Borrowing it
Nothing to install: this file belongs to charlietlamb/ferix. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/charlietlamb/ferix/main/.claude/skills/Convex Agents/SKILL.mdgit clone --depth 1 https://github.com/charlietlamb/ferixWrote 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/charlietlamb/ferix/convex-agents)<a href="https://agentmods.dev/skills/charlietlamb/ferix/convex-agents"><img src="https://agentmods.dev/badge/skills/charlietlamb/ferix/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/charlietlamb/ferix/convex-agents"><img src="https://agentmods.dev/badge/skills/charlietlamb/ferix/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 | $0.00031 | $0.03292 |
| Opus 5 | $0.00015 | $0.01646 |
| Sonnet 5 | $0.00006 | $0.00658 |
| Haiku 4.5 | $0.00003 | $0.00329 |
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 9d 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
94% identical to convex-agents — 3 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 — 516 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 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.
- 9d ago First seen · 516 lines · 31 tokens per session scan A 97a720e0d358
Convex Agents is a skill published in the GitHub repository charlietlamb/ferix (10 stars, last pushed 6mo ago), licensed MIT. It adds 31 tokens to every session and 3,292 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 94% identical to convex-agents, differing in 3 lines, and is treated as a copy.
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