Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/J-StaR-Films-Studios/VibeCode-Protocol-Suitenpx agentmods add skills/j-star-films-studios/vibecode-protocol-suite/convex-agentsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/j-star-films-studios/vibecode-protocol-suite/convex-agents)<a href="https://agentmods.dev/skills/j-star-films-studios/vibecode-protocol-suite/convex-agents"><img src="https://agentmods.dev/badge/skills/j-star-films-studios/vibecode-protocol-suite/convex-agents/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/j-star-films-studios/vibecode-protocol-suite/convex-agents"><img src="https://agentmods.dev/badge/skills/j-star-films-studios/vibecode-protocol-suite/convex-agents.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00032 | $0.03299 |
| Opus 5 | $0.00016 | $0.01649 |
| Sonnet 5 | $0.00006 | $0.00660 |
| Haiku 4.5 | $0.00003 | $0.00330 |
Grade A, and why
convex-agents scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to convex-agents — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 517 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Convex Agents
Build persistent, stateful AI agents with Convex including thread management, tool integration, streaming responses, RAG patterns, and workflow orchestration.
Documentation Sources
Before implementing, do not assume; fetch the latest documentation:
- Primary: https://docs.convex.dev/ai
- Convex Agent Component: https://www.npmjs.com/package/@convex-dev/agent
- For broader context: https://docs.convex.dev/llms.txt
Instructions
Why Convex for AI Agents
- Persistent State - Conversation history survives restarts
- Real-time Updates - Stream responses to clients automatically
- Tool Execution - Run Convex functions as agent tools
- Durable Workflows - Long-running agent tasks with reliability
- Built-in RAG - Vector search for knowledge retrieval
Setting Up Convex Agent
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,
});
Thread Management
// 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,
});
},
});
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 517 lines · 32 tokens per session scan A d23976c0dbe8
convex-agents is a skill published in the GitHub repository J-StaR-Films-Studios/VibeCode-Protocol-Suite (24 stars, last pushed 5d ago), licensed ISC. It adds 32 tokens to every session and 3,299 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to convex-agents, differing in 0 lines, and is treated as a copy.
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