Skill UI

This skill provides comprehensive, step-by-step guidance for developers to set up complete development environments from scratch. It covers dependency identification, platform-specific installation (Mac, Linux, Windows), configuration of environment variables, and verification steps for tools like Node.js, Python, and Docker. Ideal for starting new projects, onboarding team members, or troubleshooting setup issues.

This skill is designed for advanced error detective work. It analyzes complex log streams, correlates stack traces across distributed systems, and detects anomalous error patterns. It helps identify root causes by correlating errors with system deployments and changes, providing actionable findings for immediate fixes and long-term prevention strategies.

Provides detailed evaluation of machine learning models using plugin metrics to assess accuracy, precision, recall, F1, and other key indicators, helping users compare models and validate performance before deployment.

This skill provides a comprehensive guide to setting up Continuous Integration and Continuous Deployment (CI/CD) pipelines for applications integrating with Evernote. It covers advanced topics such as creating mock clients for deterministic unit testing, managing sandbox integration tests using GitHub Secrets, and structuring robust automated deployment workflows. Use this when ensuring code quality and reliable deployment for Evernote-based SaaS products.

Comprehensive guide for deploying Evernote integrations to robust production environments across major cloud platforms (AWS, GCP, Kubernetes). It covers advanced practices including multi-stage Docker builds, secure secrets management via Secret Manager, serverless function setup (Lambda), and configuring necessary health checks and CI/CD pipelines. Ideal for transitioning from testing to live deployment.

A comprehensive checklist for ensuring any Evernote integration is production-ready. It covers critical operational aspects including API key management, security hardening (encryption, sanitization), rate limit handling, robust monitoring setup, and post-deployment verification steps. Use this guide when deploying or auditing a live Evernote connection.

This guide details setting up robust Continuous Integration and Continuous Deployment (CI/CD) pipelines for Exa integrations. It covers unit tests, live integration testing using GitHub Actions secrets, and health checks. Use this when automating testing workflows or ensuring Exa API connectivity during builds and release gates.

A comprehensive guide for deploying Exa-powered search APIs to production environments. This skill covers best practices for integrating Exa into Vercel Edge Functions, Docker containers, and Google Cloud Run. It includes detailed steps for secret management, implementing Redis caching for performance, and setting up health check endpoints, ensuring a robust and scalable production search solution.

This guide details how to configure and manage the Exa search API across development, staging, and production environments. It ensures API key isolation, implements environment-specific search limits, and provides strategies for cost control and performance optimization by utilizing caching layers like Redis. Essential for scalable, multi-stage deployments.

A comprehensive checklist guiding the full lifecycle deployment of Exa search integrations to a production environment. It covers critical stages including pre-deployment security audits, code quality verification, performance baselines, monitoring setup, and emergency rollback procedures. Use this guide when preparing for a major launch or verifying production readiness.

This skill provides comprehensive guidance on deploying Expo applications to production across multiple platforms, including iOS, Android, and Web, utilizing Expo Application Services (EAS). It covers the full lifecycle, from building and configuring profiles (eas.json) to submitting builds to App Store Connect, Google Play, and TestFlight. Furthermore, it details deploying web bundles, managing app versions, optimizing App Store metadata, and implementing automated CI/CD workflows.

Automates FastAPI ML endpoint deployments by guiding architecture, producing production-ready code, and validating serving configs when you invoke the ML deployment skill.