Persistent, stateful AI agents with thread management, tool integration, streaming, and RAG on Convex.
Works with
Thread management for multi-turn conversations with automatic persistence across restarts and real-time streaming responses to clients
Tool integration allowing agents to execute Convex functions as callable tools for knowledge search, task creation, and external API calls
Built-in vector search and RAG patterns for embedding documents and retrieving relevant context to augment agen
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionconvex-agentsExecute the skills CLI command in your project's root directory to begin installation:
Fetches convex-agents 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-agents. Access via /convex-agents 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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Build persistent, stateful AI agents with Convex including thread management, tool integration, streaming responses, RAG patterns, and workflow orchestration.
Before implementing, do not assume; fetch the latest documentation:
npm install @convex-dev/agent ai openai
// convex/agent.ts
import { Agent } from "@convex-dev/agent";
import { components } from "./_generated/api";
import { OpenAI } from "openai";
const openai = new OpenAI();
export const agent = new Agent(components.agent, {
chat: openai.chat,
textEmbedding: openai.embeddings,
});
// convex/threads.ts
import { mutation, query } from "./_generated/server";
import { v } from "convex/values";
import { agent } from "./agent";
// Create a new conversation thread
export const createThread = mutation({
args: {
userId: v.id("users"),
title: v.optional(v.string()),
},
returns: v.id("threads"),
handler: async (ctx, args) => {
const threadId = await agent.createThread(ctx, {
userId: args.userId,
metadata: {
title: args.title ?? "New Conversation",
createdAt: Date.now(),
},
});
return threadId;
},
});
// List user's threads
export const listThreads = query({
args: { userId: v.id("users") },
returns: v.array(v.object({
_id: v.id("threads"),
title: v.string(),
lastMessageAt: v.optional(v.number()),
})),
handler: async (ctx, args) => {
return await agent.listThreads(ctx, {
userId: args.userId,
});
},
});
// Get thread messages
export const getMessages = query({
args: { threadId: v.id("threads") },
returns: v.array(v.object({
role: v.string(),
content: v.string(),
createdAt: v.number(),
})),
handler: async (ctx, args) => {
return await agent.getMessages(ctx, {
threadId: args.threadId,
});
},
});
// convex/chat.ts
import { action } from "./_generated/server";
import { v } from "convex/values";
import { agent } from "./agent";
import { internal } from "./_generated/api";
export const sendMessage = action({
args: {
threadId: v.id("threads"),
message: v.string(),
},
returns: v.null(),
handler: async (ctx, args) => {
// Add user message to thread
await ctx.runMutation(internal.chat.addUserMessage, {
threadId: args.threadId,
content: args.message,
});
// Generate AI response with streaming
const response = await agent.chat(ctx, {
threadId: args.threadId,
messages: [{ role: "user", content: args.message }],
stream: true,
onToken: async (token) => {
// Stream tokens to client via mutation
await ctx.runMutation(internal.chat.appendToken, {
threadId: args.threadId,
token,
});
},
});
// Save complete response
await ctx.runMutation(internal.chat.saveResponse, 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-agents has been reliable in day-to-day use. Documentation quality is above average for community skills.
Solid pick for teams standardizing on skills: convex-agents is focused, and the summary matches what you get after install.
convex-agents has been reliable in day-to-day use. Documentation quality is above average for community skills.
convex-agents fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Useful defaults in convex-agents — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
convex-agents has been reliable in day-to-day use. Documentation quality is above average for community skills.
convex-agents fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
We added convex-agents from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
I recommend convex-agents for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
convex-agents fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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