Design and build isolated, reusable Convex backend components with clear boundaries and app-facing wrappers.
Works with
Supports three component shapes: local (single-app), packaged (npm), and hybrid (both), with a decision tree to choose the right fit
Enforces architectural boundaries: components own their tables and functions, while the app handles authentication, environment access, and client-facing wrappers
Provides a complete workflow from planning (tables, public API, data flow) through
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionconvex-create-componentExecute the skills CLI command in your project's root directory to begin installation:
Fetches convex-create-component from get-convex/agent-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate convex-create-component. Access via /convex-create-component in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
0
total installs
0
this week
21
GitHub stars
0
upvotes
Run in your terminal
0
installs
0
this week
21
stars
Create reusable Convex components with clear boundaries and a small app-facing API.
convex/convex.config.ts, schema.ts, and function files../_generated/server imports, not the app's generated files.app.use(...). If the app does not already have convex/convex.config.ts, create it.components.<name> using ctx.runQuery, ctx.runMutation, or ctx.runAction.npx convex dev and fix codegen, type, or boundary issues before finishing.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.
Unless the user explicitly wants an npm package, default to a local component:
convex/components/<componentName>/defineComponent(...) in its own convex.config.tsconvex/convex.config.ts with app.use(...)npx convex dev generate the component's own _generated/ filesA minimal local component with a table and two functions, plus the app wiring.
// convex/components/notifications/convex.config.ts
import { defineComponent } from "convex/server";
export default defineComponent("notifications");
// 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", ["userId"]),
});
// 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", (q) => q.eq("userId", args.userId))
.filter((q) => q.eq(q.field("read"), false))
.collect();
},
});
// 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;
// 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) => Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
mattpocock/skills
convex-create-component has been reliable in day-to-day use. Documentation quality is above average for community skills.
Solid pick for teams standardizing on skills: convex-create-component is focused, and the summary matches what you get after install.
I recommend convex-create-component for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Keeps context tight: convex-create-component is the kind of skill you can hand to a new teammate without a long onboarding doc.
Registry listing for convex-create-component matched our evaluation — installs cleanly and behaves as described in the markdown.
Useful defaults in convex-create-component — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
convex-create-component is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Registry listing for convex-create-component matched our evaluation — installs cleanly and behaves as described in the markdown.
convex-create-component fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
We added convex-create-component from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
showing 1-10 of 63