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/vercel-labs/json-render/corenpx skills add vercel-labs/json-render --skill coregit clone --depth 1 https://github.com/vercel-labs/json-renderWhat 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 | $0.00042 | $0.02374 |
| Opus 5 | $0.00021 | $0.01187 |
| Sonnet 5 | $0.00008 | $0.00475 |
| Haiku 4.5 | $0.00004 | $0.00237 |
Grade A, and why
core 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 — 273 lines — stays where its author put it; the contents beside it link to each section on GitHub.
@json-render/core
Core package for schema definition, catalog creation, and spec streaming.
Key Concepts
- Schema: Defines the structure of specs and catalogs (use
defineSchema) - Catalog: Maps component/action names to their definitions (use
defineCatalog) - Spec: JSON output from AI that conforms to the schema
- SpecStream: JSONL streaming format for progressive spec building
Defining a Schema
import { defineSchema } from "@json-render/core";
export const schema = defineSchema((s) => ({
spec: s.object({
// Define spec structure
}),
catalog: s.object({
components: s.map({
props: s.zod(),
description: s.string(),
}),
}),
}), {
promptTemplate: myPromptTemplate, // Optional custom AI prompt
});
Creating a Catalog
import { defineCatalog } from "@json-render/core";
import { schema } from "./schema";
import { z } from "zod";
export const catalog = defineCatalog(schema, {
components: {
Button: {
props: z.object({
label: z.string(),
variant: z.enum(["primary", "secondary"]).nullable(),
}),
description: "Clickable button component",
},
},
});
Generating AI Prompts
const systemPrompt = catalog.prompt(); // Uses schema's promptTemplate
const systemPrompt = catalog.prompt({ customRules: ["Rule 1", "Rule 2"] });
SpecStream Utilities
For streaming AI responses (JSONL patches):
import { createSpecStreamCompiler } from "@json-render/core";
const compiler = createSpecStreamCompiler<MySpec>();
// Process streaming chunks
const { result, newPatches } = compiler.push(chunk);
// Get final result
const finalSpec = compiler.getResult();
Dynamic Prop Expressions
Any prop value can be a dynamic expression resolved at render time:
{ "$state": "/state/key" }- reads a value from the state model (one-way read){ "$bindState": "/path" }- two-way binding: reads from state and enables write-back. Use on the natural value prop (value, checked, pressed, etc.) of form components.{ "$bindItem": "field" }- two-way binding to a repeat item field. Use inside repeat scopes.{ "$cond": <condition>, "$then": <value>, "$else": <value> }- evaluates a visibility condition and picks a branch{ "$template": "Hello, ${/user/name}!" }- interpolates${/path}references with state values{ "$computed": "fnName", "args": { "key": <expression> } }- calls a registered function with resolved args
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 · 273 lines · 42 tokens per session scan A 1b4dd1f33e03
core is a skill published in the GitHub repository vercel-labs/json-render (16,056 stars, last pushed 4d ago), licensed Apache-2.0. It adds 42 tokens to every session and 2,374 once invoked, about $0.0002 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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