Schema definition, catalog creation, and AI prompt generation for json-render specs.
Works with
Define schemas with typed specs and catalogs using defineSchema , then create catalogs mapping component names to their props and descriptions
Generate AI system prompts from catalogs with optional custom rules; supports spec streaming via JSONL patches with createSpecStreamCompiler
Dynamic prop expressions enable state binding ( $state , $bindState ), conditionals ( $cond ), templating ( $template )
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versioncoreExecute the skills CLI command in your project's root directory to begin installation:
Fetches core from vercel-labs/json-render and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate core. Access via /core in your agent's command palette.
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.
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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
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
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Core package for schema definition, catalog creation, and spec streaming.
defineSchema)defineCatalog)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
});
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",
},
},
});
const systemPrompt = catalog.prompt(); // Uses schema's promptTemplate
const systemPrompt = catalog.prompt({ customRules: ["Rule 1", "Rule 2"] });
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();
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 });
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.
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");
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 with edit modes (default: patch-only)
buildUserPrompt({ prompt: "add a toggle", currentSpec: spec, editModes: ["patch", "merge"] });
// With runtime state
buildUserPrompt(Make data-driven prioritization decisions faster
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
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
core fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Keeps context tight: core is the kind of skill you can hand to a new teammate without a long onboarding doc.
We added core from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
core has been reliable in day-to-day use. Documentation quality is above average for community skills.
Solid pick for teams standardizing on skills: core is focused, and the summary matches what you get after install.
core is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Registry listing for core matched our evaluation — installs cleanly and behaves as described in the markdown.
core is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Solid pick for teams standardizing on skills: core is focused, and the summary matches what you get after install.
Useful defaults in core — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
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