hashbrown-dev
Building LLM-powered React applications with the Hashbrown library. Use when the user asks to (1) Build generative UI where LLMs render React components, (2) Add client-side tool calling for LLM-app interaction, (3) Stream LLM responses in React applications, (4) Execute LLM-generated JavaScript safely in a sandbox, (5) Build browser agents or AI-powered UIs with hashbrown, (6) Control React UI from LLM output, (7) Integrate with LLM providers like OpenAI, Anthropic, Google, Azure, Bedrock, or Ollama in React apps, (8) Create chatbots, form builders, predictive text inputs, or multi-threaded c
npx skills add majiayu000/claude-skill-registry --skill hashbrown-core --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Hashbrown Development Skill ## Overview Hashbrown is a React library for building LLM-powered applications with generative UI, client-side tool calling, streaming, and sandboxed JavaScript execution. It provides React hooks (`useChat`, `useUiChat`, etc.) that connect to a Node.js backend adapter, which securely communicates with LLM providers (OpenAI, Anthropic, Google, Azure, Bedrock, Ollama). **Architecture**: React frontend (using Hashbrown hooks) + Node.js backend adapter (proxies LLM API requests) ## Quick Start Workflow ### 1. Choose the Right Hook | Hook | Multi-turn Chat | Single Input | Structured Output | Tool Calling | Generate UI | | ------------------------- | :-------------: | :----------: | :---------------: | :----------: | :---------: | | `useChat` | ✅ | ❌ | ❌ | ✅ | ❌ | | `useStructuredChat` | ✅ | ❌ | ✅ | ✅ | ❌ | | `useCompletion` | ❌ | ✅ | ❌ | ✅ | ❌ | | `useStructuredCompletion` | ❌ | ✅ | ✅ | ✅ | ❌ | | `useUiChat` | ✅ | ❌ | ✅ | ✅ | ✅ | ### 2. Generate Boilerplate Use the scripts to scaffold components and servers: ```bash # List available templates python scripts/list-templates.py # Generate a component python scripts/generate-component.py simple-chat ./src/comp
- Overview
- Quick Start Workflow
- 1. Choose the Right Hook
- 2. Generate Boilerplate
- 3. Consult References for Details
- Core Capabilities
- 1. Generative UI
- 2. Client-Side Tool Calling
- 3. Structured Data Output
- 4. Sandboxed JavaScript Execution
- 5. Platform Integration
- Common Patterns
- Building a Chat Interface
- Adding Generative UI
List available templates python scripts/list-templates.py Generate a component python scripts/generate-component.py simple-chat ./src/components Generate a backend server python scripts/generate-server.py basic-chat-server ./backend
What does the hashbrown-dev skill do?
Building LLM-powered React applications with the Hashbrown library. Use when the user asks to (1) Build generative UI where LLMs render React components, (2) Add client-side tool calling for LLM-app interaction, (3) Stream LLM responses in React applications, (4) Execute LLM-generated JavaScript safely in a sandbox, (5) Build browser agents or AI-powered UIs with hashbrown, (6) Control React UI from LLM output, (7) Integrate with LLM providers like OpenAI, Anthropic, Google, Azure, Bedrock, or Ollama in React apps, (8) Create chatbots, form builders, predictive text inputs, or multi-threaded c
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill hashbrown-core --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.