Build complete AI agent applications from empty or existing codebases.
npx skills add https://github.com/chmonitor/chmonitor --skill build-agent-chmonitor
Please help me install this Agent Skill. Skill: build-agent Source: https://github.com/chmonitor/chmonitor/tree/main/.agents/skills/build-agent Command: npx skills add https://github.com/chmonitor/chmonitor --skill build-agent-chmonitor
Building AI agent applications requires navigating dozens of frameworks, setting up agent loops, tool integrations, UI layers, observability, and deployment configurations — a process that is time-consuming and error-prone, especially for teams without specialized agent engineering experience. This skill eliminates that friction by providing a structured, framework-agnostic workflow that adapts to your existing codebase or fresh project.
Use the build-agent skill to create a new AI research agent app from scratch that can search the web, summarize documents, and save findings to a Notion database.
High-intent search queries and answers about installing and using this skill.
Scaffolding an AI agent application end-to-end involves setting up the agent loop, tool calling, API surfaces, UI, and observability. This skill automates that entire process by detecting your existing codebase or starting fresh to generate a complete deployment-ready agent app.
You can add an AI agent to an existing Next.js codebase without rewriting code. The skill automatically detects your current setup, such as Vercel AI SDK, and adjusts its scaffolding workflow to integrate tool calling and deployment configurations non-destructively.
This agent scaffolding workflow supports multiple framework-agnostic options including LangGraph, Vercel AI SDK, Claude Agent SDK, Cloudflare Agents SDK, TanStack AI, Google ADK, and DeepAgents. It guides you through selecting the right framework for your specific runtime and use case.
Yes, this agent scaffolding supports Python and Go runtimes alongside TypeScript. It adapts its end-to-end workflow to your chosen runtime to handle core agent loop implementation, tool setup, and deployment to your target cloud or edge environment.
The best way to build a multi-agent system with tool calling is to use a structured scaffolding workflow that eliminates manual framework selection and integration. This skill handles the complete agent loop, tool integrations, and API surface setup for multi-agent systems.
To deploy a LangGraph agent to a cloud or edge target, you need configured deployment pipelines and observability integration. This skill eliminates manual deployment configuration by handling these layers automatically during the end-to-end agent app scaffolding process.
netbrainAutomate project initialization with stack-specific structures and agents.
thangvawnManage long-lived agent workloads with lifecycle, observability, and safety controls.
hieuckIntegrate Anthropic Claude API into Python and TypeScript applications.
ethan-rioAdd end-to-end tracing and observability to LLM applications via Arize AX.
