential for building production-grade, cost-aware, and robust LLM orchestration systems.

Guides teams through automated performance baseline creation, offering templates, validation rules, and best-practice checklists for load, stress, and benchmarking workflows within performance testing efforts.

A comprehensive guide maintained by Supabase for optimizing Postgres performance. It provides critical rules and best practices across query performance, schema design, connection management, and security (including RLS). Use this skill when writing, reviewing, or optimizing complex SQL queries, database schemas, or configurations to ensure optimal database health and performance.

A comprehensive guide covering critical pitfalls and best practices for writing robust PowerShell scripts on Windows. It details essential rules for operator syntax (parentheses), safe path handling, null checking, advanced error management (try/catch), and correct JSON serialization, ensuring your automation code is highly reliable and maintainable.

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.