json-render-core

Schema definition, catalog creation, and spec streaming for AI-driven JSON rendering.

vercel-labs/json-renderUpdated Apr 8, 2026

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Claude CodeCursorClineWindsurfCodexGooseGitHub CopilotZed

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Install Skill

Run in your terminal

$npx skills add https://github.com/vercel-labs/json-render --skill json-render-core

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this week

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What it does

  • Define schemas with typed specs and component catalogs using defineSchema and defineCatalog ; generate AI prompts automatically or with custom rules

  • Support dynamic prop expressions including state binding ( $state , $bindState ), conditionals ( $cond ), templates, and computed functions

  • Stream AI responses as JSONL patches using createSpecStreamCompiler for progressive spec building

  • Built-in val

Category

Productivity

Last updated

Apr 8, 2026

Installation Guide

How to use json-render-core on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your machine
  • Node.js 16+ with npm — verify with node --version
  • Active project directory where you want to add json-render-core
2

Run the install command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/vercel-labs/json-render --skill json-render-core

Fetches json-render-core from vercel-labs/json-render and configures it for Cursor.

3

Select Cursor when prompted

The CLI shows a list of agents. Use arrow keys and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ────────────────
│ · Cline · Codex · Goose · Windsurf
│ ●Cursor(selected)
│ · Cursor · Aider · Continue
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/json-render-core

Restart Cursor to activate json-render-core. Access via /json-render-core in your agent's command palette.

Security Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.

Documentation

@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

$cond uses the same syntax as visibility conditions ($state, eq, neq, not, arrays for AND). $then and $else can themselves be expressions (recursive).

Components do not use a statePath prop for two-way binding. Instead, use { "$bindState": "/path" } on the natural value prop (e.g. value, checked, pressed).

{
  "color": {
    "$cond": { "$state": "/activeTab", "eq": "home" },
    "$then": "#007AFF",
    "$else": "#8E8E93"
  },
  "label": { "$template": "Welcome, ${/user/name}!" },
  "fullName": {
    "$computed": "fullName",
    "args": {
      "first": { "$state": "/form/firstName" },
      "last": { "$state": "/form/lastName" }
    }
  }
}
import { resolvePropValue, resolveElementProps } from "@json-render/core";

const resolved = resolveElementProps(element.props, { stateModel: myState });

State Watchers

Elements can declare a watch field (top-level, sibling of type/props/children) to trigger actions when state values change:

{
  "type": "Select",
  "props": { "value": { "$bindState": "/form/country" }, "options": ["US", "Canada"] },
  "watch": {
    "/form/country": { "action": "loadCities", "params": { "country": { "$state": "/form/country" } } }
  },
  "children": []
}

Watchers only fire on value changes, not on initial render.

Validation

Built-in validation functions: required, email, url, numeric, minLength, maxLength, min, max, pattern, matches, equalTo, lessThan, greaterThan, requiredIf.

Cross-field validation uses $state expressions in args:

import { check } from "@json-render/core";

check.required("Field is required");
check.matches("/form/password", "Passwords must match");
check.lessThan("/form/endDate", "Must be before end date");
check.greaterThan("/form/startDate", "Must be after start date");
check.requiredIf("/form/enableNotifications", "Required when enabled");

User Prompt Builder

Build structured user prompts with optional spec refinement and state context:

import { buildUserPrompt } from "@json-render/core";

// Fresh generation
buildUserPrompt({ prompt: "create a todo app" });

// Refinement (patch-only mode)
buildUserPrompt({ prompt: "add a toggle", currentSpec: spec });

// With runtime state
buildUserPrompt({ prompt: "show data", state: { todos: 

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Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Steps

  1. 1Install product management skill
  2. 2Start with user story generation for known feature
  3. 3Progress to competitive analysis: research 2-3 competitors
  4. 4Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5Draft stakeholder communications and refine based on feedback
  6. 6Build template library for recurring PM tasks
  7. 7Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use when

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid when

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Related Skills

Reviews

4.836 reviews
  • A
    Ava TandonDec 28, 2024

    We added json-render-core from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • A
    Ava RobinsonDec 20, 2024

    json-render-core is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • S
    Shikha MishraDec 12, 2024

    Useful defaults in json-render-core — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • S
    Sakshi PatilNov 27, 2024

    Registry listing for json-render-core matched our evaluation — installs cleanly and behaves as described in the markdown.

  • D
    Dev RobinsonNov 19, 2024

    Keeps context tight: json-render-core is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • C
    Chen HarrisNov 11, 2024

    json-render-core fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Y
    Yash ThakkerNov 3, 2024

    json-render-core has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • D
    Dhruvi JainOct 22, 2024

    Solid pick for teams standardizing on skills: json-render-core is focused, and the summary matches what you get after install.

  • C
    Chaitanya PatilOct 18, 2024

    json-render-core reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • W
    William ThompsonOct 10, 2024

    json-render-core is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

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