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Convex Schema Validator

Community
waynesutton
convex-schema-validator

Defining and validating database schemas with proper typing, index configuration, optional fields, unions, and migration strategies for schema changes

Overview

Publisherwaynesutton
Repositoryconvexskills
Skill nameconvex-schema-validator
Stars
404
Forks
32
Bundled files
3
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by waynesutton on GitHub. Read the source before you install it.

Installation

Install the Convex Schema Validator AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/waynesutton/convexskills.git /tmp/convexskills
mkdir -p .claude/skills
cp -r /tmp/convexskills/skills/convex-schema-validator .claude/skills/convex-schema-validator
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Convex Schema Validator in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Convex Schema Validator on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Convex Schema Validator is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Convex Schema Validator

Define and validate database schemas in Convex with proper typing, index configuration, optional fields, unions, and strategies for schema migrations.

Documentation Sources

Before implementing, do not assume; fetch the latest documentation:

Instructions

Basic Schema Definition

typescript
// 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 Types

ValidatorTypeScript TypeExample
v.string()string"hello"
v.number()number42, 3.14
v.boolean()booleantrue, false
v.null()nullnull
v.int64()bigint9007199254740993n
v.bytes()ArrayBufferBinary data
v.id("table")Id<"table">Document reference
v.array(v)T[][1, 2, 3]
v.object({}){ ... }{ name: "..." }
v.optional(v)T | undefinedOptional field
v.union(...)T1 | T2Multiple types
v.literal(x)"x"Exact value
v.any()anyAny value
v.record(k, v)Record<K, V>Dynamic keys

Index Configuration

typescript
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"],
    }),
});

Complex Types

typescript
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()),
  }),
});

Discriminated Unions

typescript
export default defineSchema({
  events: defineTable(
    v.union(
      v.object({
        type: v.literal("user_signup"),
        userId: v.id("users"),
        email: v.string(),
      }),
      v.object({
        type: v.literal("purchase"),
        userId: v.id("users"),
        orderId: v.id("orders"),
        amount: v.number(),
      }),
      v.object({
        type: v.literal("page_view"),
        sessionId: v.string(),
        path: v.string(),
      })
    )
  ).index("by_type", ["type"]),
});

Optional vs Nullable Fields

typescript
export default defineSchema({
  items: defineTable({
    // Optional: field may not exist
    description: v.optional(v.string()),
    
    // Nullable: field exists but can be null
    deletedAt: v.union(v.number(), v.null()),
    
    // Optional and nullable
    notes: v.optional(v.union(v.string(), v.null())),
  }),
});

Index Naming Convention

Always include all indexed fields in the index name:

typescript
export default defineSchema({
  posts: defineTable({
    authorId: v.id("users"),
    categoryId: v.id("categories"),
    publishedAt: v.number(),
    status: v.string(),
  })
    // Good: descriptive names
    .index("by_author", ["authorId"])
    .index("by_author_and_category", ["authorId", "categoryId"])
    .index("by_category_and_status", ["categoryId", "status"])
    .index("by_status_and_published", ["status", "publishedAt"]),
});

Schema Migration Strategies

Adding New Fields
typescript
// Before
users: defineTable({
  name: v.string(),
  email: v.string(),
})

// After - add as optional first
users: defineTable({
  name: v.string(),
  email: v.string(),
  avatarUrl: v.optional(v.string()), // New optional field
})
Backfilling Data
typescript
// convex/migrations.ts
import { internalMutation } from "./_generated/server";
import { v } from "convex/values";

export const backfillAvatars = internalMutation({
  args: {},
  returns: v.number(),
  handler: async (ctx) => {
    const users = await ctx.db
      .query("users")
      .filter((q) => q.eq(q.field("avatarUrl"), undefined))
      .take(100);

    for (const user of users) {
      await ctx.db.patch(user._id, {
        avatarUrl: `https://api.dicebear.com/7.x/initials/svg?seed=${user.name}`,
      });
    }

    return users.length;
  },
});
Making Optional Fields Required
typescript
// Step 1: Backfill all null values
// Step 2: Update schema to required
users: defineTable({
  name: v.string(),
  email: v.string(),
  avatarUrl: v.string(), // Now required after backfill
})

Examples

Complete E-commerce Schema

typescript
// convex/schema.ts
import { defineSchema, defineTable } from "convex/server";
import { v } from "convex/values";

export default defineSchema({
  users: defineTable({
    email: v.string(),
    name: v.string(),
    role: v.union(v.literal("customer"), v.literal("admin")),
    createdAt: v.number(),
  })
    .index("by_email", ["email"])
    .index("by_role", ["role"]),

  products: defineTable({
    name: v.string(),
    description: v.string(),
    price: v.number(),
    category: v.string(),
    inventory: v.number(),
    isActive: v.boolean(),
  })
    .index("by_category", ["category"])
    .index("by_active_and_category", ["isActive", "category"])
    .searchIndex("search_products", {
      searchField: "name",
      filterFields: ["category", "isActive"],
    }),

  orders: defineTable({
    userId: v.id("users"),
    items: v.array(v.object({
      productId: v.id("products"),
      quantity: v.number(),
      priceAtPurchase: v.number(),
    })),
    total: v.number(),
    status: v.union(
      v.literal("pending"),
      v.literal("paid"),
      v.literal("shipped"),
      v.literal("delivered"),
      v.literal("cancelled")
    ),
    shippingAddress: v.object({
      street: v.string(),
      city: v.string(),
      state: v.string(),
      zip: v.string(),
      country: v.string(),
    }),
    createdAt: v.number(),
    updatedAt: v.number(),
  })
    .index("by_user", ["userId"])
    .index("by_user_and_status", ["userId", "status"])
    .index("by_status", ["status"]),

  reviews: defineTable({
    productId: v.id("products"),
    userId: v.id("users"),
    rating: v.number(),
    comment: v.optional(v.string()),
    createdAt: v.number(),
  })
    .index("by_product", ["productId"])
    .index("by_user", ["userId"]),
});

Using Schema Types in Functions

typescript
// convex/products.ts
import { query, mutation } from "./_generated/server";
import { v } from "convex/values";
import { Doc, Id } from "./_generated/dataModel";

// Use Doc type for full documents
type Product = Doc<"products">;

// Use Id type for references
type ProductId = Id<"products">;

export const get = query({
  args: { productId: v.id("products") },
  returns: v.union(
    v.object({
      _id: v.id("products"),
      _creationTime: v.number(),
      name: v.string(),
      description: v.string(),
      price: v.number(),
      category: v.string(),
      inventory: v.number(),
      isActive: v.boolean(),
    }),
    v.null()
  ),
  handler: async (ctx, args): Promise<Product | null> => {
    return await ctx.db.get(args.productId);
  },
});

Best Practices

  • Never run npx convex deploy unless explicitly instructed
  • Never run any git commands unless explicitly instructed
  • Always define explicit schemas rather than relying on inference
  • Use descriptive index names that include all indexed fields
  • Start with optional fields when adding new columns
  • Use discriminated unions for polymorphic data
  • Validate data at the schema level, not just in functions
  • Plan index strategy based on query patterns

Common Pitfalls

  1. Missing indexes for queries - Every withIndex needs a corresponding schema index
  2. Wrong index field order - Fields must be queried in order defined
  3. Using v.any() excessively - Lose type safety benefits
  4. Not making new fields optional - Breaks existing data
  5. Forgetting system fields - _id and _creationTime are automatic

References

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Convex Schema Validator AI skill do?

Defining and validating database schemas with proper typing, index configuration, optional fields, unions, and migration strategies for schema changes

Why use Convex Schema Validator on TypingMind?

Because you install it once and use it with any model. Convex Schema Validator is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Convex Schema Validator in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/waynesutton/convexskills/tree/main/skills/convex-schema-validator. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Convex Schema Validator?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Convex Schema Validator?

As many as you like. As long as a model supports skills, you can use Convex Schema Validator with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Convex Schema Validator AI skill free?

Yes. It is published on GitHub by waynesutton under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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