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Convex Best Practices

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waynesutton
convex-best-practices

Guidelines for building production-ready Convex apps covering function organization, query patterns, validation, TypeScript usage, error handling, and the Zen of Convex design philosophy

Overview

Publisherwaynesutton
Repositoryconvexskills
Skill nameconvex-best-practices
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 Best Practices 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-best-practices .claude/skills/convex-best-practices
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Convex Best Practices 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 Best Practices 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 Best Practices 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 Best Practices

Build production-ready Convex applications by following established patterns for function organization, query optimization, validation, TypeScript usage, and error handling.

Code Quality

All patterns in this skill comply with @convex-dev/eslint-plugin. Install it for build-time validation:

bash
npm i @convex-dev/eslint-plugin --save-dev
js
// eslint.config.js
import { defineConfig } from "eslint/config";
import convexPlugin from "@convex-dev/eslint-plugin";

export default defineConfig([
  ...convexPlugin.configs.recommended,
]);

The plugin enforces four rules:

RuleWhat it enforces
no-old-registered-function-syntaxObject syntax with handler
require-argument-validatorsargs: {} on all functions
explicit-table-idsTable name in db operations
import-wrong-runtimeNo Node imports in Convex runtime

Docs: https://docs.convex.dev/eslint

Documentation Sources

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

Instructions

The Zen of Convex

  1. Convex manages the hard parts - Let Convex handle caching, real-time sync, and consistency
  2. Functions are the API - Design your functions as your application's interface
  3. Schema is truth - Define your data model explicitly in schema.ts
  4. TypeScript everywhere - Leverage end-to-end type safety
  5. Queries are reactive - Think in terms of subscriptions, not requests

Function Organization

Organize your Convex functions by domain:

typescript
// convex/users.ts - User-related functions
import { query, mutation } from "./_generated/server";
import { v } from "convex/values";

export const get = query({
  args: { userId: v.id("users") },
  returns: v.union(
    v.object({
      _id: v.id("users"),
      _creationTime: v.number(),
      name: v.string(),
      email: v.string(),
    }),
    v.null(),
  ),
  handler: async (ctx, args) => {
    return await ctx.db.get("users", args.userId);
  },
});

Argument and Return Validation

Always define validators for arguments AND return types:

typescript
export const createTask = mutation({
  args: {
    title: v.string(),
    description: v.optional(v.string()),
    priority: v.union(v.literal("low"), v.literal("medium"), v.literal("high")),
  },
  returns: v.id("tasks"),
  handler: async (ctx, args) => {
    return await ctx.db.insert("tasks", {
      title: args.title,
      description: args.description,
      priority: args.priority,
      completed: false,
      createdAt: Date.now(),
    });
  },
});

Query Patterns

Use indexes instead of filters for efficient queries:

typescript
// Schema with index
export default defineSchema({
  tasks: defineTable({
    userId: v.id("users"),
    status: v.string(),
    createdAt: v.number(),
  })
    .index("by_user", ["userId"])
    .index("by_user_and_status", ["userId", "status"]),
});

// Query using index
export const getTasksByUser = query({
  args: { userId: v.id("users") },
  returns: v.array(
    v.object({
      _id: v.id("tasks"),
      _creationTime: v.number(),
      userId: v.id("users"),
      status: v.string(),
      createdAt: v.number(),
    }),
  ),
  handler: async (ctx, args) => {
    return await ctx.db
      .query("tasks")
      .withIndex("by_user", (q) => q.eq("userId", args.userId))
      .order("desc")
      .collect();
  },
});

Error Handling

Use ConvexError for user-facing errors:

typescript
import { ConvexError } from "convex/values";

export const updateTask = mutation({
  args: {
    taskId: v.id("tasks"),
    title: v.string(),
  },
  returns: v.null(),
  handler: async (ctx, args) => {
    const task = await ctx.db.get("tasks", args.taskId);

    if (!task) {
      throw new ConvexError({
        code: "NOT_FOUND",
        message: "Task not found",
      });
    }

    await ctx.db.patch("tasks", args.taskId, { title: args.title });
    return null;
  },
});

Avoiding Write Conflicts (Optimistic Concurrency Control)

Convex uses OCC. Follow these patterns to minimize conflicts:

typescript
// GOOD: Make mutations idempotent
export const completeTask = mutation({
  args: { taskId: v.id("tasks") },
  returns: v.null(),
  handler: async (ctx, args) => {
    const task = await ctx.db.get("tasks", args.taskId);

    // Early return if already complete (idempotent)
    if (!task || task.status === "completed") {
      return null;
    }

    await ctx.db.patch("tasks", args.taskId, {
      status: "completed",
      completedAt: Date.now(),
    });
    return null;
  },
});

// GOOD: Patch directly without reading first when possible
export const updateNote = mutation({
  args: { id: v.id("notes"), content: v.string() },
  returns: v.null(),
  handler: async (ctx, args) => {
    // Patch directly - ctx.db.patch throws if document doesn't exist
    await ctx.db.patch("notes", args.id, { content: args.content });
    return null;
  },
});

// GOOD: Use Promise.all for parallel independent updates
export const reorderItems = mutation({
  args: { itemIds: v.array(v.id("items")) },
  returns: v.null(),
  handler: async (ctx, args) => {
    const updates = args.itemIds.map((id, index) =>
      ctx.db.patch("items", id, { order: index }),
    );
    await Promise.all(updates);
    return null;
  },
});

TypeScript Best Practices

typescript
import { Id, Doc } from "./_generated/dataModel";

// Use Id type for document references
type UserId = Id<"users">;

// Use Doc type for full documents
type User = Doc<"users">;

// Define Record types properly
const userScores: Record<Id<"users">, number> = {};

Internal vs Public Functions

typescript
// Public function - exposed to clients
export const getUser = query({
  args: { userId: v.id("users") },
  returns: v.union(
    v.null(),
    v.object({
      /* ... */
    }),
  ),
  handler: async (ctx, args) => {
    // ...
  },
});

// Internal function - only callable from other Convex functions
export const _updateUserStats = internalMutation({
  args: { userId: v.id("users") },
  returns: v.null(),
  handler: async (ctx, args) => {
    // ...
  },
});

Examples

Complete CRUD Pattern

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

const taskValidator = v.object({
  _id: v.id("tasks"),
  _creationTime: v.number(),
  title: v.string(),
  completed: v.boolean(),
  userId: v.id("users"),
});

export const list = query({
  args: { userId: v.id("users") },
  returns: v.array(taskValidator),
  handler: async (ctx, args) => {
    return await ctx.db
      .query("tasks")
      .withIndex("by_user", (q) => q.eq("userId", args.userId))
      .collect();
  },
});

export const create = mutation({
  args: {
    title: v.string(),
    userId: v.id("users"),
  },
  returns: v.id("tasks"),
  handler: async (ctx, args) => {
    return await ctx.db.insert("tasks", {
      title: args.title,
      completed: false,
      userId: args.userId,
    });
  },
});

export const update = mutation({
  args: {
    taskId: v.id("tasks"),
    title: v.optional(v.string()),
    completed: v.optional(v.boolean()),
  },
  returns: v.null(),
  handler: async (ctx, args) => {
    const { taskId, ...updates } = args;

    // Remove undefined values
    const cleanUpdates = Object.fromEntries(
      Object.entries(updates).filter(([_, v]) => v !== undefined),
    );

    if (Object.keys(cleanUpdates).length > 0) {
      await ctx.db.patch("tasks", taskId, cleanUpdates);
    }
    return null;
  },
});

export const remove = mutation({
  args: { taskId: v.id("tasks") },
  returns: v.null(),
  handler: async (ctx, args) => {
    await ctx.db.delete("tasks", args.taskId);
    return null;
  },
});

Best Practices

  • Never run npx convex deploy unless explicitly instructed
  • Never run any git commands unless explicitly instructed
  • Always define return validators for functions
  • Use indexes for all queries that filter data
  • Make mutations idempotent to handle retries gracefully
  • Use ConvexError for user-facing error messages
  • Organize functions by domain (users.ts, tasks.ts, etc.)
  • Use internal functions for sensitive operations
  • Leverage TypeScript's Id and Doc types

Common Pitfalls

  1. Using filter instead of withIndex - Always define indexes and use withIndex
  2. Missing return validators - Always specify the returns field
  3. Non-idempotent mutations - Check current state before updating
  4. Reading before patching unnecessarily - Patch directly when possible
  5. Not handling null returns - Document IDs might not exist

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 Best Practices AI skill do?

Guidelines for building production-ready Convex apps covering function organization, query patterns, validation, TypeScript usage, error handling, and the Zen of Convex design philosophy

Why use Convex Best Practices on TypingMind?

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

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

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 Best Practices?

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

Is the Convex Best Practices 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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