Skill UI

A comprehensive guide to building enterprise-grade AI applications using Firebase Genkit. This skill covers the full development lifecycle, including implementing advanced RAG systems, multi-step tool-calling agents, and complex workflows across Node.js, Python, and Go. It teaches best practices for schema validation (Zod/Pydantic), robust error handling, OpenTelemetry tracing, and deploying reliably to Firebase Functions or Cloud Run.

Automates GraphQL resolver creation for backend teams, giving step-by-step guidance, industry best practices, and production-ready code/configs to validate resolvers across Node.js, Python, Go, database design, caching, messaging, and microservices.

Automatically assists backend developers with gRPC service generation, offering best-practice patterns, multi-language scaffolds for Node.js, Python, Go, and validating configs for databases, caching, and messaging to stay production-ready.

This guide provides expert patterns and code templates for integrating with the HubSpot CRM API. It covers secure authentication methods, including OAuth 2.0 for public apps and Private App Tokens for single-account use. Topics include CRUD operations on CRM objects, handling associations, webhooks, and batch processing. Implementation examples are provided for both Node.js and Python SDKs.

This skill specializes in writing, debugging, and refactoring modern JavaScript code (ES2023+). It covers advanced patterns like async/await, Promise handling, ESM modules, and optimal performance for both browser and Node.js environments. Use it for vanilla applications, complex async flows, and performance optimization.

This skill acts as an expert architect for scaffolding robust, production-ready applications using TypeScript. It generates complete, structured project templates for various types, including Next.js full-stack apps, React/Vite SPAs, and Node.js APIs. It incorporates modern tooling like pnpm, Vite, and Next.js features, ensuring type safety, proper dependency management, comprehensive testing setups, and adherence to current industry best practices. Use it to accelerate project setup and maintain scalable architecture.

Automated backend guidance for Kafka producer and consumer workflows, offering best practices, code samples, and configuration help for Node.js, Python, Go, databases, caching, messaging, and microservices scenarios.

A comprehensive guide for developers on utilizing the Kling AI Text-to-Video REST API. This skill provides minimal working examples in Python and Node.js, demonstrating the complete workflow: submitting a text prompt, polling the task status, and retrieving the final generated video URL. Ideal for integrating advanced AI video generation capabilities into development projects.

A comprehensive guide detailing how to set up secure API authentication for Kling AI using JSON Web Tokens (JWT). This process involves generating tokens from your Access Key (AK) and Secret Key (SK) credentials, providing code examples for Python and Node.js, and implementing best practices for token management and refreshing to ensure continuous API access.

This skill provides a comprehensive guide for deploying LangChain chains and agents into production-grade APIs. It supports multiple development stacks, including Python (LangServe/FastAPI) and Node.js (Express). Topics covered include containerization using Docker, setting up robust health checks, and deploying to major cloud platforms like Google Cloud Run and AWS Lambda. Learn how to build scalable, observable, and enterprise-grade API endpoints for advanced LLM applications.

Provides comprehensive guides for installing the LangChain SDK in both Python and Node.js environments. Covers the necessary steps to set up core packages and securely configure API keys for major LLM providers like OpenAI, Anthropic, and Google, enabling immediate development of complex AI applications.

This bundle collects comprehensive diagnostic evidence for Langfuse support tickets and troubleshooting. It gathers system environment details (Node.js, Python, OS), installed SDK versions, API connectivity status, and redacted application logs. Use this when encountering persistent or complex Langfuse issues to provide full context to support teams.