Skill UI

Automates common CTE query builder tasks for data analytics, offering best-practice guidance, production-ready SQL snippets, and validation against standard requirements when you need help with BI, visualization, or statistical analysis.

Guides you through building Dagster data pipelines with best-practice advice, ready-to-use configurations, and validation checks for ETL, transformation, orchestration, and streaming workflows whenever the Dagster pipeline creator skill is activated.

This skill provides production patterns for implementing robust data quality validation using Great Expectations, dbt tests, and formal data contracts. It is essential for building reliable, automated, and verifiable data pipelines, ensuring data integrity throughout the entire ETL/ELT process, and establishing clear data governance rules.

Guides building async FastAPI services with Pydantic V2 models, JWT auth, SQLAlchemy async, WebSocket, and OpenAPI, ensuring production-grade standards for REST endpoints, schema validation, and docs.

Automates guidance for API filtering query builder tasks, offering industry best practices, production-ready code, and validation across REST, GraphQL, OpenAPI, and authentication design patterns.

A comprehensive guide to implementing production-grade reliability patterns for web scraping pipelines using Firecrawl. Learn techniques such as circuit breakers, crawl fallbacks, content quality validation, and robust backoff strategies to handle timeouts, API failures, and inconsistent data quality, ensuring maximum uptime and data integrity.

This guide provides production-ready patterns for integrating the Firecrawl SDK in TypeScript and Python. It covers essential architectural best practices, such as implementing singleton clients, typed service wrappers, automatic retries with exponential backoff, concurrency management, and robust response validation using Zod. Ideal for building scalable, reliable web scraping services.

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 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.

Guides API developers through building well-structured GraphQL mutations with best-practice patterns, production-ready code, and validation help whenever “graphql mutation builder” appears in the request.

A comprehensive guide for implementing standardized, robust error handling in modern APIs. It utilizes RFC 7807 Problem Details, centralized middleware, and typed error classes to convert diverse failure types (validation, auth, database, upstream service) into consistent, machine-readable responses with correct HTTP status codes. Essential for building production-grade, resilient backends.

This document provides production-grade patterns for integrating with the Ideogram API in TypeScript and Python. It addresses common challenges in API usage, including implementing singleton client management, robust exponential backoff retry logic for rate limiting, and strict response validation using libraries like Zod. Use this guide when building reliable, scalable applications that depend on external AI image generation services.