Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/tanstack/ai/ag-ui-protocolnpx skills add TanStack/ai --skill ag-ui-protocolgit clone --depth 1 https://github.com/TanStack/aiWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00094 | $0.03477 |
| Opus 5 | $0.00047 | $0.01739 |
| Sonnet 5 | $0.00019 | $0.00695 |
| Haiku 4.5 | $0.00009 | $0.00348 |
Grade A, and why
ai-core/ag-ui-protocol scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 335 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AG-UI Protocol
This skill builds on ai-core. Read it first for critical rules.
Setup — Server Endpoint Producing AG-UI Events via SSE
import { chat, toServerSentEventsResponse } from '@tanstack/ai'
import { openaiText } from '@tanstack/ai-openai'
export async function POST(request: Request) {
const { messages } = await request.json()
const stream = chat({
adapter: openaiText('gpt-5.2'),
messages,
})
return toServerSentEventsResponse(stream)
}
chat() returns an AsyncIterable<StreamChunk>. Each StreamChunk is a
typed AG-UI event (discriminated union on type). The toServerSentEventsResponse()
helper encodes that iterable into an SSE-formatted Response with correct headers.
Setup — Receiving AG-UI RunAgentInput on the Server
import {
chat,
chatParamsFromRequestBody,
mergeAgentTools,
toServerSentEventsResponse,
} from '@tanstack/ai'
import { openaiText } from '@tanstack/ai-openai/adapters'
import { serverTools } from './tools'
export async function POST(req: Request) {
let params
try {
params = await chatParamsFromRequestBody(await req.json())
} catch (error) {
return new Response(
error instanceof Error ? error.message : 'Bad request',
{ status: 400 },
)
}
const stream = chat({
adapter: openaiText('gpt-4o'),
messages: params.messages,
tools: mergeAgentTools(serverTools, params.tools),
})
return toServerSentEventsResponse(stream)
}
chatParamsFromRequestBody validates the body against RunAgentInputSchema from @ag-ui/core. mergeAgentTools merges the server's tool registry with client-declared tools (server wins on collision; client-only tools become no-execute stubs that flow through the runtime's ClientToolRequest path).
params.messages is a mixed array of TanStack UIMessage anchors (with parts) and AG-UI fan-out duplicates ({role:'tool',...}, {role:'reasoning',...}). The existing convertMessagesToModelMessages (called inside chat()) handles dedup automatically.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 335 lines · 94 tokens per session scan A 58d774027186
ai-core/ag-ui-protocol is a skill published in the GitHub repository TanStack/ai (3,056 stars, last pushed today), licensed MIT. It adds 94 tokens to every session and 3,477 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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