core

core is a skill for Claude Code, Codex from vercel-labs/json-render. It costs 42 tokens per session (3,444 once invoked), scanned A, original, Apache-2.0.

A TypeScript package for defining the rules and structure of JSON descriptions that an AI can generate for user interfaces or videos. It also defines catalogs of available components and supports streaming these descriptions as they are built.

In plain words
What is it for?
Use it to define schemas, describe available components and actions, create catalogs, generate prompts, and process streamed JSON specifications.
Why use it?
It keeps AI-generated JSON consistent with the structures your application expects. It also gives the AI clear information about which components it can use.

Skill for Claude CodeCodex ✓ vendor

Written for no agent in particular: nothing here depends on one.

Good fit Use it to define schemas, describe available components and actions, create catalogs, generate prompts, and process streamed JSON specifications.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/vercel-labs/json-render/core
About the project

json-render is a framework where AI produces user interfaces as structured JSON constrained by a developer-defined component catalogue. It is for building dynamic interfaces from natural-language prompts across web and mobile frameworks.

vercel-labs/json-render · 18,711 stars · on GitHub · json-render.dev

Install

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.

Any agent
npx skills add vercel-labs/json-render --skill core
Clone the repo
git clone --depth 1 https://github.com/vercel-labs/json-render

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for core

README.md
[![agentmods](https://agentmods.dev/badge/skills/vercel-labs/json-render/core/github.svg)](https://agentmods.dev/skills/vercel-labs/json-render/core)
Your own site
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Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for core

Your own site · 80×15
<a href="https://agentmods.dev/skills/vercel-labs/json-render/core"><img src="https://agentmods.dev/badge/skills/vercel-labs/json-render/core.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 42 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,444 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • Socket pass 24 Sept 2026
  • Snyk pass 24 Sept 2026
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What 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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00042 $0.03444
Opus 5.5 $0.00017 $0.01378
Sonnet 5.5 $0.00008 $0.00689
Haiku 4.5 $0.00004 $0.00344

Measured 24d ago against content hash d0e8a8a3c419, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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 24d 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.

skills/core/SKILL.md · 296 lines

How it starts

The opening of the file, as written. The whole thing — 296 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

Experimental Decision-Model Composition

For decision-model composition, import experimental_composeSpec and experimental_createEvaluator from @json-render/core. These APIs are unreleased; use a source build until published, then pin exact versions. Experimental exports and Experimental_ types can change in any release.

  • Run the Gateway evaluator server-side with { model: "typesafe-ai/jev", apiKey: process.env.AI_GATEWAY_API_KEY! }. A plain model identifier is required; Jev is the current example; do not import a provider constructor.
  • Call experimental_composeSpec({ catalog, candidates, prompt, evaluate, initialState, signal }). It is an async generator; stream step.spec snapshots to your existing renderer and inspect complete.stopReason (finish, limit, unavailable). Errors and cancellation throw; retain the last snapshot as partial UI.
  • New trees default to strategy: "batch": one evaluation selects root/membership, then a second arranges the selected elements when needed. The first snapshot contains selected content in catalog order under the root's default/first slot. Resource variants share one exclusive question; repeated counts include the root. Root selection takes precedence over conflicting speculative membership for that recipe/resource. Equal sibling positions retain catalog order. Combined layouts are validated before publication; cycles or excessive depth throw. maxElements caps batched creation (default 32). Limit-truncated selections or a missing required layout call return limit. Use strategy: "sequential" for legacy next/parent adapters or sequential creation. Edits stay sequential.
  • Batched trace steps use choice: "select" | "layout" and an answers record. Count each trace as one evaluation, including its tokens and latency once. Custom evaluators must answer every offered question; names/choices are opaque and include root/select_*, then parent_*/order_* for batches.
  • For follow-up edits, pass the selected version as initialSpec. It is cloned and validated; the evaluator may add, replace, remove non-root subtrees, or move/reorder them. Unchanged IDs, bindings, and state are preserved. Optional elementDescriptions shares identifying descriptions without exposing raw props/state. initialState overrides the seed state. Seeds must be valid trees within the catalog, expression subset, and depth limit. Matching recipes consume usage/resource limits; removals/replacements release them. Replacements/moves use two evaluations (select target, then recipe/destination), each counted against the budget. Treat operation and position keys as opaque.
  • Supply atomic candidates with { id, description, element: { type, props, on?, visible? }, root?, maxUses?, resource? }. Catalog alone is insufficient: the app must supply values and binding recipes. Jev chooses elements and parent slots, never free-form text or code. It never executes actions.
  • Candidates are configured component instances, not page templates. Build them from current app records/operations or bind props to initialState; offer explicit alternatives for chart types, field configurations, and layout variants. The model chooses grouping and order within those options. Name required sections in prompts; structural validity does not imply semantic completeness.
  • V1 supports flat Spec catalogs, named slots, literals, $state, $bindState, and state visibility. No prebuilt children, repeat/watch, computed/template/conditional props, or custom directives. Success/error callbacks must reference allowed actions. Events must be declared in the component catalog.
  • Props and action params are validated against initial state without applying schema transforms/defaults. Supply valid initial values and validate/authorize action calls at runtime. Built-ins without parameter schemas get name validation only.
  • root defaults true, maxUses defaults one, shared resource values make alternatives mutually exclusive. Defaults: 32 evaluations (terminal calls included; no extra finish call for batches), depth eight, 10-second Gateway timeout per call. Supply an overall abort signal.
  • Candidate descriptions, prompt, instructions, topology, and explicit context are sent to the evaluator. Initial state and raw props/binding values are not sent automatically.
  • For custom providers implement Experimental_CompositionEvaluator: accept { state, questions, signal }, return { answers: { [question]: { choice, confidence? } }, usage?: { inputTokens? } }. Only return offered criteria keys.

Read the full file on GitHub · 296 lines

Changes

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.

  1. 24d ago Changed · +4 lines d0e8a8a3c419
  2. 29d ago Changed · +19 lines 6e96b93caf06
  3. 1mo ago First seen · 273 lines · 42 tokens per session scan A 1b4dd1f33e03

Subscribe to this mod's changes

core is a skill published in the GitHub repository vercel-labs/json-render (18,711 stars, last pushed 4d ago), licensed Apache-2.0. It adds 42 tokens to every session and 3,444 once invoked, about $0.0002 per session on Opus 5.5. 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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