Self-contained Convex packages with isolated databases, functions, and type-safe exports for reuse across projects.
Works with
Includes isolated database tables, queries, mutations, actions, and TypeScript types within a single component package
Supports multiple component instances in a single app with custom naming and configuration options
Provides optional React hooks for type-safe frontend integration and simplified consumer APIs
Publishes to npm with proper peer dependencies, TypeScrip
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionconvex-component-authoringExecute the skills CLI command in your project's root directory to begin installation:
Fetches convex-component-authoring from waynesutton/convexskills 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-component-authoring. Access via /convex-component-authoring 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
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Create self-contained, reusable Convex components with proper isolation, exports, and dependency management for sharing across projects.
Before implementing, do not assume; fetch the latest documentation:
Convex components are self-contained packages that include:
my-convex-component/
├── package.json
├── tsconfig.json
├── README.md
├── src/
│ ├── index.ts # Main exports
│ ├── component.ts # Component definition
│ ├── schema.ts # Component schema
│ └── functions/
│ ├── queries.ts
│ ├── mutations.ts
│ └── actions.ts
└── convex.config.ts # Component configuration
// convex.config.ts
import { defineComponent } from "convex/server";
export default defineComponent("myComponent");
// src/schema.ts
import { defineSchema, defineTable } from "convex/server";
import { v } from "convex/values";
export default defineSchema({
// Tables are isolated to this component
items: defineTable({
name: v.string(),
data: v.any(),
createdAt: v.number(),
}).index("by_name", ["name"]),
config: defineTable({
key: v.string(),
value: v.any(),
}).index("by_key", ["key"]),
});
// src/component.ts
import { defineComponent, ComponentDefinition } from "convex/server";
import schema from "./schema";
import * as queries from "./functions/queries";
import * as mutations from "./functions/mutations";
const component = defineComponent("myComponent", {
schema,
functions: {
...queries,
...mutations,
},
});
export default component;
// src/functions/queries.ts
import { query } from "../_generated/server";
import { v } from "convex/values";
export const list = query({
args: {
limit: v.optional(v.number()),
},
returns: v.array(v.object({
_id: v.id("items"),
name: v.string(),
data: v.any(),
createdAt: v.number(),
})),
handler: async (ctx, args) => {
return await ctx.db
.query("items")
.order("desc")
.take(args.limit ?? 10);
},
});
export const get = query({
args: { name: v.string() },
returns: v.union(v.object({
_id: v.id("items"),
name: v.string(),
data: v.any(),
}), v.null()),
handler: async (ctx, args) => {
return await ctx.db
.query("items")
.withIndex("by_name", (q) => q.eq("name", args.name))
.unique();
},
});
// src/functions/mutations.ts
import { mutation } from "../_generated/server";
import { v } from "convex/values";
export const create = mutation({
args: {
name: v.string(),
data: v.any(),
},
returns: v.id("items"),
handler: async (ctx, args) => {
return await ctx.db.insert("items", {
name: args.name,
data: args.data,
createdAt: Date.now(),
});
},
});
export const update = mutation({
args: {
id: v.id("items"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
I recommend convex-component-authoring for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Useful defaults in convex-component-authoring — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
convex-component-authoring has been reliable in day-to-day use. Documentation quality is above average for community skills.
convex-component-authoring fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
convex-component-authoring is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Solid pick for teams standardizing on skills: convex-component-authoring is focused, and the summary matches what you get after install.
convex-component-authoring reduced setup friction for our internal harness; good balance of opinion and flexibility.
I recommend convex-component-authoring for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
convex-component-authoring has been reliable in day-to-day use. Documentation quality is above average for community skills.
convex-component-authoring reduced setup friction for our internal harness; good balance of opinion and flexibility.
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