json-render — Codex skill: what it does, how to install
Guides rendering of AI SDK v6 UIMessage parts, tool call results, and streaming states in custom React chat interfaces.
npx skills add openai/plugins --skill json-render -a codexWhat json-render does
This skill guides the agent on parsing UIMessage structures from AI SDK v6. Raw responses often break into unformatted JSON because they combine text blocks, reasoning outputs, tool calls, and internal step-start events.
It provides two rendering approaches: drop-in AI Elements components or manual rendering with @ai-sdk/react. The manual pattern handles tool states like approval-requested, output-denied, and finished outputs through custom cards. It also covers the Next.js server route where convertToModelMessages must be awaited.
Add this skill if you write custom chat interfaces and want clean React cards instead of raw payload dumps.
What it helps with
- render UIMessage parts into structured React elements
- display tool execution results as visual cards
- handle approval-requested and output-denied tool states
- filter out internal step-start tokens during streaming
Example prompts for the agent
- Render AI SDK v6 chat messages with custom tool call cards
- How do I handle tool approval states in my chat component?
- Show the Next.js route handler for AI SDK v6 streaming
How to install json-render
1Skills CLI (Vercel)
npx skills add openai/plugins --skill json-render -a codex2Manually: put the files in place
.agents/skills/json-render/SKILL.mdFor all projects use ~/.agents/skills/
1Skills CLI (Vercel)
npx skills add openai/plugins --skill json-render -a claude-code2Manually: put the files in place
~/.claude/skills/json-render/SKILL.mdCopy the whole skill folder; for one project use .claude/skills/json-render/
Manually: put the files in place
.cursor/skills/json-render/SKILL.mdCursor also reads .agents/skills/ and .claude/skills/
1GitHub CLI
gh skill install openai/plugins json-render2Manually: put the files in place
.github/skills/json-render/SKILL.mdPlatform CLI
gemini skills install https://github.com/openai/plugins --path plugins/vercel/skills/json-renderManually: put the files in place
.devin/skills/json-render/SKILL.mdThe legacy .windsurf/skills/ path still works
Claude.ai
Zip the folder and upload it in Settings → Capabilities → Skills
From the same set201
The whole set →Similar
Questions about json-render
How to install json-render in Codex?
Run npx skills add openai/plugins --skill json-render -a codex. The skill specification will be placed in .agents/skills/json-render/SKILL.md.
What does the json-render skill do?
It teaches the agent how to render AI SDK v6 messages in React. It includes patterns for tool cards, reasoning blocks, and streaming status checks.
What server changes does AI SDK v6 require?
In the Next.js route handler, convertToModelMessages is now asynchronous and must be awaited before passing messages to streamText.
Source text
SKILL.md · Apache-2.0
---
name: json-render
description: AI chat response rendering guidance — handling UIMessage parts, tool call displays, streaming states, and structured data presentation. Use when building custom chat UIs, rendering tool results, or troubleshooting AI response display issues.
metadata:
priority: 4
docs:
- "https://nextjs.org/docs/app/api-reference/file-conventions/route"
sitemap: "https://nextjs.org/sitemap.xml"
pathPatterns:
- 'components/chat/**'
- 'components/chat-*.tsx'
- 'components/chat-*.ts'
- 'src/components/chat/**'
- 'src/components/chat-*.tsx'
- 'src/components/chat-*.ts'
- 'components/message*.tsx'
- 'src/components/message*.tsx'
bashPatterns: []
---
# AI Chat Response Rendering
You are an expert in rendering AI SDK v6 chat responses — UIMessage parts, tool call results, streaming states, and structured data display in React applications.
## The Problem
When building chat interfaces with AI SDK v6, the raw message format includes multiple part types (text, tool calls, reasoning, images). Without proper rendering, responses appear as raw JSON or malformed output.
## AI SDK v6 Message Format
In v6, messages use the `UIMessage` type with a `parts` array:
```ts
interface UIMessage {
id: string
role: 'user' | 'assistant'
parts: UIMessagePart[]
}
// Part types:
// - { type: 'text', text: string }
// - { type: 'tool-<toolName>', toolCallId: string, state: string, input?: unknown, output?: unknown }
// state values: 'partial-call' | 'call' | 'output-available' | 'approval-requested' | 'approval-responded' | 'output-denied'
// - { type: 'reasoning', text: string }
// - { type: 'step-start' } // internal, skip in rendering
```
## Recommended: Use AI Elements
The simplest approach is to use AI Elements, which handles all part types automatically:
```tsx
import { Message } from '@/components/ai-elements/message'
import { Conversation } from '@/components/ai-elements/conversation'
{messages.map((message) => (
<Message key={message.id} message={message} />
))}
```
⤳ skill: ai-elements — Full component library for AI interfaces
## Manual Rendering Pattern
If you need custom rendering without AI Elements, follow this pattern:
```tsx
'use client'
import { useChat } from '@ai-sdk/react'
import { DefaultChatTransport } from 'ai'
export function Chat() {
const { messages, sendMessage, status } = useChat({
transport: new DefaultChatTransport({ api: '/api/chat' }),
})
const isLoading = status === 'streaming' || status === 'submitted'
return (
<div>
{messages.map((message) => (
<div key={message.id}>
{message.parts?.map((part, i) => {
// 1. Text parts — render as formatted text
if (part.type === 'text' && part.text.trim()) {
return (
<div key={i} className={
message.role === 'user'
? 'bg-primary text-primary-foreground rounded-lg px-3 py-2'
: 'bg-muted rounded-lg px-3 py-2'
}>
{part.text}
</div>
)
}
// 2. Tool parts — type is "tool-<toolName>"
if (part.type.startsWith('tool-')) {
const toolPart = part as {
type: string
toolCallId: string
state: string
input?: unknown
output?: unknown
}
const toolName = toolPart.type.replace('tool-', '')
if (toolPart.state === 'output-available' && toolPart.output) {
return <ToolResultCard key={i} name={toolName} output={toolPart.output} />
}
if (toolPart.state === 'output-denied') {
return (
<div key={i} className="text-sm text-muted-foreground">
{toolName} was denied
</div>
)
}
if (toolPart.state === 'approval-requested') {
return (
<div key={i} className="text-sm text-yellow-500">
{toolName} requires approval
</div>
)
}
return (
<div key={i} className="text-sm text-muted-foreground animate-pulse">
Running {toolName}...
</div>
)
}
// 3. Reasoning parts
if (part.type === 'reasoning') {
return (
<details key={i} className="text-xs text-muted-foreground">
<summary>Thinking...</summary>
<p className="whitespace-pre-wrap">{(part as { text: string }).text}</p>
</details>
)
}
// 4. Skip unknown types (step-start, etc.)
return null
})}
</div>
))}
</div>
)
}
```
## Rendering Tool Results as Cards
Instead of dumping raw JSON, render structured tool output as human-readable cards:
```tsx
function ToolResultCard({ name, output }: { name: string; output: unknown }) {
const data = output as Record<string, unknown>
// Pattern: Check for known result shapes and render accordingly
if (data?.success && data?.issue) {
const issue = data.issue as { identifier?: string; title?: string }
return (
<div className="rounded border border-border bg-card p-2 text-sm">
<span className="font-medium text-green-400">
{name === 'createIssue' ? 'Created' : 'Updated'} {issue.identifier}
</span>
<p className="text-muted-foreground">{issue.title}</p>
</div>
)
}
if (data?.items && Array.isArray(data.items)) {
return (
<div className="rounded border border-border bg-card p-2 text-sm">
<p className="font-medium">{data.items.length} results</p>
{data.items.slice(0, 5).map((item: Record<string, unknown>, i: number) => (
<p key={i} className="text-muted-foreground">{String(item.name || item.title || item.id)}</p>
))}
</div>
)
}
if (data?.error) {
return (
<div className="rounded border border-destructive/30 bg-destructive/10 p-2 text-sm text-destructive">
{String(data.error)}
</div>
)
}
// Fallback: simple completion message (not raw JSON)
return (
<div className="rounded border border-border bg-card p-2 text-xs text-muted-foreground">
{name} completed
</div>
)
}
```
## Server-Side Requirements
The server route must use the correct v6 response format:
```ts
// app/api/chat/route.ts
import { streamText, convertToModelMessages, gateway } from 'ai'
export async function POST(req: Request) {
const { messages } = await req.json()
// IMPORTANT: convertToModelMessages is async in v6
const modelMessages = await convertToModelMessages(messages)
const result = streamText({
model: gateway('anthropic/claude-sonnet-4.6'),
messages: modelMessages,
})
// Use toUIMessageStreamResponse for chat UIs (not toDataStreamResponse)
return result.toUIMessageStreamResponse()
}
```
## Client-Side Requirements
```tsx
import { useChat } from '@ai-sdk/react'
import { DefaultChatTransport } from 'ai'
const { messages, sendMessage, status } = useChat({
// v6 uses transport instead of api
transport: new DefaultChatTransport({ api: '/api/chat' }),
})
// v6 uses sendMessage instead of handleSubmit
sendMessage({ text: inputValue })
// Status values: 'ready' | 'submitted' | 'streaming'
const isLoading = status === 'streaming' || status === 'submitted'
```
## Common Mistakes
### 1. Raw JSON in chat responses
**Cause**: Rendering `message.content` instead of iterating `message.parts`.
**Fix**: Always iterate `message.parts` and handle each type:
```tsx
// WRONG — shows raw JSON
<div>{message.content}</div>
// RIGHT — renders each part type
{message.parts?.map((part, i) => {
if (part.type === 'text') return <span key={i}>{part.text}</span>
// ... handle other types
})}
```
### 2. Tool results showing as JSON blobs
**Cause**: Using `JSON.stringify(output)` as the display.
**Fix**: Create structured card components for known tool output shapes.
### 3. "Invalid prompt: messages do not contain..." error
**Cause**: Not converting UI messages to model messages on the server.
**Fix**: Use `await convertToModelMessages(messages)` — it's async in v6.
### 4. Messages not appearing / empty responses
**Cause**: Using `toDataStreamResponse()` instead of `toUIMessageStreamResponse()`.
**Fix**: Use `toUIMessageStreamResponse()` when the client uses `useChat` with `DefaultChatTransport`.
### 5. useChat not working with v6
**Cause**: Using the v5 `useChat({ api: '/api/chat' })` pattern.
**Fix**: Use `DefaultChatTransport`:
```tsx
// v5 (old)
const { messages, handleSubmit, input } = useChat({ api: '/api/chat' })
// v6 (current)
const { messages, sendMessage, status } = useChat({
transport: new DefaultChatTransport({ api: '/api/chat' }),
})
```
## Decision Tree
```
Building a chat UI with AI SDK v6?
└─ Want pre-built components?
└─ Yes → Use AI Elements (⤳ skill: ai-elements)
└─ No → Manual rendering with parts iteration
└─ Tool results look like JSON?
└─ Create ToolResultCard components for each tool's output shape
└─ Text not rendering?
└─ Check part.type === 'text' and use part.text
└─ Server errors?
└─ Check: await convertToModelMessages(), toUIMessageStreamResponse()
```
## Server-Side Message Validation
Use `validateUIMessages` to validate incoming messages before processing:
```ts
import { validateUIMessages, convertToModelMessages, streamText, gateway } from 'ai'
export async function POST(req: Request) {
const { messages } = await req.json()
const validatedMessages = validateUIMessages(messages)
const modelMessages = await convertToModelMessages(validatedMessages)
// ...
}
```
## Official Documentation
- [AI SDK UI](https://ai-sdk.dev/docs/ai-sdk-ui)
- [useChat Reference](https://ai-sdk.dev/docs/ai-sdk-ui/chatbot)
- [UIMessage Types](https://ai-sdk.dev/docs/reference/ai-sdk-core/ui-message)
- [AI Elements](https://ai-sdk.dev/elements)
© Vercel Labs · Apache-2.0 · openai/plugins