skill
Convex Create Component
نبذة
# 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:
| Goal | Shape | Reference | | ------------------------------------------------- | ---------------- | ----------------------------------- | | Component for this app only | Local | `references/local-components.md` | | Publish or share across apps | Packaged | `references/packaged-components.md` | | User explicitly needs local + shared library code | Hybrid | `references/hybrid-components.md` | | Not sure | Default to local | `references/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 { mut
التثبيت
شغل هذا الأمر
npx skills add get-convex/agent-skillsيعمل مع
خطوات التثبيت
Install with `npx skills add get-convex/agent-skills`, or clone the repository and copy the `skills/convex-create-component` folder into your Claude skills directory.
أصول ذات صلة
مختارات أخرى في الإنتاجية والمكتب.
npm install @modelcontextprotocol/server-time
git clone https://github.com/anthropics/knowledge-work-plugins && cp -r knowledge-work-plugins/operations/skills/change-request ~/.claude/skills/
git clone https://github.com/anthropics/knowledge-work-plugins && cp -r knowledge-work-plugins/product-management/skills/product-brainstorming ~/.claude/skills/
git clone https://github.com/anthropics/knowledge-work-plugins && cp -r knowledge-work-plugins/operations/skills/compliance-tracking ~/.claude/skills/
git clone https://github.com/anthropics/knowledge-work-plugins && cp -r knowledge-work-plugins/operations/skills/process-optimization ~/.claude/skills/
git clone https://github.com/anthropics/knowledge-work-plugins && cp -r knowledge-work-plugins/enterprise-search/skills/search-strategy ~/.claude/skills/
افحص قبل التثبيت
شغل أي مصدر عبر فحوصاتنا - الظهور في الذكاء الاصطناعي والأمان والأداء واكتشاف التقنيات.
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الأمان
محلل سرعة الصفحة
الأداء
اختبار جودة المحتوى العربي بالذكاء الاصطناعي
جودة المحتوى
مختبر وكلاء الذكاء الاصطناعي
اختبار الذكاء الاصطناعي
كاشف منصة الموقع
الترحيل
تدقيق الظهور في محركات الذكاء الاصطناعي
الظهور في الذكاء الاصطناعي
مولد ملف llms.txt
الظهور في الذكاء الاصطناعي
مقياس سهولة القراءة بالعربية
جودة المحتوى
منشئ البيانات المنظمة
الظهور في الذكاء الاصطناعي
حاسبة تكاليف الذكاء الاصطناعي
اختبار الذكاء الاصطناعي
محلل العناوين العربية
جودة المحتوى