json-render-core
Schema definition, catalog creation, and spec streaming for AI-driven JSON rendering.
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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
Installation Guide
How to use json-render-core on Cursor
AI-first code editor with Composer
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
Run the install command
Execute the skills CLI command in your project's root directory to begin installation:
Fetches json-render-core from vercel-labs/json-render and configures it for Cursor.
Select Cursor when prompted
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
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: List & Monetize Your Skill
Submit your Claude Code skill and start earning
Get started →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
- 1Install product management skill
- 2Start with user story generation for known feature
- 3Progress to competitive analysis: research 2-3 competitors
- 4Use for roadmap prioritization: apply RICE/ICE scoring
- 5Draft stakeholder communications and refine based on feedback
- 6Build template library for recurring PM tasks
- 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
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
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Reviews
- AAva Tandon★★★★★Dec 28, 2024
We added json-render-core from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- AAva Robinson★★★★★Dec 20, 2024
json-render-core is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- SShikha Mishra★★★★★Dec 12, 2024
Useful defaults in json-render-core — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- SSakshi Patil★★★★★Nov 27, 2024
Registry listing for json-render-core matched our evaluation — installs cleanly and behaves as described in the markdown.
- DDev Robinson★★★★★Nov 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.
- CChen Harris★★★★★Nov 11, 2024
json-render-core fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- YYash Thakker★★★★★Nov 3, 2024
json-render-core has been reliable in day-to-day use. Documentation quality is above average for community skills.
- DDhruvi Jain★★★★★Oct 22, 2024
Solid pick for teams standardizing on skills: json-render-core is focused, and the summary matches what you get after install.
- CChaitanya Patil★★★★★Oct 18, 2024
json-render-core reduced setup friction for our internal harness; good balance of opinion and flexibility.
- WWilliam Thompson★★★★★Oct 10, 2024
json-render-core is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
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