Skill UI

Automates Flask blueprint creation and related backend guidance, triggering whenever users request blueprint creator help or backend best practices; generates production-ready code, validations, and covers node.js/python/go microservices patterns.

This guide teaches developers how to achieve type-safe and predictable error handling in TypeScript using functional programming concepts from fp-ts, specifically focusing on the Either and TaskEither patterns. Instead of relying on disruptive try/catch exceptions, this pattern treats errors as explicit data values, forcing the calling code to handle failure paths, which is crucial for building robust, maintainable, and functional APIs and services.

Automate Freshservice ITSM workflows via Rube MCP and Composio toolkits, covering ticket search, creation, bulk updates, service requests, and email-triggered tickets while retrieving current schemas before each call.

This comprehensive workflow guides the entire lifecycle of building a complex full-stack feature, from initial architectural design to final integration testing. It enforces an API-first approach, ensuring that the database schema, backend services, and frontend components are developed cohesively, promoting scalability, testability, and adherence to modern industry best practices.

This skill guides the development of robust, security-focused full-stack web applications. It ensures layered security coverage across the entire application stack, from database queries (parameterized queries) to API endpoints (authentication/authorization) and frontend components (input validation, output encoding). Use this when building end-to-end features, microservices, or REST APIs that require simultaneous attention to Frontend, Backend, and Security best practices.

A comprehensive guide for deploying applications, especially those integrated with AI services like Gamma, to major cloud environments including Vercel, AWS, GCP, and GitHub. Covers best practices for secret management, environment variable configuration, and setting up CI/CD pipelines across various cloud architectures.

This guide sets up a robust local development workflow for integrating with the Gamma REST API. It includes a reusable client wrapper, a polling helper for handling asynchronous tasks, and a local mock server. Use this setup when building automation scripts, testing API calls offline, or simulating API responses without needing external service credentials or network connectivity.

A comprehensive guide for configuring API access across distinct environments (Development, Staging, Production) for SaaS services. It details architectural patterns for separating workspaces, managing environment-specific secrets (e.g., using AWS Secrets Manager), and implementing environment guards to ensure code behaves correctly and securely in different stages of deployment. Ideal for CI/CD pipeline setup and robust client-side configuration.

A comprehensive guide providing reference architectural patterns for building scalable, maintainable, and robust enterprise integrations involving Gamma services. It details three patterns—Basic, Service Layer, and Event-Driven—covering use cases from simple prototypes to high-volume, microservices-based systems, guiding system designers and architects on best practices.

This skill provides a comprehensive set of reusable, production-grade TypeScript patterns for interacting with the Gamma REST API, which lacks an official SDK. It includes implementation for typed clients, advanced polling mechanisms, structured error handling, and complex generation workflows (e.g., template-based generation). Use this when building robust, reliable applications that integrate deeply with Gamma's content generation services.

Guides building production-ready serverless apps on GCP with Cloud Run services and Functions, covering containerized APIs, event-driven Pub/Sub workflows, cold-start tuning, and deployment automation.

Provides automated guidance for Google Cloud Storage lifecycle policy scenarios, including pattern recommendations, production-ready configurations, and best practices for compute, storage, BigQuery, Vertex AI, and other GCP-specific services.