ferix: Skill for Claude Code

.claude/skills/Convex Agents/SKILL.md

Convex Agents is a skill for Claude Code from charlietlamb/ferix. It costs 31 tokens per session (3,292 once invoked), scanned A, a copy of convex-agents, MIT.

A guide to building AI agents with Convex, a backend platform that stores data and runs server-side functions. It covers saved conversation threads, tool calls, streamed replies, document search, and long-running workflows.

In plain words
What is it for?
Use it to create Convex-based chat agents, connect them to OpenAI, store conversation history, stream responses to users, call Convex functions as tools, search a knowledge base, and coordinate durable tasks.
Why use it?
It provides patterns for keeping agent conversations and tasks working across restarts instead of treating each request as separate. It also shows how an agent can use application functions and retrieved documents.

Skill for Claude Code

Written for Claude Code: installed under .claude/.

This is charlietlamb/ferix's own configuration. It tells Claude Code how to work on ferix itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything ferix configures →

Needs its repository: it reads a path above its own folder, which exists only inside the repository. The line is import { api } from "../convex/_generated/api";.

Reuse

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.

Copy the file
curl -O https://raw.githubusercontent.com/charlietlamb/ferix/main/.claude/skills/Convex Agents/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/charlietlamb/ferix

Made for: Claude Code.

Wrote 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.

agentmods badge for Convex Agents

README.md
[![agentmods](https://agentmods.dev/badge/skills/charlietlamb/ferix/convex-agents/github.svg)](https://agentmods.dev/skills/charlietlamb/ferix/convex-agents)
Your own site
<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.

agentmods 80×15 button for Convex Agents

Your own site · 80×15
<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>
Per session 31 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,292 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 94% copy Near-identical to another mod in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 9d ago against content hash 97a720e0d358, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

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.

Origin

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.

.claude/skills/Convex Agents/SKILL.md · 516 lines

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:

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,
    });
  },
});

Read the full file on GitHub · 516 lines

Changes

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.

  1. 9d ago First seen · 516 lines · 31 tokens per session scan A 97a720e0d358

Subscribe to this mod's changes

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.

Related

Other skills, from other repositories

convex-agents

Building AI agents with the Convex Agent component including thread management, tool integration, streaming responses, RAG patterns, and workflow orchestration.

waynesutton/convexskills · 32 tokens

convex-agents

Building AI agents with the Convex Agent component including thread management, tool integration, streaming responses, RAG patterns, and workflow orchestration.

J-StaR-Films-Studios/VibeCode-Protocol-Suite · 32 tokens

developing-genkit-tooling

Best practices for authoring Genkit tooling, including CLI commands and MCP server tools. Covers naming conventions, architectural patterns, and consistency guidelines.

genkit-ai/genkit · 35 tokens

stripe-apps

Use when building, modifying, or reviewing a Stripe App — or when the user describes something that implies one (e.g. "add a panel to the customer page", "customize my Stripe Dashboard", "react to Stripe events from my app", "connect my service to Stripe without sharing API keys"). Covers the full app development…

stripe/ai · 197 tokens

stripe-projects

Use when the user wants to provision infrastructure or third-party services using Stripe Projects. Triggers: "I need a database", "set up auth", "add caching", "give me a Postgres", "provision Redis", "I need hosting", "add a vector DB", "get me an API key for X", "get credentials for X", "sign up for a service", "set…

stripe/ai · 213 tokens

stripe-docs

Use when the user or agent needs to read, search, or look up Stripe documentation or API reference. Prefer this over curl or WebFetch for any docs.stripe.com content. Use to fetch gated documentation.

stripe/ai · 46 tokens