Type-safe database schema definition with indexes, validation, and migration strategies for Convex.
Works with
Supports 13+ validator types including strings, numbers, booleans, document references, arrays, objects, unions, and discriminated unions for flexible data modeling
Enables single-field and compound indexes plus full-text search indexes for optimized query performance
Provides optional and nullable field patterns with clear migration paths for adding required fields and backfilling dat
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionconvex-schema-validatorExecute the skills CLI command in your project's root directory to begin installation:
Fetches convex-schema-validator from waynesutton/convexskills 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 convex-schema-validator. Access via /convex-schema-validator 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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Define and validate database schemas in Convex with proper typing, index configuration, optional fields, unions, and strategies for schema migrations.
Before implementing, do not assume; fetch the latest documentation:
// convex/schema.ts
import { defineSchema, defineTable } from "convex/server";
import { v } from "convex/values";
export default defineSchema({
users: defineTable({
name: v.string(),
email: v.string(),
avatarUrl: v.optional(v.string()),
createdAt: v.number(),
}),
tasks: defineTable({
title: v.string(),
description: v.optional(v.string()),
completed: v.boolean(),
userId: v.id("users"),
priority: v.union(
v.literal("low"),
v.literal("medium"),
v.literal("high")
),
}),
});
| Validator | TypeScript Type | Example |
|---|---|---|
v.string() |
string |
"hello" |
v.number() |
number |
42, 3.14 |
v.boolean() |
boolean |
true, false |
v.null() |
null |
null |
v.int64() |
bigint |
9007199254740993n |
v.bytes() |
ArrayBuffer |
Binary data |
v.id("table") |
Id<"table"> |
Document reference |
v.array(v) |
T[] |
[1, 2, 3] |
v.object({}) |
{ ... } |
{ name: "..." } |
v.optional(v) |
T | undefined |
Optional field |
v.union(...) |
T1 | T2 |
Multiple types |
v.literal(x) |
"x" |
Exact value |
v.any() |
any |
Any value |
v.record(k, v) |
Record<K, V> |
Dynamic keys |
export default defineSchema({
messages: defineTable({
channelId: v.id("channels"),
authorId: v.id("users"),
content: v.string(),
sentAt: v.number(),
})
// Single field index
.index("by_channel", ["channelId"])
// Compound index
.index("by_channel_and_author", ["channelId", "authorId"])
// Index for sorting
.index("by_channel_and_time", ["channelId", "sentAt"]),
// Full-text search index
articles: defineTable({
title: v.string(),
body: v.string(),
category: v.string(),
})
.searchIndex("search_content", {
searchField: "body",
filterFields: ["category"],
}),
});
export default defineSchema({
// Nested objects
profiles: defineTable({
userId: v.id("users"),
settings: v.object({
theme: v.union(v.literal("light"), v.literal("dark")),
notifications: v.object({
email: v.boolean(),
push: v.boolean(),
}),
}),
}),
// Arrays of objects
orders: defineTable({
customerId: v.id("users"),
items: v.array(v.object({
productId: v.id("products"),
quantity: v.number(),
price: v.number(),
})),
status: v.union(
v.literal("pending"),
v.literal("processing"),
v.literal("shipped"),
v.literal("delivered")
),
}),
// Record type for dynamic keys
analytics: defineTable({
date: v.string(),
metrics: v.record(v.string(), v.number()),
}),
});
export default defineSchema({
events: defineTable(
v.union(
v.object({
type: v.literal("user_signup"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
pproenca/dot-skills
mattpocock/skills
Registry listing for convex-schema-validator matched our evaluation — installs cleanly and behaves as described in the markdown.
convex-schema-validator reduced setup friction for our internal harness; good balance of opinion and flexibility.
Solid pick for teams standardizing on skills: convex-schema-validator is focused, and the summary matches what you get after install.
I recommend convex-schema-validator for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
convex-schema-validator has been reliable in day-to-day use. Documentation quality is above average for community skills.
Keeps context tight: convex-schema-validator is the kind of skill you can hand to a new teammate without a long onboarding doc.
convex-schema-validator reduced setup friction for our internal harness; good balance of opinion and flexibility.
convex-schema-validator has been reliable in day-to-day use. Documentation quality is above average for community skills.
Registry listing for convex-schema-validator matched our evaluation — installs cleanly and behaves as described in the markdown.
Keeps context tight: convex-schema-validator is the kind of skill you can hand to a new teammate without a long onboarding doc.
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