Khiw Nitithadachot
Community@khiwniti
Khiw Nitithadachot publishes production-grade implementation guides for building, securing, and deploying AI agent SaaS platforms across frontend, backend, and data layers.
Agent Skills by Khiw Nitithadachot
Showing 11 vetted skills indexed across 1 GitHub repositories.
khiwnitiSandbox Integration Guide
Implements isolated code execution sandboxes for AI agents using Daytona, E2B, or Docker.
khiwnitiSecurity Checklist
Validates authentication, API security, injection prevention, and GDPR compliance patterns in Next.js SaaS applications.
khiwnitiAgent Streaming UX
Implements SSE token streaming and WebSocket reconnection patterns for real-time AI agent interfaces.
khiwnitiAgent Chat UI Patterns
Implements React components for streaming agent chat interfaces with tool-call rendering.
khiwnitiAI Agent SaaS Patterns
Provides architecture patterns for building multi-platform AI agent SaaS applications.
khiwnitiProduction Checklist
Validates AI agent SaaS applications against a production readiness checklist covering security, observability, and scalability.
khiwnitiLLM Integration Patterns
Implements multi-provider LLM routing, streaming, tool calling, and failover for AI agent SaaS applications.
khiwnitiAgent State Management
Implements Zustand and TanStack Query state management patterns for AI agent chat frontends.
khiwnitiNext.js App Router Guide
Implements Next.js App Router patterns including route groups, dynamic routes, and middleware.
khiwnitiDeployment Strategies
Configures VM, Docker, and Kubernetes deployment pipelines for AI agent SaaS applications.
khiwnitiDatabase Architecture
Designs multi-database architectures combining PostgreSQL, Neo4j, and Redis for AI agent SaaS applications.
Frequently Asked Questions About Khiw Nitithadachot
FAQPage SchemaWhat tasks can I accomplish using Khiw Nitithadachot's skills?▼
You can implement sandboxed agent code execution, SSE/WebSocket streaming UX, React chat UI components, Zustand and TanStack Query state layers, Next.js App Router routing, multi-provider LLM integration, multi-database architecture, security validation, and production deployment for AI agent SaaS platforms.
Who are these skills designed for?▼
These skills target frontend and full-stack engineers building production AI agent SaaS applications. They suit developers working with React, Next.js App Router, Zustand, TanStack Query, and backend engineers handling LLM integration, sandboxed execution, and multi-database data layers.
How do I apply these skills in a real project workflow?▼
Invoke a skill when its trigger phrases match your task, such as 'Daytona setup' or 'SSE streaming for agents'. Each skill provides implementation patterns covering architecture, code structure, validation checklists, and deployment steps that you apply directly within your AI agent SaaS codebase.
What prerequisites and dependencies do these skills assume?▼
Prerequisites vary by skill: Next.js App Router for routing, React for chat UI, Zustand and TanStack Query for state, PostgreSQL/Neo4j/Redis for data layers, Docker or Daytona/E2B for sandboxes, and Anthropic or OpenAI providers for LLM integration.
Do these skills cover production readiness and security?▼
Yes. The Security Checklist skill covers authentication patterns, input validation, secrets management, and GDPR compliance, while the Production Checklist and Deployment Strategies skills provide pre-launch validation, environment variable handling, CI/CD, and VM-based scalable deployment patterns.