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Convex Component Authoring

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waynesutton
convex-component-authoring

How to create, structure, and publish self-contained Convex components with proper isolation, exports, and dependency management

Overview

Publisherwaynesutton
Repositoryconvexskills
Skill nameconvex-component-authoring
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 Component Authoring 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-component-authoring .claude/skills/convex-component-authoring
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Convex Component Authoring 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 Component Authoring 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 Component Authoring 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 Component Authoring

Create self-contained, reusable Convex components with proper isolation, exports, and dependency management for sharing across projects.

Documentation Sources

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

Instructions

What Are Convex Components?

Convex components are self-contained packages that include:

  • Database tables (isolated from the main app)
  • Functions (queries, mutations, actions)
  • TypeScript types and validators
  • Optional frontend hooks

Component Structure

my-convex-component/
├── package.json
├── tsconfig.json
├── README.md
├── src/
│   ├── index.ts           # Main exports
│   ├── component.ts       # Component definition
│   ├── schema.ts          # Component schema
│   └── functions/
│       ├── queries.ts
│       ├── mutations.ts
│       └── actions.ts
└── convex.config.ts       # Component configuration

Creating a Component

1. Component Configuration
typescript
// convex.config.ts
import { defineComponent } from "convex/server";

export default defineComponent("myComponent");
2. Component Schema
typescript
// src/schema.ts
import { defineSchema, defineTable } from "convex/server";
import { v } from "convex/values";

export default defineSchema({
  // Tables are isolated to this component
  items: defineTable({
    name: v.string(),
    data: v.any(),
    createdAt: v.number(),
  }).index("by_name", ["name"]),
  
  config: defineTable({
    key: v.string(),
    value: v.any(),
  }).index("by_key", ["key"]),
});
3. Component Definition
typescript
// src/component.ts
import { defineComponent, ComponentDefinition } from "convex/server";
import schema from "./schema";
import * as queries from "./functions/queries";
import * as mutations from "./functions/mutations";

const component = defineComponent("myComponent", {
  schema,
  functions: {
    ...queries,
    ...mutations,
  },
});

export default component;
4. Component Functions
typescript
// src/functions/queries.ts
import { query } from "../_generated/server";
import { v } from "convex/values";

export const list = query({
  args: {
    limit: v.optional(v.number()),
  },
  returns: v.array(v.object({
    _id: v.id("items"),
    name: v.string(),
    data: v.any(),
    createdAt: v.number(),
  })),
  handler: async (ctx, args) => {
    return await ctx.db
      .query("items")
      .order("desc")
      .take(args.limit ?? 10);
  },
});

export const get = query({
  args: { name: v.string() },
  returns: v.union(v.object({
    _id: v.id("items"),
    name: v.string(),
    data: v.any(),
  }), v.null()),
  handler: async (ctx, args) => {
    return await ctx.db
      .query("items")
      .withIndex("by_name", (q) => q.eq("name", args.name))
      .unique();
  },
});
typescript
// src/functions/mutations.ts
import { mutation } from "../_generated/server";
import { v } from "convex/values";

export const create = mutation({
  args: {
    name: v.string(),
    data: v.any(),
  },
  returns: v.id("items"),
  handler: async (ctx, args) => {
    return await ctx.db.insert("items", {
      name: args.name,
      data: args.data,
      createdAt: Date.now(),
    });
  },
});

export const update = mutation({
  args: {
    id: v.id("items"),
    data: v.any(),
  },
  returns: v.null(),
  handler: async (ctx, args) => {
    await ctx.db.patch(args.id, { data: args.data });
    return null;
  },
});

export const remove = mutation({
  args: { id: v.id("items") },
  returns: v.null(),
  handler: async (ctx, args) => {
    await ctx.db.delete(args.id);
    return null;
  },
});
5. Main Exports
typescript
// src/index.ts
export { default as component } from "./component";
export * from "./functions/queries";
export * from "./functions/mutations";

// Export types for consumers
export type { Id } from "./_generated/dataModel";

Using a Component

typescript
// In the consuming app's convex/convex.config.ts
import { defineApp } from "convex/server";
import myComponent from "my-convex-component";

const app = defineApp();

app.use(myComponent, { name: "myComponent" });

export default app;
typescript
// In the consuming app's code
import { useQuery, useMutation } from "convex/react";
import { api } from "../convex/_generated/api";

function MyApp() {
  // Access component functions through the app's API
  const items = useQuery(api.myComponent.list, { limit: 10 });
  const createItem = useMutation(api.myComponent.create);
  
  return (
    <div>
      {items?.map((item) => (
        <div key={item._id}>{item.name}</div>
      ))}
      <button onClick={() => createItem({ name: "New", data: {} })}>
        Add Item
      </button>
    </div>
  );
}

Component Configuration Options

typescript
// convex/convex.config.ts
import { defineApp } from "convex/server";
import myComponent from "my-convex-component";

const app = defineApp();

// Basic usage
app.use(myComponent);

// With custom name
app.use(myComponent, { name: "customName" });

// Multiple instances
app.use(myComponent, { name: "instance1" });
app.use(myComponent, { name: "instance2" });

export default app;

Providing Component Hooks

typescript
// src/hooks.ts
import { useQuery, useMutation } from "convex/react";
import { FunctionReference } from "convex/server";

// Type-safe hooks for component consumers
export function useMyComponent(api: {
  list: FunctionReference<"query">;
  create: FunctionReference<"mutation">;
}) {
  const items = useQuery(api.list, {});
  const createItem = useMutation(api.create);
  
  return {
    items,
    createItem,
    isLoading: items === undefined,
  };
}

Publishing a Component

package.json
json
{
  "name": "my-convex-component",
  "version": "1.0.0",
  "description": "A reusable Convex component",
  "main": "dist/index.js",
  "types": "dist/index.d.ts",
  "files": [
    "dist",
    "convex.config.ts"
  ],
  "scripts": {
    "build": "tsc",
    "prepublishOnly": "npm run build"
  },
  "peerDependencies": {
    "convex": "^1.0.0"
  },
  "devDependencies": {
    "convex": "^1.17.0",
    "typescript": "^5.0.0"
  },
  "keywords": [
    "convex",
    "component"
  ]
}
tsconfig.json
json
{
  "compilerOptions": {
    "target": "ES2020",
    "module": "ESNext",
    "moduleResolution": "bundler",
    "declaration": true,
    "outDir": "dist",
    "strict": true,
    "esModuleInterop": true,
    "skipLibCheck": true
  },
  "include": ["src/**/*"],
  "exclude": ["node_modules", "dist"]
}

Examples

Rate Limiter Component

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

export default defineSchema({
  requests: defineTable({
    key: v.string(),
    timestamp: v.number(),
  })
    .index("by_key", ["key"])
    .index("by_key_and_time", ["key", "timestamp"]),
});
typescript
// rate-limiter/src/functions/mutations.ts
import { mutation } from "../_generated/server";
import { v } from "convex/values";

export const checkLimit = mutation({
  args: {
    key: v.string(),
    limit: v.number(),
    windowMs: v.number(),
  },
  returns: v.object({
    allowed: v.boolean(),
    remaining: v.number(),
    resetAt: v.number(),
  }),
  handler: async (ctx, args) => {
    const now = Date.now();
    const windowStart = now - args.windowMs;
    
    // Clean old entries
    const oldEntries = await ctx.db
      .query("requests")
      .withIndex("by_key_and_time", (q) => 
        q.eq("key", args.key).lt("timestamp", windowStart)
      )
      .collect();
    
    for (const entry of oldEntries) {
      await ctx.db.delete(entry._id);
    }
    
    // Count current window
    const currentRequests = await ctx.db
      .query("requests")
      .withIndex("by_key", (q) => q.eq("key", args.key))
      .collect();
    
    const remaining = Math.max(0, args.limit - currentRequests.length);
    const allowed = remaining > 0;
    
    if (allowed) {
      await ctx.db.insert("requests", {
        key: args.key,
        timestamp: now,
      });
    }
    
    const oldestRequest = currentRequests[0];
    const resetAt = oldestRequest 
      ? oldestRequest.timestamp + args.windowMs 
      : now + args.windowMs;
    
    return { allowed, remaining: remaining - (allowed ? 1 : 0), resetAt };
  },
});
typescript
// Usage in consuming app
import { useMutation } from "convex/react";
import { api } from "../convex/_generated/api";

function useRateLimitedAction() {
  const checkLimit = useMutation(api.rateLimiter.checkLimit);
  
  return async (action: () => Promise<void>) => {
    const result = await checkLimit({
      key: "user-action",
      limit: 10,
      windowMs: 60000,
    });
    
    if (!result.allowed) {
      throw new Error(`Rate limited. Try again at ${new Date(result.resetAt)}`);
    }
    
    await action();
  };
}

Best Practices

  • Never run npx convex deploy unless explicitly instructed
  • Never run any git commands unless explicitly instructed
  • Keep component tables isolated (don't reference main app tables)
  • Export clear TypeScript types for consumers
  • Document all public functions and their arguments
  • Use semantic versioning for component releases
  • Include comprehensive README with examples
  • Test components in isolation before publishing

Common Pitfalls

  1. Cross-referencing tables - Component tables should be self-contained
  2. Missing type exports - Export all necessary types
  3. Hardcoded configuration - Use component options for customization
  4. No versioning - Follow semantic versioning
  5. Poor documentation - Document all public APIs

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 Component Authoring AI skill do?

How to create, structure, and publish self-contained Convex components with proper isolation, exports, and dependency management

Why use Convex Component Authoring on TypingMind?

Because you install it once and use it with any model. Convex Component Authoring 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 Component Authoring in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/waynesutton/convexskills/tree/main/skills/convex-component-authoring. 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 Component Authoring?

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 Component Authoring?

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

Is the Convex Component Authoring 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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