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/structured-outputsnpx skills add TanStack/ai --skill structured-outputsgit 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 | $0.00128 | $0.06335 |
| Opus 5 | $0.00064 | $0.03168 |
| Sonnet 5 | $0.00026 | $0.01267 |
| Haiku 4.5 | $0.00013 | $0.00634 |
Grade A, and why
ai-core/structured-outputs 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 — 589 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Structured Outputs
Dependency note: This skill builds on ai-core. Read it first for critical rules. The
useChatpatterns below build on ai-core/chat-experience — read that for the base hook surface, then come back here for the structured-output specifics.
Setup
import { chat } from '@tanstack/ai'
import { openaiText } from '@tanstack/ai-openai'
import { z } from 'zod'
const person = await chat({
adapter: openaiText('gpt-5.2'),
messages: [{ role: 'user', content: 'John Doe, 30' }],
outputSchema: z.object({
name: z.string(),
age: z.number(),
}),
})
person.name // string — fully typed, no cast
person.age // number
When outputSchema is provided, chat() returns Promise<InferSchemaType<TSchema>> instead of AsyncIterable<StreamChunk>. The result is fully typed.
Adding stream: true switches the return to StructuredOutputStream<InferSchemaType<TSchema>> — incremental JSON deltas plus a terminal validated object. See Pattern 3 below for direct iteration, Pattern 4 for the useChat shape on the client, Pattern 5 for multi-turn structured chats, and Pattern 6 for harness adapters.
Decision: which pattern fits
| Building this | Use |
|---|---|
| One prompt in → one typed object out (script, server endpoint, CLI) | Pattern 1 (basic) or 2 (nested) |
| A UI that fills in field by field as the model streams (progressive form, live card) | Pattern 4 — useChat({ outputSchema }) |
| Direct iteration of the stream in Node or tests | Pattern 3 — async iterable |
| Users iterate on a structured object across multiple turns (recipe builder, ticket refinement) | Pattern 5 — multi-turn structured chat |
| Tools that gather info, then return a typed object | Combine any of the above with tools — see ai-core/tool-calling |
| A coding agent in a sandbox inspects files, then returns a typed object | Pattern 6 — harness outputSchema |
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 · 589 lines · 128 tokens per session scan A 0fa3e2fec6df
ai-core/structured-outputs is a skill published in the GitHub repository TanStack/ai (3,045 stars, last pushed 2d ago), licensed MIT. It adds 128 tokens to every session and 6,335 once invoked, about $0.0006 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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