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.

Learn robust and production-ready patterns for interacting with Supabase backend services. This skill covers client initialization, advanced CRUD operations (filtering, upserting), authentication, real-time subscriptions, and RPC calls. Provides best practices for both TypeScript and Python environments, ensuring secure and reliable data handling.

A comprehensive guide to building event-driven architectures using Supabase. It covers four core event mechanisms: Database Webhooks (pg_net), Postgres LISTEN/NOTIFY, Edge Function HTTP calls, and Realtime subscriptions. Includes production-ready SQL triggers, signature verification logic, and best practices for handling data changes (INSERT, UPDATE, DELETE) as external HTTP events.

Auto-activating skill that guides tensorflow model trainer workflows, covering data preparation, model training, hyperparameter tuning, and experiment tracking with production-ready code and standards validation.

Test Data Builder Guidance auto-activates when you ask about test data strategies, best practices, or configuration for automation frameworks, delivering step-by-step patterns, production-ready samples, and validation against common standards.

This skill generates code to create complex, interactive 3D scenes and visualizations on the web using the Three.js library and WebGL. It covers the entire development lifecycle, including setting up the core components (Scene, Camera, Renderer), defining geometry, applying materials, adding lighting, and implementing animation loops. Ideal for web developers needing to display product mockups, scientific data visualizations, or immersive user experiences.

Automates time series decomposition work in data analytics by guiding through patterns and best practices, generating production-ready code/configuration, and validating outputs for SQL, visualization, and BI workflows.

A comprehensive checklist designed to ensure TwinMind integrations are deployed securely, stably, and compliantly in a production environment. It covers critical best practices across multiple domains, including authentication, data encryption (PII, GDPR), robust infrastructure setup (auto-scaling, load balancing), advanced monitoring (metrics, tracing, alerting), and full CI/CD deployment workflows. Use this when preparing for go-live or conducting production audits.

This skill provides production-grade patterns for Unity's Data-Oriented Technology Stack (DOTS). It covers essential components like Entity Component System (ECS), the Job System, and the Burst Compiler, enabling developers to build high-performance, scalable games. Use this when optimizing CPU-bound logic, managing thousands of entities, or migrating traditional OOP game code to a data-oriented design.

This comprehensive toolkit guides senior UX designers and researchers through the entire product development lifecycle. It covers generating data-backed personas, mapping user journeys, planning structured usability tests, and synthesizing raw research data into actionable design recommendations. Use it when conducting deep user research or validating product designs.

A comprehensive checklist for deploying large-scale, production-ready GPU workloads on Vast.ai. It covers critical lifecycle stages including account authentication, instance validation, data encryption, persistent checkpointing strategies, fault tolerance for spot instances, robust monitoring (utilization & cost), and strict cost controls, ensuring reliable and stable ML/AI deployments.