Build generative UI apps with Tambo — create rich, interactive React components from natural language.
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AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versiongenerative-uiExecute the skills CLI command in your project's root directory to begin installation:
Fetches generative-ui from tambo-ai/tambo and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate generative-ui. Access via /generative-ui in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
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Build generative UI apps with Tambo — create rich, interactive React components from natural language.
Load these when you need deeper implementation details beyond the bootstrap flow:
tambo add components. Component library, non-interactive flags, exit codes.These shared references are duplicated from building-with-tambo so each skill works independently.
The goal is to get the user from zero to a running app in a single prompt. Ask all questions upfront using AskUserQuestion with multiple questions, then execute everything without stopping.
Use AskUserQuestion with up to 3 questions in ONE call. Authentication is handled by the CLI in a later step.
Question 1: What do you want to build?
Ask the user what kind of app they're building. This drives which starter components to create. Examples: "a dashboard", "a chatbot", "a data visualization tool", "a task manager". If the user already said what they want in their initial message, skip this question.
Question 2: Framework
Options:
Question 3: App name
Let the user pick a name for their project directory. Default suggestion: derive from what they want to build (e.g., "my-dashboard", "my-chatbot"). Use kebab-case (letters, numbers, hyphens only). If the user gives a non-slug name like "Sales Dashboard", propose sales-dashboard instead.
Skip questions when the user already told you the answer. If they said "build me a Next.js dashboard app called analytics", you already know the framework, the app idea, and the name.
Run all of these sequentially without asking for confirmation between steps. If any command fails, stop the flow, surface the error, and ask the user how to proceed — do not continue to later steps.
All templates (standard, vite, analytics, expo) come with chat UI, TamboProvider wiring, component registry, and starter components already included. You do NOT need to add chat UI or wire up the app — just scaffold, configure the API key, add custom components, and start the server.
For Next.js (recommended):
npx tambo create-app <app-name> --template=standard --skip-tambo-init
cd <app-name>
For Vite:
npx tambo create-app <app-name> --template=vite --skip-tambo-init
cd <app-name>
Use --skip-tambo-init since create-app normally tries to run tambo init interactively, which won't work in non-interactive environments like coding agents. We handle authentication in the next step.
npx tambo init --project-name=<app-name>
This opens the browser for authentication and polls until the user completes auth (up to 15 minutes). Use a long timeout (at least 15 minutes) when running this command. Once auth completes, the CLI creates the project and writes the API key to .env.local with the correct env var for the framework (NEXT_PUBLIC_TAMBO_API_KEY, VITE_TAMBO_API_KEY, etc.).
IMPORTANT: Do NOT ask the user to paste an API key manually. Always use the CLI auth flow.
The template includes basic components, but add 1-2 components tailored to what the user wants to build. Don't use generic examples:
StatsCard, DataTableBotResponse with markdown supportChart with configurable dataTaskCard, TaskBoardContentCardEach component needs:
.describe() on every fieldlib/tambo.ts — add to the existing components array, don't replace it)Schema constraints — Tambo will reject invalid schemas at runtime:
z.record() — Record types (objects with dynamic keys) are not supported anywhere in the schema, including nested inside arrays or objects. Use z.object() with explicit named keys instead.z.map() or z.set() — Use arrays and objects instead.z.array(z.object({ col1: z.string(), col2: z.number() })) with explicit column keys — NOT z.array(z.record(z.string(), z.unknown())).React best practices for generated components:
key props when rendering lists (.map()). Use a unique field from the data (like id) — not the array index.id field (e.g., z.string().describe("Unique identifier")) in schemas for array items so there's always a stable key available.Example:
// src/components/StatsCard.tsx
import { z } from "zod/v4";
export const StatsCardSchema = z.object({
title: z.string().describe("Metric name"),
value: z.number().describe("Current value"),
change: z.number().optional().describe("Percent change from previous period"),
trend: z.enum(["up", "down", "flat"]).optional().describe("Trend direction"),
});
type StatsCardProps = z.infer<typeof StatsCardSchema>;
export function StatsCard({
title,
value,
change,
trend = "flat",
}: StatsCardProps) {
// ... implementation with Tailwind styling
}
Then add to the existing registry in lib/tambo.ts:
// Add to the existing components array — don't replace what's already there
// Next.js: import { StatsCard, StatsCardSchema } from "@/components/StatsCard";
// Vite: import { StatsCard, StatsCardSchema } from "../components/StatsCard";
import { StatsCard, StatsCardSchema } from "@/components/StatsCard";
// ... existing components ...
{
name: "StatsCard",
component: StatsCard,
description: "Displays a metric with value and trend. Use when user asks about stats, metrics, or KPIs.",
propsSchema: StatsCardSchema,
},
Only start the dev server after all code changes (scaffolding, init, component creation, registry updates) are complete.
npm run dev
Run this in the background so the user can see their app immediately.
After everything is running, give a brief summary:
http://localhost:3000 for Next.js, http://localhost:5173 for Vite)npx tambo init to authenticateNext.js 14+ (App Router)
├── TypeScript
├── Tailwind CSS
├── Zod (for schemas)
└── @tambo-ai/react
npx tambo create-app my-app --template=standard
Vite + React
├── TypeScript
├── Tailwind CSS
├── Zod
└── @tambo-ai/react
Vite + React
├── TypeScript
├── Plain CSS
├── Zod
└── @tambo-ai/react
Every generative component must be registered:
import { TamboComponent } from "@tambo-ai/react";
import { ComponentName, ComponentNameSchema } from "@/components/ComponentName";
export const components: TamboComponent[] = [
{
name: "ComponentName",
component: ComponentName,
description: "What it does. When to use it.",
propsSchema: ComponentNameSchema,
},
];
Key rules:
.describe() on every field — this is how the AI knows what to passTemplates already include chat UI. These are only needed if the user wants additional UI primitives beyond what the template provides:
npx tambo add message-thread-full --yes # Complete chat interface
npx tambo add control-bar --yes # Controls and actions
npx tambo add thread-history --yes # Conversation history sidebar
| Technology | Support Level | Notes |
|---|---|---|
| Next.js 14+ | Full | Recommended, App Router preferred |
| Vite | Full | Great for SPAs |
| Create React App | Partial | Works but CRA is deprecated |
| Remix | Partial | Works with client components |
| TypeScript | Full | Strongly recommended |
| JavaScript | Full | Works but less type safety |
| Tailwind CSS | Full | Default for CLI components |
| Plain CSS | Full | Need custom component styling |
| CSS Modules | Full | Supported |
| Zod | Required | Used for all schemas |
Have the user verify the app works by:
If something doesn't work:
If you encounter any of these while helping a user, suggest filing a GitHub issue:
@tambo-ai/react, @tambo-ai/client, or the tambo CLIUse this template when drafting an issue for tambo-ai/tambo:
**Implementation Guide
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Steps
- 1Install skill using provided installation command
- 2Test with simple use case relevant to your work
- 3Evaluate output quality and relevance
- 4Iterate on prompts to improve results
- 5Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
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Frontendsame categoryReviews
4.5★★★★★50 reviews- LLuis Garcia★★★★★Dec 24, 2024
Keeps context tight: generative-ui is the kind of skill you can hand to a new teammate without a long onboarding doc.
- LLucas Perez★★★★★Dec 24, 2024
Registry listing for generative-ui matched our evaluation — installs cleanly and behaves as described in the markdown.
- ZZara Taylor★★★★★Dec 16, 2024
generative-ui has been reliable in day-to-day use. Documentation quality is above average for community skills.
- GGanesh Mohane★★★★★Dec 8, 2024
generative-ui has been reliable in day-to-day use. Documentation quality is above average for community skills.
- SShikha Mishra★★★★★Dec 4, 2024
Registry listing for generative-ui matched our evaluation — installs cleanly and behaves as described in the markdown.
- AAnika Srinivasan★★★★★Dec 4, 2024
generative-ui fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- YYash Thakker★★★★★Nov 23, 2024
generative-ui reduced setup friction for our internal harness; good balance of opinion and flexibility.
- HHenry Chawla★★★★★Nov 23, 2024
We added generative-ui from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- AAnika Reddy★★★★★Nov 15, 2024
Useful defaults in generative-ui — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- SSoo Kapoor★★★★★Nov 15, 2024
I recommend generative-ui for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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