Skill UI

A comprehensive, production-ready TypeScript wrapper for interacting with the Vercel REST API. This client provides typed methods for managing deployments, projects, environment variables, and domains. It includes robust features like standardized error handling, exponential backoff retry logic, and structured request patterns, making programmatic integration with Vercel reliable and type-safe.

A specialized assistant designed to act as a senior software architect. It critiques code generated by AI tools or rapidly prototyped code, moving beyond simple syntax checks. The auditor identifies deep structural flaws, technical debt, architectural weaknesses, and production risks (e.g., security vulnerabilities, poor error handling, tight coupling) that make code fragile or unsuitable for deployment.

This comprehensive guide provides step-by-step instructions for integrating the Google Gemini API into applications using Python and Node.js. It covers the entire lifecycle, from basic text generation to advanced use cases like multimodal inputs (text + image), real-time streaming responses, function calling/tool use, and multi-turn chat. Includes best practices for model selection, error handling, and production deployment.

A comprehensive guide and skill for building robust, production-ready REST API endpoints. This skill covers full lifecycle development, including input validation, centralized error handling, authentication/authorization checks, CRUD operations, pagination, and generating proper API documentation. It ensures that back-end services are secure, scalable, and adhere to industry best practices.

This guide demonstrates how to implement clean and robust asynchronous pipelines in TypeScript using `TaskEither` from `fp-ts`. It solves the common problem of deeply nested, unmanageable `try/catch` blocks, allowing developers to compose sequential API calls and operations while correctly handling success and failure paths in a functional, immutable manner. Ideal for complex data fetching and workflow orchestration.

A comprehensive cheat sheet for the Either type in fp-ts, designed for robust error handling and flow control in TypeScript. It details how to use Either to manage synchronous operations that might fail, facilitating type-safe validation, fallible computations, and complex data chaining (e.g., replacing traditional try/catch blocks). Ideal for developers needing a quick, typed reference for functional programming patterns.

Learn how to handle errors in TypeScript using functional patterns like `Either` and `TaskEither`, eliminating messy try/catch blocks. This guide teaches that errors are simply data, allowing you to write cleaner, more predictable, and type-safe code that explicitly handles potential failures through explicit return values.

Master robust and type-safe patterns for React development using fp-ts. This guide covers managing state with Option, sophisticated form validation using Either (collecting all errors), and handling asynchronous data fetching with TaskEither. It promotes a highly functional, predictable coding style for React 18/19 and Next.js.

TaskEither is a functional programming utility from fp-ts designed for handling asynchronous operations that might fail. It provides a robust, type-safe mechanism for managing complex async workflows, such as chained API calls or promise wrappers. Instead of relying on traditional try/catch blocks where errors can be lost, TaskEither forces the developer to explicitly handle both success and failure paths, making error management predictable and composable.

A comprehensive guide for implementing reliable and resilient network communication in modern applications. Covers best practices for API requests, utilizing libraries like React Query/SWR for advanced data fetching, implementing robust error handling with retries and exponential backoff, managing authentication tokens, and ensuring offline-first support across various environments (including Expo Router loaders).

Guides modern Perl 5.36+ idioms and best practices for building robust, maintainable apps—covering signatures, dereferencing, Moo/roles, regexes and error handling—useful when writing, reviewing, refactoring, or migrating Perl code.

A comprehensive design specification detailing 98 rules for building Command Line Interface (CLI) tools that are safe, reliable, and easily consumable by AI agents. It covers structured JSON output formats, standardized error handling (to stderr), robust input contracts, safety guardrails (e.g., preventing path traversal), and tiered compliance levels (Agent-Friendly, Agent-Ready, Agent-Native) for seamless integration into AI workflows.