Skill UI

An expert guide for designing, setting up, and optimizing large-scale monorepositories. Masters advanced build systems like Nx, Turborepo, Bazel, and Lerna to manage complex dependency graphs, implement efficient build caching (local and remote), and optimize slow CI/CD pipelines for multi-project development workflows.

This guide details advanced architectural patterns for building complex AI systems using multiple Language Model instances (agents). It addresses the single-agent context bottleneck by distributing work, enabling parallelism, and facilitating specialization across different subtasks. Key patterns include Supervisor/Orchestrator, Peer-to-Peer/Swarm, and Hierarchical designs, providing guidelines for context isolation and effective coordination protocols.

This comprehensive workflow guides the process of building and deploying new features that must maintain consistent functionality and design across multiple operating systems (web, mobile, and desktop). It strictly follows an API-first architecture, starting with defining shared API contracts (OpenAPI/GraphQL) and a unified design system. The process then guides parallel implementation across specialized stacks like React/Next.js, SwiftUI, and Jetpack Compose, ensuring high code consistency and faster time-to-market.

Guides building and selling Notion templates as sustainable digital product businesses, covering template design, pricing tiers, marketplaces like Gumroad or Lemon Squeezy, marketing tactics, and scaling support systems.

Design comprehensive observability strategies for production systems, covering SLI/SLO/SLA frameworks, dashboards, alert tuning, runbooks, and incident response workflows that align metrics, logs, and traces across reliability pillars.

Expert skill in designing and implementing comprehensive observability stacks for enterprise microservices. Covers advanced monitoring (Prometheus, Grafana), distributed tracing (Jaeger, OpenTelemetry), centralized logging (ELK, Splunk), and defining SLIs/SLOs. Ideal for ensuring high availability, mastering incident response, and performing reliability engineering on large-scale, distributed applications.

This skill specializes in implementing Service Level Objectives (SLOs) and Service Level Indicators (SLIs) to enforce reliability standards. It guides users in designing comprehensive SLO frameworks, calculating error budgets, and building robust monitoring systems. Use this when you need to establish data-driven reliability targets, balance feature velocity with technical health, and align service performance directly with core business objectives.

This module provides minimal working examples for the OpenEvidence API, designed for clinical intelligence applications. Users can perform complex clinical queries to get treatment recommendations, check potential drug interactions based on patient context, and search for authoritative medical guidelines from major health organizations (e.g., ACC/AHA). Ideal for developing evidence-based healthcare decision support systems.

This comprehensive reference architecture guides the integration of OpenEvidence into clinical decision support systems (CDS). It is designed for healthcare platforms needing evidence-based query processing, deterministic citation tracking, and full audit logging. Key considerations include HIPAA compliance, optimizing for sub-second response times in point-of-care workflows, and establishing robust data models and event pipelines.

A comprehensive technical audit checklist designed to validate the production readiness of OpenRouter API integrations. It covers critical areas including security practices (e.g., secret management, key rotation), system reliability (e.g., fallbacks, retries, timeouts), and observability requirements (e.g., structured logging, cost tracking). Use this guide before launching or during major operational reviews to ensure robust, scalable, and compliant deployments.

This skill provides comprehensive reference architectures for building production-grade AI applications using OpenRouter as a unified LLM gateway. It covers three scaling patterns: simple single-service, standard microservice (with caching and budget enforcement), and enterprise event-driven systems (with message queues and workers). Use this when planning system design, reviewing complex AI pipelines, or ensuring scalability and cost control in your LLM integration.

A comprehensive workflow bundle designed for system administrators and developers. It guides users through the entire lifecycle of system management and shell scripting across Linux, macOS, and Windows. Key phases include environment assessment, writing robust Bash scripts, advanced debugging techniques, linting with ShellCheck, and rigorous test automation using Bats, ensuring scalable and reliable automation.