Skill UI

This skill guides the setup of a robust, multi-environment architecture for Supabase, ensuring complete data isolation between development, staging, and production projects. It covers best practices for managing separate environment-specific secrets, safe database migration promotion (db push), environment-aware client initialization, and implementing safeguards to prevent accidental cross-environment operations in production. Essential for scaling SaaS applications.

This comprehensive guide provides systematic methods to optimize Supabase query and database performance. Techniques cover PostgreSQL indexing (basic, composite, partial), using EXPLAIN ANALYZE for query plan analysis, optimizing client calls (column selection, pagination), and managing infrastructure resources like connection pooling. Use this when facing slow queries, high latency, or preparing a project for production-scale traffic.

A comprehensive guardrail system for enforcing organizational governance across Supabase projects. It provides reusable RLS policy templates, strict naming conventions, CI-checked migration processes, cost alert configurations, and security audit scripts. Use this to ensure development consistency, security, and cost control at scale.

A comprehensive guide to implementing enterprise-grade Supabase architectures. This skill covers advanced patterns like monorepo layout, multi-tenant Row Level Security (RLS) isolation, microservices integration, and operational patterns (caching, queues, audit logging). Use this when building complex, scalable, and production-ready applications on Supabase.

This skill acts as an expert architect for scaffolding production-ready Rust applications. It generates complete, idiomatic, and robust project structures suitable for binary CLI tools, reusable libraries, and complex multi-crate workspaces (monorepos). It ensures adherence to Rust best practices, including proper module organization, advanced Cargo dependency management, and comprehensive testing setup.

This skill guides developers through the 'Green' phase of Test-Driven Development (TDD). It focuses on implementing the absolute minimum code required to satisfy failing tests, allowing the system to pass tests quickly. It is ideal for initial scaffolding and proving functionality, delaying complex design decisions until the refactoring phase.

This skill provides a comprehensive framework for designing optimal team structures for early-stage startups. It covers role-by-role hiring plans, compensation benchmarks (salary and total compensation), and equity allocation strategies, guiding founders from Pre-Seed through Series A to ensure scalable growth and proper talent investment.

Scans codebases for debt, scores severity, tracks trends, produces prioritized remediation plans, and reports progress for engineering teams balancing refactoring with feature delivery.

A comprehensive guide to building functional and scalable Telegram bots. Covers bot architecture design, mastering the Telegram Bot API, optimizing user experience (UX), implementing monetization strategies (Freemium, Subscriptions), and deploying production-ready bots using webhooks. Learn to transform complex ideas into engaging, real-world automated assistants.

This skill provides a comprehensive collection of quick-start templates for developing applications across diverse tech stacks. Users can quickly scaffold full-stack web apps (Next.js), SaaS products, REST APIs (Express, FastAPI), mobile apps (Flutter, React Native), and specialized tools like CLI utilities. It ensures the initial project setup adheres to modern best practices, saving significant time during the initial development phase.

A comprehensive guide and expert resource for mastering durable workflow orchestration using the Temporal Python SDK. Covers advanced patterns including distributed transactions, saga implementation, signal handling, and query patterns. Essential for building highly reliable, scalable, long-running backend systems, and mastering async/await execution models for production deployment.

Optimizes large language model inference on NVIDIA GPUs with TensorRT, delivering 10-100× faster throughput, quantized precision (FP8/INT4), multi-GPU scaling, and serving-ready tooling for production deployments.