Skill UI

This comprehensive guide facilitates the development of a production-ready Minimum Viable Product (MVP) for a SaaS business. It covers the entire lifecycle, including idea validation, modern tech stack selection (Next.js, Supabase, Stripe), architectural design for multi-tenancy, database schema planning, authentication setup, payment integration, and a detailed pre-launch checklist for technical and marketing readiness.

This expert assists developers in building type-safe, high-performance database layers using Drizzle ORM with TypeScript. It provides deep knowledge of schema design, complex relational querying (joins, aggregations), managing migrations via Drizzle Kit, and seamless integration with modern stacks like Next.js, tRPC, and serverless environments. Ideal for developers needing a zero-overhead, type-safe ORM alternative.

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.

This guide outlines how to build new API connectors or providers that strictly adhere to the existing integration architecture and patterns of a target repository. It is designed for adding integrations while maintaining architectural consistency (e.g., layout, configuration schema, authentication model, and testing style), preventing the creation of novel, unsupported patterns.

Guidance for implementing Cloud Firestore in Flutter: selecting the database, configuring offline persistence, designing schemas, querying with filters and pagination, writing batched transactions, indexing, optimizing for scale, and avoiding write hotspots.

Use this skill to correctly set up Firebase Data Connect in Flutter apps. Covers schema and connector design, typed SDK generation, executing queries and mutations, real-time listeners, pagination, offline handling, error recovery, and security rules.

This comprehensive guide covers best practices for building robust database systems. It teaches crucial concepts like schema normalization, effective indexing strategies, selecting the right database (e.g., SQLite vs PostgreSQL) and ORM, and optimizing queries to prevent common performance issues like N+1 problems. It emphasizes making context-aware architectural decisions.

Analyzes PostgreSQL snippets or entire projects to flag anti-patterns, enforce best practices, optimize JSONB/array usage, schema design, custom types, triggers, extensions, and security policies like RLS.

A comprehensive expert prompt for evaluating Power BI data models. It guides the assessment of model architecture (Star Schema, relationships), storage optimization (Import, DirectQuery, Dual), data quality, performance scalability, and governance best practices. Use this when reviewing an existing BI model to ensure it is robust, maintainable, and highly performant.

A universal assistant for conducting deep code reviews across major SQL databases (MySQL, PostgreSQL, SQL Server, Oracle). It rigorously analyzes SQL code for critical vulnerabilities (like SQL injection), performance bottlenecks, maintainability issues, and adherence to industry best practices. Features include security audits, index optimization recommendations, query structure analysis, and schema design validation, ensuring robust and secure database development.

This skill is designed for understanding complex database structures. It allows users to list all available tables, intelligently filter relevant tables based on natural language queries, retrieve detailed schemas (columns, types, primary/foreign keys), and map inter-table relationships. It culminates in generating the M-Schema format, which is essential for accurately preparing the context needed for advanced SQL query generation.

A powerful pre-action hook designed to enforce rigorous checks before AI agents modify code, create files, or execute destructive commands. Instead of relying on superficial self-evaluation, GateGuard forces the LLM to gather concrete facts (like dependencies, data schemas, and impacted modules), significantly improving code reliability and architectural depth. It provides multiple gates for edits, writes, and destructive bash commands.