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Convex Realtime

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waynesutton
convex-realtime

Patterns for building reactive apps including subscription management, optimistic updates, cache behavior, and paginated queries with cursor-based loading

Overview

Publisherwaynesutton
Repositoryconvexskills
Skill nameconvex-realtime
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 Realtime 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-realtime .claude/skills/convex-realtime
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Convex Realtime 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 Realtime 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 Realtime 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 Realtime

Build reactive applications with Convex's real-time subscriptions, optimistic updates, intelligent caching, and cursor-based pagination.

Documentation Sources

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

Instructions

How Convex Realtime Works

  1. Automatic Subscriptions - useQuery creates a subscription that updates automatically
  2. Smart Caching - Query results are cached and shared across components
  3. Consistency - All subscriptions see a consistent view of the database
  4. Efficient Updates - Only re-renders when relevant data changes

Basic Subscriptions

typescript
// React component with real-time data
import { useQuery } from "convex/react";
import { api } from "../convex/_generated/api";

function TaskList({ userId }: { userId: Id<"users"> }) {
  // Automatically subscribes and updates in real-time
  const tasks = useQuery(api.tasks.list, { userId });

  if (tasks === undefined) {
    return <div>Loading...</div>;
  }

  return (
    <ul>
      {tasks.map((task) => (
        <li key={task._id}>{task.title}</li>
      ))}
    </ul>
  );
}

Conditional Queries

typescript
import { useQuery } from "convex/react";
import { api } from "../convex/_generated/api";

function UserProfile({ userId }: { userId: Id<"users"> | null }) {
  // Skip query when userId is null
  const user = useQuery(
    api.users.get,
    userId ? { userId } : "skip"
  );

  if (userId === null) {
    return <div>Select a user</div>;
  }

  if (user === undefined) {
    return <div>Loading...</div>;
  }

  return <div>{user.name}</div>;
}

Mutations with Real-time Updates

typescript
import { useMutation, useQuery } from "convex/react";
import { api } from "../convex/_generated/api";

function TaskManager({ userId }: { userId: Id<"users"> }) {
  const tasks = useQuery(api.tasks.list, { userId });
  const createTask = useMutation(api.tasks.create);
  const toggleTask = useMutation(api.tasks.toggle);

  const handleCreate = async (title: string) => {
    // Mutation triggers automatic re-render when data changes
    await createTask({ title, userId });
  };

  const handleToggle = async (taskId: Id<"tasks">) => {
    await toggleTask({ taskId });
  };

  return (
    <div>
      <button onClick={() => handleCreate("New Task")}>Add Task</button>
      <ul>
        {tasks?.map((task) => (
          <li key={task._id} onClick={() => handleToggle(task._id)}>
            {task.completed ? "✓" : "○"} {task.title}
          </li>
        ))}
      </ul>
    </div>
  );
}

Optimistic Updates

Show changes immediately before server confirmation:

typescript
import { useMutation, useQuery } from "convex/react";
import { api } from "../convex/_generated/api";
import { Id } from "../convex/_generated/dataModel";

function TaskItem({ task }: { task: Task }) {
  const toggleTask = useMutation(api.tasks.toggle).withOptimisticUpdate(
    (localStore, args) => {
      const { taskId } = args;
      const currentValue = localStore.getQuery(api.tasks.get, { taskId });
      
      if (currentValue !== undefined) {
        localStore.setQuery(api.tasks.get, { taskId }, {
          ...currentValue,
          completed: !currentValue.completed,
        });
      }
    }
  );

  return (
    <div onClick={() => toggleTask({ taskId: task._id })}>
      {task.completed ? "✓" : "○"} {task.title}
    </div>
  );
}

Optimistic Updates for Lists

typescript
import { useMutation } from "convex/react";
import { api } from "../convex/_generated/api";

function useCreateTask(userId: Id<"users">) {
  return useMutation(api.tasks.create).withOptimisticUpdate(
    (localStore, args) => {
      const { title, userId } = args;
      const currentTasks = localStore.getQuery(api.tasks.list, { userId });
      
      if (currentTasks !== undefined) {
        // Add optimistic task to the list
        const optimisticTask = {
          _id: crypto.randomUUID() as Id<"tasks">,
          _creationTime: Date.now(),
          title,
          userId,
          completed: false,
        };
        
        localStore.setQuery(api.tasks.list, { userId }, [
          optimisticTask,
          ...currentTasks,
        ]);
      }
    }
  );
}

Cursor-Based Pagination

typescript
// convex/messages.ts
import { query } from "./_generated/server";
import { v } from "convex/values";
import { paginationOptsValidator } from "convex/server";

export const listPaginated = query({
  args: {
    channelId: v.id("channels"),
    paginationOpts: paginationOptsValidator,
  },
  handler: async (ctx, args) => {
    return await ctx.db
      .query("messages")
      .withIndex("by_channel", (q) => q.eq("channelId", args.channelId))
      .order("desc")
      .paginate(args.paginationOpts);
  },
});
typescript
// React component with pagination
import { usePaginatedQuery } from "convex/react";
import { api } from "../convex/_generated/api";

function MessageList({ channelId }: { channelId: Id<"channels"> }) {
  const { results, status, loadMore } = usePaginatedQuery(
    api.messages.listPaginated,
    { channelId },
    { initialNumItems: 20 }
  );

  return (
    <div>
      {results.map((message) => (
        <div key={message._id}>{message.content}</div>
      ))}
      
      {status === "CanLoadMore" && (
        <button onClick={() => loadMore(20)}>Load More</button>
      )}
      
      {status === "LoadingMore" && <div>Loading...</div>}
      
      {status === "Exhausted" && <div>No more messages</div>}
    </div>
  );
}

Infinite Scroll Pattern

typescript
import { usePaginatedQuery } from "convex/react";
import { useEffect, useRef } from "react";
import { api } from "../convex/_generated/api";

function InfiniteMessageList({ channelId }: { channelId: Id<"channels"> }) {
  const { results, status, loadMore } = usePaginatedQuery(
    api.messages.listPaginated,
    { channelId },
    { initialNumItems: 20 }
  );
  
  const observerRef = useRef<IntersectionObserver>();
  const loadMoreRef = useRef<HTMLDivElement>(null);

  useEffect(() => {
    if (observerRef.current) {
      observerRef.current.disconnect();
    }

    observerRef.current = new IntersectionObserver((entries) => {
      if (entries[0].isIntersecting && status === "CanLoadMore") {
        loadMore(20);
      }
    });

    if (loadMoreRef.current) {
      observerRef.current.observe(loadMoreRef.current);
    }

    return () => observerRef.current?.disconnect();
  }, [status, loadMore]);

  return (
    <div>
      {results.map((message) => (
        <div key={message._id}>{message.content}</div>
      ))}
      <div ref={loadMoreRef} style={{ height: 1 }} />
      {status === "LoadingMore" && <div>Loading...</div>}
    </div>
  );
}

Multiple Subscriptions

typescript
import { useQuery } from "convex/react";
import { api } from "../convex/_generated/api";

function Dashboard({ userId }: { userId: Id<"users"> }) {
  // Multiple subscriptions update independently
  const user = useQuery(api.users.get, { userId });
  const tasks = useQuery(api.tasks.list, { userId });
  const notifications = useQuery(api.notifications.unread, { userId });

  const isLoading = user === undefined || 
                    tasks === undefined || 
                    notifications === undefined;

  if (isLoading) {
    return <div>Loading...</div>;
  }

  return (
    <div>
      <h1>Welcome, {user.name}</h1>
      <p>You have {tasks.length} tasks</p>
      <p>{notifications.length} unread notifications</p>
    </div>
  );
}

Examples

Real-time Chat Application

typescript
// convex/messages.ts
import { query, mutation } from "./_generated/server";
import { v } from "convex/values";

export const list = query({
  args: { channelId: v.id("channels") },
  returns: v.array(v.object({
    _id: v.id("messages"),
    _creationTime: v.number(),
    content: v.string(),
    authorId: v.id("users"),
    authorName: v.string(),
  })),
  handler: async (ctx, args) => {
    const messages = await ctx.db
      .query("messages")
      .withIndex("by_channel", (q) => q.eq("channelId", args.channelId))
      .order("desc")
      .take(100);

    // Enrich with author names
    return Promise.all(
      messages.map(async (msg) => {
        const author = await ctx.db.get(msg.authorId);
        return {
          ...msg,
          authorName: author?.name ?? "Unknown",
        };
      })
    );
  },
});

export const send = mutation({
  args: {
    channelId: v.id("channels"),
    authorId: v.id("users"),
    content: v.string(),
  },
  returns: v.id("messages"),
  handler: async (ctx, args) => {
    return await ctx.db.insert("messages", {
      channelId: args.channelId,
      authorId: args.authorId,
      content: args.content,
    });
  },
});
typescript
// ChatRoom.tsx
import { useQuery, useMutation } from "convex/react";
import { api } from "../convex/_generated/api";
import { useState, useRef, useEffect } from "react";

function ChatRoom({ channelId, userId }: Props) {
  const messages = useQuery(api.messages.list, { channelId });
  const sendMessage = useMutation(api.messages.send);
  const [input, setInput] = useState("");
  const messagesEndRef = useRef<HTMLDivElement>(null);

  // Auto-scroll to bottom on new messages
  useEffect(() => {
    messagesEndRef.current?.scrollIntoView({ behavior: "smooth" });
  }, [messages]);

  const handleSend = async (e: React.FormEvent) => {
    e.preventDefault();
    if (!input.trim()) return;

    await sendMessage({
      channelId,
      authorId: userId,
      content: input.trim(),
    });
    setInput("");
  };

  return (
    <div className="chat-room">
      <div className="messages">
        {messages?.map((msg) => (
          <div key={msg._id} className="message">
            <strong>{msg.authorName}:</strong> {msg.content}
          </div>
        ))}
        <div ref={messagesEndRef} />
      </div>
      
      <form onSubmit={handleSend}>
        <input
          value={input}
          onChange={(e) => setInput(e.target.value)}
          placeholder="Type a message..."
        />
        <button type="submit">Send</button>
      </form>
    </div>
  );
}

Best Practices

  • Never run npx convex deploy unless explicitly instructed
  • Never run any git commands unless explicitly instructed
  • Use "skip" for conditional queries instead of conditionally calling hooks
  • Implement optimistic updates for better perceived performance
  • Use usePaginatedQuery for large datasets
  • Handle undefined state (loading) explicitly
  • Avoid unnecessary re-renders by memoizing derived data

Common Pitfalls

  1. Conditional hook calls - Use "skip" instead of if statements
  2. Not handling loading state - Always check for undefined
  3. Missing optimistic update rollback - Optimistic updates auto-rollback on error
  4. Over-fetching with pagination - Use appropriate page sizes
  5. Ignoring subscription cleanup - React handles this automatically

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 Realtime AI skill do?

Patterns for building reactive apps including subscription management, optimistic updates, cache behavior, and paginated queries with cursor-based loading

Why use Convex Realtime on TypingMind?

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

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

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 Realtime?

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

Is the Convex Realtime 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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