json-render-generative-ui
Generative UI framework that renders AI-generated JSON specs into type-safe UI components across React, Vue, Svelte, Solid, React Native, video, PDF, and email.
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npx skills add https://github.com/aradotso/trending-skills --skill json-render-generative-uiIs this agent skill safe to install?
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This skill describes the json-render Generative UI framework, which allows AI to generate dynamic interfaces using JSON specifications. It downloads various framework-specific packages from npm and includes built-in guardrails like Zod schema validation to mitigate risks associated with processing AI-generated content, though an indirect prompt injection surface remains.
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Score: 93/100 · 2 sections analyzed
What does this agent skill do?
json-render Generative UI Framework
Skill by ara.so — Daily 2026 Skills collection.
json-render is a Generative UI framework that lets AI generate dynamic interfaces from natural language prompts, constrained to a predefined component catalog. AI outputs JSON; json-render renders it safely and predictably across any platform.
Installation
# React (core)
npm install @json-render/core @json-render/react
# React + shadcn/ui (36 pre-built components)
npm install @json-render/shadcn
# React Native
npm install @json-render/core @json-render/react-native
# Vue
npm install @json-render/core @json-render/vue
# Svelte
npm install @json-render/core @json-render/svelte
# SolidJS
npm install @json-render/core @json-render/solid
# Video (Remotion)
npm install @json-render/core @json-render/remotion
# PDF
npm install @json-render/core @json-render/react-pdf
# Email
npm install @json-render/core @json-render/react-email @react-email/components @react-email/render
# 3D (React Three Fiber)
npm install @json-render/core @json-render/react-three-fiber @react-three/fiber @react-three/drei three
# OG Images / SVG / PNG
npm install @json-render/core @json-render/image
# State management adapters
npm install @json-render/zustand # or redux, jotai, xstate
# MCP integration (Claude, ChatGPT, Cursor)
npm install @json-render/mcp
# YAML wire format
npm install @json-render/yaml
Core Concepts
| Concept | Description |
|---|---|
| Catalog | Defines allowed components and actions (the guardrails for AI) |
| Spec | AI-generated JSON describing which components to render and with what props |
| Registry | Maps catalog component names to actual render implementations |
| Renderer | Platform-specific component that takes a spec + registry and renders UI |
| Actions | Named events AI can trigger (e.g. export_report, refresh_data) |
Spec Format
The flat spec format uses a root key + elements map:
const spec = {
root: "card-1",
elements: {
"card-1": {
type: "Card",
props: { title: "Dashboard" },
children: ["metric-1", "metric-2", "button-1"],
},
"metric-1": {
type: "Metric",
props: { label: "Revenue", value: "124000", format: "currency" },
children: [],
},
"metric-2": {
type: "Metric",
props: { label: "Growth", value: "0.18", format: "percent" },
children: [],
},
"button-1": {
type: "Button",
props: { label: "Export Report", action: "export_report" },
children: [],
},
},
};
Step 1: Define a Catalog
import { defineCatalog } from "@json-render/core";
import { schema } from "@json-render/react/schema";
import { z } from "zod";
const catalog = defineCatalog(schema, {
components: {
Card: {
props: z.object({ title: z.string() }),
description: "A card container with a title",
},
Metric: {
props: z.object({
label: z.string(),
value: z.string(),
format: z.enum(["currency", "percent", "number"]).nullable(),
}),
description: "Displays a single metric value with optional formatting",
},
Button: {
props: z.object({
label: z.string(),
action: z.string(),
}),
description: "Clickable button that triggers an action",
},
Stack: {
props: z.object({
direction: z.enum(["row", "column"]).default("column"),
gap: z.number().optional(),
}),
description: "Layout container that stacks children",
},
},
actions: {
export_report: { description: "Export the current dashboard to PDF" },
refresh_data: { description: "Refresh all metric data" },
navigate: {
description: "Navigate to a page",
payload: z.object({ path: z.string() }),
},
},
});
Step 2: Define a Registry (React)
import { defineRegistry, Renderer } from "@json-render/react";
function format(value: string, fmt: string | null): string {
if (fmt === "currency") return `$${Number(value).toLocaleString()}`;
if (fmt === "percent") return `${(Number(value) * 100).toFixed(1)}%`;
return value;
}
const { registry } = defineRegistry(catalog, {
components: {
Card: ({ props, children }) => (
<div className="rounded-lg border p-4 shadow-sm">
<h3 className="text-lg font-semibold mb-3">{props.title}</h3>
{children}
</div>
),
Metric: ({ props }) => (
<div className="flex flex-col">
<span className="text-sm text-gray-500">{props.label}</span>
<span className="text-2xl font-bold">
{format(props.value, props.format)}
</span>
</div>
),
Button: ({ props, emit }) => (
<button
className="px-4 py-2 bg-blue-600 text-white rounded"
onClick={() => emit("press")}
>
{props.label}
</button>
),
Stack: ({ props, children }) => (
<div
style={{
display: "flex",
flexDirection: props.direction ?? "column",
gap: props.gap ?? 8,
}}
>
{children}
</div>
),
},
});
Step 3: Render the Spec
import { Renderer } from "@json-render/react";
function Dashboard({ spec, onAction }) {
return (
<Renderer
spec={spec}
registry={registry}
onAction={(action, payload) => {
console.log("Action triggered:", action, payload);
onAction?.(action, payload);
}}
/>
);
}
Generating Specs with AI (Vercel AI SDK)
import { generateObject } from "ai";
import { openai } from "@ai-sdk/openai";
import { getCatalogSchema, getCatalogPrompt } from "@json-render/core";
async function generateDashboard(userPrompt: string) {
const { object: spec } = await generateObject({
model: openai("gpt-4o"),
schema: getCatalogSchema(catalog),
system: getCatalogPrompt(catalog),
prompt: userPrompt,
});
return spec;
}
// Usage
const spec = await generateDashboard(
"Create a sales dashboard showing revenue, conversion rate, and an export button"
);
Streaming Specs
import { streamObject } from "ai";
import { openai } from "@ai-sdk/openai";
import { getCatalogSchema, getCatalogPrompt, parseSpecStream } from "@json-render/core";
import { Renderer } from "@json-render/react";
import { useState, useEffect } from "react";
function StreamingDashboard({ prompt }: { prompt: string }) {
const [spec, setSpec] = useState(null);
useEffect(() => {
async function stream() {
const { partialObjectStream } = await streamObject({
model: openai("gpt-4o"),
schema: getCatalogSchema(catalog),
system: getCatalogPrompt(catalog),
prompt,
});
for await (const partial of partialObjectStream) {
setSpec(partial); // Renderer handles partial specs gracefully
}
}
stream();
}, [prompt]);
if (!spec) return <div>Generating UI...</div>;
return <Renderer spec={spec} registry={registry} />;
}
Using Pre-built shadcn/ui Components
import { defineCatalog } from "@json-render/core";
import { schema } from "@json-render/react/schema";
import { defineRegistry, Renderer } from "@json-render/react";
import { shadcnComponentDefinitions } from "@json-render/shadcn/catalog";
import { shadcnComponents } from "@json-render/shadcn";
// Pick any of the 36 available shadcn components
const catalog = defineCatalog(schema, {
components: {
Card: shadcnComponentDefinitions.Card,
Stack: shadcnComponentDefinitions.Stack,
Heading: shadcnComponentDefinitions.Heading,
Text: shadcnComponentDefinitions.Text,
Button: shadcnComponentDefinitions.Button,
Badge: shadcnComponentDefinitions.Badge,
Table: shadcnComponentDefinitions.Table,
Chart: shadcnComponentDefinitions.Chart,
Input: shadcnComponentDefinitions.Input,
Select: shadcnComponentDefinitions.Select,
},
actions: {
submit: { description: "Submit a form" },
export: { description: "Export data" },
},
});
const { registry } = defineRegistry(catalog, {
components: {
Card: shadcnComponents.Card,
Stack: shadcnComponents.Stack,
Heading: shadcnComponents.Heading,
Text: shadcnComponents.Text,
Button: shadcnComponents.Button,
Badge: shadcnComponents.Badge,
Table: shadcnComponents.Table,
Chart: shadcnComponents.Chart,
Input: shadcnComponents.Input,
Select: shadcnComponents.Select,
},
});
function AIPage({ spec }) {
return <Renderer spec={spec} registry={registry} />;
}
Vue Renderer
import { h, defineComponent } from "vue";
import { defineCatalog } from "@json-render/core";
import { schema } from "@json-render/vue/schema";
import { defineRegistry, Renderer } from "@json-render/vue";
import { z } from "zod";
const catalog = defineCatalog(schema, {
components: {
Card: {
props: z.object({ title: z.string() }),
description: "Card container",
},
Button: {
props: z.object({ label: z.string() }),
description: "Button",
},
},
actions: {
click: { description: "Button clicked" },
},
});
const { registry } = defineRegistry(catalog, {
components: {
Card: ({ props, children }) =>
h("div", { class: "card" }, [
h("h3", null, props.title),
children,
]),
Button: ({ props, emit }) =>
h("button", { onClick: () => emit("click") }, props.label),
},
});
// In your Vue SFC:
// <template>
// <Renderer :spec="spec" :registry="registry" />
// </template>
React Native Renderer
import { defineCatalog } from "@json-render/core";
import { schema } from "@json-render/react-native/schema";
import {
standardComponentDefinitions,
standardActionDefinitions,
} from "@json-render/react-native/catalog";
import { defineRegistry, Renderer } from "@json-render/react-native";
// 25+ standard mobile components out of the box
const catalog = defineCatalog(schema, {
components: { ...standardComponentDefinitions },
actions: standardActionDefinitions,
});
const { registry } = defineRegistry(catalog, {
components: {}, // use all standard implementations
});
export function AIScreen({ spec }) {
return <Renderer spec={spec} registry={registry} />;
}
PDF Generation
import { renderToBuffer } from "@json-render/react-pdf";
const invoiceSpec = {
root: "doc",
elements: {
doc: {
type: "Document",
props: { title: "Invoice #1234" },
children: ["page-1"],
},
"page-1": {
type: "Page",
props: { size: "A4" },
children: ["heading-1", "table-1"],
},
"heading-1": {
type: "Heading",
props: { text: "Invoice #1234", level: "h1" },
children: [],
},
"table-1": {
type: "Table",
props: {
columns: [
{ header: "Item", width: "60%" },
{ header: "Amount", width: "40%", align: "right" },
],
rows: [
["Widget A", "$10.00"],
["Widget B", "$25.00"],
["Total", "$35.00"],
],
},
children: [],
},
},
};
// Returns a Buffer you can send as a response
const buffer = await renderToBuffer(invoiceSpec);
// In a Next.js route handler:
export async function GET() {
const buffer = await renderToBuffer(invoiceSpec);
return new Response(buffer, {
headers: { "Content-Type": "application/pdf" },
});
}
Email Generation
import { renderToHtml } from "@json-render/react-email";
import { schema, standardComponentDefinitions } from "@json-render/react-email";
import { defineCatalog } from "@json-render/core";
const catalog = defineCatalog(schema, {
components: standardComponentDefinitions,
});
const emailSpec = {
root: "html-1",
elements: {
"html-1": {
type: "Html",
props: { lang: "en" },
children: ["head-1", "body-1"],
},
"head-1": { type: "Head", props: {}, children: [] },
"body-1": {
type: "Body",
props: { style: { backgroundColor: "#f6f9fc" } },
children: ["container-1"],
},
"container-1": {
type: "Container",
props: { style: { maxWidth: "600px", margin: "0 auto" } },
children: ["heading-1", "text-1", "button-1"],
},
"heading-1": {
type: "Heading",
props: { text: "Welcome aboard!" },
children: [],
},
"text-1": {
type: "Text",
props: { text: "Thanks for signing up. Click below to get started." },
children: [],
},
"button-1": {
type: "Button",
props: { text: "Get Started", href: "https://example.com" },
children: [],
},
},
};
const html = await renderToHtml(emailSpec);
MCP Integration (Claude, ChatGPT, Cursor)
import { createMCPServer } from "@json-render/mcp";
const server = createMCPServer({
catalog,
name: "my-ui-server",
version: "1.0.0",
});
server.start();
State Management Integration
import { create } from "zustand";
import { createZustandAdapter } from "@json-render/zustand";
const useStore = create((set) => ({
data: {},
setData: (data) => set({ data }),
}));
const stateStore = createZustandAdapter(useStore);
// Pass to Renderer for action handling with state
<Renderer spec={spec} registry={registry} stateStore={stateStore} />;
YAML Wire Format
import { parseYAML, toYAML } from "@json-render/yaml";
// AI can output YAML instead of JSON (often more token-efficient)
const yamlSpec = `
root: card-1
elements:
card-1:
type: Card
props:
title: Hello World
children: [button-1]
button-1:
type: Button
props:
label: Click Me
children: []
`;
const spec = parseYAML(yamlSpec);
Full Next.js App Router Example
// app/dashboard/page.tsx
import { generateObject } from "ai";
import { openai } from "@ai-sdk/openai";
import { getCatalogSchema, getCatalogPrompt } from "@json-render/core";
import { DashboardRenderer } from "./DashboardRenderer";
import { catalog } from "@/lib/catalog";
export default async function DashboardPage({
searchParams,
}: {
searchParams: { q?: string };
}) {
const prompt = searchParams.q ?? "Show me a sales overview dashboard";
const { object: spec } = await generateObject({
model: openai("gpt-4o"),
schema: getCatalogSchema(catalog),
system: getCatalogPrompt(catalog),
prompt,
});
return <DashboardRenderer spec={spec} />;
}
// app/dashboard/DashboardRenderer.tsx
"use client";
import { Renderer } from "@json-render/react";
import { registry } from "@/lib/registry";
import { useRouter } from "next/navigation";
export function DashboardRenderer({ spec }) {
const router = useRouter();
return (
<Renderer
spec={spec}
registry={registry}
onAction={(action, payload) => {
switch (action) {
case "navigate":
router.push(payload.path);
break;
case "export_report":
window.open("/api/export", "_blank");
break;
case "refresh_data":
router.refresh();
break;
}
}}
/>
);
}
Common Patterns
Conditional Component Availability
// Restrict catalog based on user role
function getCatalogForRole(role: "admin" | "viewer") {
const base = { Card, Stack, Heading, Text, Metric };
const adminOnly = role === "admin" ? { Button, Form, Table } : {};
const adminActions = role === "admin"
? { export: { description: "Export data" } }
: {};
return defineCatalog(schema, {
components: { ...base, ...adminOnly },
actions: adminOnly ? adminActions : {},
});
}
Dynamic Props with Runtime Data
// Components can fetch their own data
const { registry } = defineRegistry(catalog, {
components: {
LiveMetric: ({ props }) => {
const { data } = useSWR(`/api/metrics/${props.metricId}`);
return (
<div>
<span>{props.label}</span>
<span>{data?.value ?? "..."}</span>
</div>
);
},
},
});
Type-Safe Action Handling
import { type ActionHandler } from "@json-render/core";
const handleAction: ActionHandler<typeof catalog> = (action, payload) => {
// action and payload are fully typed based on your catalog definition
if (action === "navigate") {
router.push(payload.path); // payload.path is typed as string
}
};
Troubleshooting
| Problem | Cause | Fix |
|---|---|---|
| AI generates unknown component type | Component not in catalog | Add component to defineCatalog or update AI prompt |
| Props validation error | AI hallucinated a prop | Tighten Zod schema, add .strict() or .describe() hints |
| Renderer shows nothing | root key doesn't match an elements key | Check spec structure; root must reference a valid element ID |
| Partial spec renders incorrectly | Streaming not handled | Use parseSpecStream utility or check for null elements before render |
| Actions not firing | onAction not passed to Renderer | Pass onAction prop to <Renderer> |
| shadcn components unstyled | Missing Tailwind config | Ensure @json-render/shadcn paths are in tailwind.config.js content array |
| TypeScript errors in registry | Catalog/registry mismatch | Ensure defineRegistry(catalog, ...) uses the same catalog instance |
Environment Variables
# For AI generation (use your preferred provider)
OPENAI_API_KEY=your_key_here
ANTHROPIC_API_KEY=your_key_here
# For MCP server
MCP_SERVER_PORT=3001
Key API Reference
// Core
defineCatalog(schema, { components, actions }) // Define guardrails
getCatalogSchema(catalog) // Get Zod schema for AI
getCatalogPrompt(catalog) // Get system prompt for AI
// React
defineRegistry(catalog, { components }) // Create typed registry
<Renderer spec={spec} registry={registry} onAction={fn} />
// Core utilities
parseSpecStream(stream) // Parse streaming partial specs
toYAML(spec) // Convert spec to YAML
parseYAML(yaml) // Parse YAML spec to JSON
// PDF
renderToBuffer(spec) // → Buffer
renderToStream(spec) // → ReadableStream
// Email
renderToHtml(spec) // → HTML string
renderToText(spec) // → plain text string
How can the creator link this skill?
Add the canonical catalog link to the repository README so users can inspect current installs and available audits. The publishing guide covers the complete discovery path.
<a href="https://skillzs.dev/skills/aradotso/trending-skills/json-render-generative-ui">View json-render-generative-ui on skillZs</a>