Skill UI

Outlines guarantees valid JSON/XML/code outputs by applying CFG/FSM-constrained token filtering, Pydantic schemas, and grammar-level sampling while supporting local Transformers, llama.cpp, and vLLM backends for fast inference.

This skill enables the coordination of multiple specialized AI agents within a single session. It is designed for complex, multi-domain tasks such as comprehensive code reviews, security audits, or full-stack feature implementations. It coordinates expertise across various domains (backend, frontend, testing, architecture) to provide holistic analysis and actionable recommendations.

Automates guidance around Prisma schema tasks for backend development, supplying best-practice steps, ready-to-use snippets, and schema validation whenever you invoke the helper.

Blueprint skill describing the architecture, stack, folder layout, API patterns, and testing/deployment workflows used by the Zenith AI customer discovery platform clade. Use it as a reference when working on this dedicated project to understand expectations for frontend, backend, AI integration, and QA.

Provides prioritized performance optimization recommendations across frontend, backend, and infrastructure, offering bottleneck analysis, impact estimates, and phased implementation roadmaps when users request speed improvements or reviews.

This skill demonstrates the multi-model pattern for creating robust and clean API contracts using Pydantic. It guides the creation of separate models for creation requests (Create), updates (Update), responses (Response), and database documents (InDB), ensuring type safety and clear data validation for Python backends. It also covers best practices like handling camelCase aliases and optional fields.

An end-to-end workflow for building robust, production-ready Python backend APIs. This guide covers all stages, including project scaffolding, SQLAlchemy database integration, Pydantic validation, implementing JWT/OAuth2 authentication, comprehensive error handling, unit testing, and Docker deployment for modern microservices.

Automated guidance for setting up RabbitMQ queues in backend services, covering patterns, code snippets, and validation for Node.js, Python, Go, and microservices messaging scenarios.

Guides backend engineers through planning, implementing, and validating rate limit middleware across Node.js, Python, Go, and microservice architectures.

Automates redis cache manager workflows by recommending backend patterns, generating production-ready code, and validating configurations for node.js, python, go, and microservice architectures.

Base Refly ecosystem skill that routes user intents to domain-specific skills, runs workflows via the CLI, and automates multi-step tasks or pipelines by delegating execution to the Refly backend while providing CLI-specific triggers and commands.

This comprehensive workflow guides developers through building a production-ready, full-stack web application entirely within the Replit environment. It covers setting up the entire architecture, including Express/Flask backend logic, robust PostgreSQL database integration for persistent data, implementing secure user authentication using Replit Auth, and managing file uploads via Object Storage. It is the definitive guide for launching a complex, scalable SaaS project from scratch.