Atlas / Skills / vercel-labs / Core

CoreSAFE

skills/vercel-labs/core

The Generative UI framework

Verdict
SAFE
Grade
B
Trust score
89 /100
Version
—
Hosts
—
License
Apache-2.0
Stars
18,749
01

Overview

The Generative UI framework

Read from source at commit 6e433eb9a71fOBSERVED · 2026-10-21
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02

What it tells the agent

The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
name: core
description: Core package for defining schemas, catalogs, and AI prompt generation for json-render. Use when working with @json-render/core, defining schemas, creating catalogs, or building JSON specs for UI/video generation.
---

# @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.

See `packages/core/README.md` and `/docs/jev` for app integration and source-build instructions. The web playground is an example consumer, not a dependency of the API.

## Defining a Schema

```typescript
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

```typescript
import { defineCatalog } from "@json-render/core";
import { schema } from "./schema";
import { z } from "zod";

export const catalog = defineCatalog(schema, {
  componen
03

Trust audit

SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.

LayerWhat it checksResult
L0Provenance & inventoryPASS
L1Static analysis of the codeNA
L2Instruction surface (what it tells the agent)PASS
L3Class-specific surfacePASS
L4Behavioural (sandbox)SKIPPED

What the source does

Filesystem
none-observed
Network
none-observed
Shell
none-observed
Dependencies
pinned
Secrets in source
none-found

Findings (0)

No findings outside the package's declared scope.

Gates applied: no_behavioural_pass.

Audited 2026-10-21 · audit v0.4.1 · source sha 6e433eb9a71ffull audit observations/trust-audit/skill/vercel-labs__core.json · Report an issue / request a re-scan
04

Audit history

Every audit this skill has had.

DateSourceVerdictGradeScoreChange
2026-10-216e433eb9a71fSAFEB89first audit
05

Questions

What does the core — vercel-labs/json-render skill do?

The Generative UI framework

Is core — vercel-labs/json-render safe to install?

The audit found nothing in the source that contradicts what it says it does, and graded it B (89/100). Grade A is held back for packages that have also passed a sandboxed behavioural run, which is why a clean skill reads B.

What can core — vercel-labs/json-render access on my machine?

The audit observed no filesystem, network or shell use at all in its source.

How current is this page?

The grade is for one exact copy of the source (6e433eb9a71f), read on 2026-10-21. The repository is watched, and a new audit runs when it changes — this is the first audit.

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