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Convex Create Component

Organization
convex
convex-create-component

Builds reusable Convex components with isolated tables and app-facing APIs. Use for new components, reusable backend modules, integrations, or component boundary work.

Overview

Publisherconvex
Repositoryagent-skills
Skill nameconvex-create-component
Stars
69
Forks
12
Bundled files
6
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.

  • 6 bundled files

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

  • Open source

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

Installation

Install the Convex Create Component 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.
    https://github.com/convex/agent-skills/tree/main/skills/convex-create-component
  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/convex/agent-skills.git /tmp/agent-skills
mkdir -p .claude/skills
cp -r /tmp/agent-skills/skills/convex-create-component .claude/skills/convex-create-component
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Convex Create Component 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 Create Component 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 Create Component 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 Create Component

Create reusable Convex components with clear boundaries and a small app-facing API.

When to Use

  • Creating a new Convex component in an existing app
  • Extracting reusable backend logic into a component
  • Building a third-party integration that should own its own tables and workflows
  • Packaging Convex functionality for reuse across multiple apps

When Not to Use

  • One-off business logic that belongs in the main app
  • Thin utilities that do not need Convex tables or functions
  • App-level orchestration that should stay in convex/
  • Cases where a normal TypeScript library is enough

Workflow

  1. Ask the user what they are building and what the end goal is. If the repo already makes the answer obvious, say so and confirm before proceeding.
  2. Choose the shape using the decision tree below and read the matching reference file.
  3. Decide whether a component is justified. Prefer normal app code or a regular library if the feature does not need isolated tables, backend functions, or reusable persistent state.
  4. Make a short plan for:
    • what tables the component owns
    • what public functions it exposes
    • what data must be passed in from the app (auth, env vars, parent IDs)
    • what stays in the app as wrappers or HTTP mounts
  5. Create the component structure with convex.config.ts, schema.ts, and function files.
  6. Implement functions using the component's own ./_generated/server imports, not the app's generated files.
  7. Wire the component into the app with app.use(...). If the app does not already have convex/convex.config.ts, create it.
  8. Call the component from the app through components.<name> using ctx.runQuery, ctx.runMutation, or ctx.runAction.
  9. If React clients, HTTP callers, or public APIs need access, create wrapper functions in the app instead of exposing component functions directly.
  10. Run npx convex dev and fix codegen, type, or boundary issues before finishing.

Choose the Shape

Ask the user, then pick one path:

GoalShapeReference
Component for this app onlyLocalreferences/local-components.md
Publish or share across appsPackagedreferences/packaged-components.md
User explicitly needs local + shared library codeHybridreferences/hybrid-components.md
Not sureDefault to localreferences/local-components.md

Read exactly one reference file before proceeding.

Default Approach

Unless the user explicitly wants an npm package, default to a local component:

  • Put it under convex/components/<componentName>/
  • Define it with defineComponent(...) in its own convex.config.ts
  • Install it from the app's convex/convex.config.ts with app.use(...)
  • Let npx convex dev generate the component's own _generated/ files

Component Skeleton

A minimal local component with a table and two functions, plus the app wiring.

ts
// convex/components/notifications/convex.config.ts
import { defineComponent } from "convex/server";

export default defineComponent("notifications");
ts
// convex/components/notifications/schema.ts
import { defineSchema, defineTable } from "convex/server";
import { v } from "convex/values";

export default defineSchema({
  notifications: defineTable({
    userId: v.string(),
    message: v.string(),
    read: v.boolean(),
  }).index("by_user_read", ["userId", "read"]),
});
ts
// convex/components/notifications/lib.ts
import { v } from "convex/values";
import { mutation, query } from "./_generated/server.js";

export const send = mutation({
  args: { userId: v.string(), message: v.string() },
  returns: v.id("notifications"),
  handler: async (ctx, args) => {
    return await ctx.db.insert("notifications", {
      userId: args.userId,
      message: args.message,
      read: false,
    });
  },
});

export const listUnread = query({
  args: { userId: v.string() },
  returns: v.array(
    v.object({
      _id: v.id("notifications"),
      _creationTime: v.number(),
      userId: v.string(),
      message: v.string(),
      read: v.boolean(),
    }),
  ),
  handler: async (ctx, args) => {
    return await ctx.db
      .query("notifications")
      .withIndex("by_user_read", (q) =>
        q.eq("userId", args.userId).eq("read", false),
      )
      .collect();
  },
});
ts
// convex/convex.config.ts
import { defineApp } from "convex/server";
import notifications from "./components/notifications/convex.config.js";

const app = defineApp();
app.use(notifications);

export default app;
ts
// convex/notifications.ts  (app-side wrapper)
import { v } from "convex/values";
import { mutation, query } from "./_generated/server";
import { components } from "./_generated/api";
import { getAuthUserId } from "@convex-dev/auth/server";

export const sendNotification = mutation({
  args: { message: v.string() },
  returns: v.null(),
  handler: async (ctx, args) => {
    const userId = await getAuthUserId(ctx);
    if (!userId) throw new Error("Not authenticated");

    await ctx.runMutation(components.notifications.lib.send, {
      userId,
      message: args.message,
    });
    return null;
  },
});

export const myUnread = query({
  args: {},
  handler: async (ctx) => {
    const userId = await getAuthUserId(ctx);
    if (!userId) throw new Error("Not authenticated");

    return await ctx.runQuery(components.notifications.lib.listUnread, {
      userId,
    });
  },
});

Note the reference path shape: a function in convex/components/notifications/lib.ts is called as components.notifications.lib.send from the app.

Critical Rules

  • Keep authentication in the app, because ctx.auth is not available inside components.
  • Keep environment access in the app, because component functions cannot read process.env.
  • Pass parent app IDs across the boundary as strings, because Id types become plain strings in the app-facing ComponentApi.
  • Do not use v.id("parentTable") for app-owned tables inside component args or schema, because the component has no access to the app's table namespace.
  • Import query, mutation, and action from the component's own ./_generated/server, not the app's generated files.
  • Do not expose component functions directly to clients. Create app wrappers when client access is needed, because components are internal and need auth/env wiring the app provides.
  • If the component defines HTTP handlers, mount the routes in the app's convex/http.ts, because components cannot register their own HTTP routes.
  • If the component needs pagination, use paginator from convex-helpers instead of built-in .paginate(), because .paginate() does not work across the component boundary.
  • Define indexes for queried fields instead of using Convex .filter() after a database query.
  • Add args and returns validators to all public component functions, because the component boundary requires explicit type contracts.

Patterns

Authentication and environment access

ts
// Bad: component code cannot rely on app auth or env
const identity = await ctx.auth.getUserIdentity();
const apiKey = process.env.OPENAI_API_KEY;
ts
// Good: the app resolves auth and env, then passes explicit values
const userId = await getAuthUserId(ctx);
if (!userId) throw new Error("Not authenticated");

await ctx.runAction(components.translator.translate, {
  userId,
  apiKey: process.env.OPENAI_API_KEY,
  text: args.text,
});

Client-facing API

ts
// Bad: assuming a component function is directly callable by clients
export const send = components.notifications.send;
ts
// Good: re-export through an app mutation or query
export const sendNotification = mutation({
  args: { message: v.string() },
  returns: v.null(),
  handler: async (ctx, args) => {
    const userId = await getAuthUserId(ctx);
    if (!userId) throw new Error("Not authenticated");

    await ctx.runMutation(components.notifications.lib.send, {
      userId,
      message: args.message,
    });
    return null;
  },
});

IDs across the boundary

ts
// Bad: parent app table IDs are not valid component validators
args: {
  userId: v.id("users"),
}
ts
// Good: treat parent-owned IDs as strings at the boundary
args: {
  userId: v.string(),
}

Advanced Patterns

For additional patterns including function handles for callbacks, deriving validators from schema, static configuration with a globals table, and class-based client wrappers, see references/advanced-patterns.md.

Validation

Try validation in this order:

  1. npx convex codegen --component-dir convex/components/<name>
  2. npx convex codegen
  3. npx convex dev

Important:

  • Fresh repos may fail these commands until CONVEX_DEPLOYMENT is configured.
  • Until codegen runs, component-local ./_generated/* imports and app-side components.<name>... references will not typecheck.
  • If validation blocks on Convex login or deployment setup, stop and ask the user for that exact step instead of guessing.

Reference Files

Read exactly one of these after the user confirms the goal:

  • references/local-components.md
  • references/packaged-components.md
  • references/hybrid-components.md

Official docs: Authoring Components

Checklist

  • Asked the user what they want to build and confirmed the shape
  • Read the matching reference file
  • Confirmed a component is the right abstraction
  • Planned tables, public API, boundaries, and app wrappers
  • Component lives under convex/components/<name>/ (or package layout if publishing)
  • Component imports from its own ./_generated/server
  • Auth, env access, and HTTP routes stay in the app
  • Parent app IDs cross the boundary as v.string()
  • Public functions have args and returns validators
  • Ran npx convex dev and fixed codegen or type issues

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 Create Component AI skill do?

Builds reusable Convex components with isolated tables and app-facing APIs. Use for new components, reusable backend modules, integrations, or component boundary work.

Why use Convex Create Component on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/convex/agent-skills/tree/main/skills/convex-create-component. 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 Create Component?

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 Create Component?

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

Is the Convex Create Component AI skill free?

Yes. It is published on GitHub by convex 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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