Skill UI

tRPC allows developers to build highly type-safe APIs in a full-stack environment without needing to write or maintain separate schemas. It provides end-to-end type safety from server routers to client components, supporting queries, mutations, subscriptions, and robust middleware. Ideal for building modern TypeScript monorepos, Next.js, or Remix applications.

This guide provides comprehensive patterns for designing and implementing secure multi-tenant SaaS applications using shared databases. It covers critical techniques like Row-Level Security (RLS) in PostgreSQL, implementing tenant-aware middleware in TypeScript (Express/Fastify), and enforcing data scoping at the ORM level (Prisma/Drizzle). Learn how to prevent data leakage and manage tenant isolation across shared schemas.

Transformers.js allows developers to run state-of-the-art machine learning models directly within JavaScript/TypeScript environments (browser, Node.js). It supports a comprehensive range of tasks including NLP (text generation, summarization, translation), Computer Vision (image classification, object detection), and Audio processing. This capability enables building fully client-side, multimodal AI applications without requiring a dedicated Python backend or server infrastructure.

This guide addresses the critical difference between standard Node.js SHA3-256 and the Keccak-256 hash function required by Ethereum. Using the wrong hash function can silently corrupt calculated function selectors, event topics, storage slots, and derived addresses. It provides best practices and code examples for using dedicated libraries (like ethers, viem, web3.js) to ensure correct, Ethereum-compliant hashing in JavaScript and TypeScript environments.

This comprehensive guide outlines best practices for writing robust JavaScript and TypeScript unit tests using the Jest framework. It covers essential topics including proper test structure, effective dependency mocking strategies (using jest.mock and jest.spyOn), handling asynchronous code, implementing snapshot testing, and modern techniques for testing React components with React Testing Library. It also provides a detailed reference of common Jest matchers.

This tool automates the creation of robust Playwright end-to-end tests. Given a detailed user scenario, it executes a multi-step process using the Playwright MCP, generating high-quality, executable Playwright TypeScript code. It saves the test file and automatically runs and iterates the test until it passes, ensuring comprehensive functional coverage and reliable validation.

An advanced, AI-powered agent designed to generate comprehensive and workable unit tests for virtually any programming language (e.g., Python, C#, Java, Go, TypeScript). It operates via a sophisticated multi-agent pipeline (Research → Plan → Implement → Build → Test → Fix → Lint). This ensures generated tests are not only written following project conventions but also compile, pass all tests, and effectively improve code coverage.

This skill guides the integration of Arize AX tracing into existing applications, especially complex LLM applications. It follows a structured, two-phase, agent-assisted flow: first analyzing the codebase (read-only) to understand dependencies, and then implementing instrumentation. It supports multi-language environments (Python, TypeScript, Java, Go) and integrates deeply with OpenTelemetry and various LLM providers, ensuring comprehensive observability without altering core business logic.

Generates a structured Epic architecture specification from a Product Requirements Document, guiding senior architects to capture high-level approaches, layered Mermeid diagrams, features, technical enablers, stack choices, and value estimates for TypeScript/Next.js monorepo projects.

Generates customizable prompts to scan a codebase, identify high-quality exemplars, and document patterns across languages (.NET, Java, JavaScript, TypeScript, React, Angular, Python) with configurable depth, categorization, and documentation formats.

Build agentic applications by embedding GitHub Copilot workflows into apps using Python, TypeScript, Go, or .NET. Create custom tools, manage sessions, and implement streaming responses without building orchestration logic. Ideal for developers integrating AI agents into existing software.

Phoenix Evals enables developers to build and run evaluators for AI and LLM applications. It supports both code-based and LLM-based evaluation methods, including RAG systems. Users can create datasets, run experiments, validate evaluator accuracy, and implement production guardrails. Available in Python and TypeScript, it helps ensure model performance and reliability through systematic testing and human validation.