flins: Skill for Claude Code

.agents/skills/Convex Agents/SKILL.md

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

A Convex component for building AI agents with stored conversations, tool use, streamed replies, knowledge retrieval, and long-running workflows. Convex is a backend platform that provides database and server functions for applications.

In plain words
What is it for?
Use it to build agents with conversation threads, Convex function tools, streaming responses, vector-search-based retrieval, and durable multi-step workflows.
Why use it?
It supplies the application pieces needed to keep agent conversations and tasks running across requests or restarts instead of managing them all yourself.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: installed under .agents/ (shared by several agents).

This is powroom/flins's own configuration. It tells Claude Code and Codex how to work on flins 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 flins 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 powroom/flins. 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/powroom/flins/main/.agents/skills/Convex Agents/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/powroom/flins

Made for: Claude Code, Codex.

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/powroom/flins/convex-agents/github.svg)](https://agentmods.dev/skills/powroom/flins/convex-agents)
Your own site
<a href="https://agentmods.dev/skills/powroom/flins/convex-agents"><img src="https://agentmods.dev/badge/skills/powroom/flins/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/powroom/flins/convex-agents"><img src="https://agentmods.dev/badge/skills/powroom/flins/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 8d ago against content hash 97a720e0d358, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, 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 8d 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.

.agents/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. 8d 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 powroom/flins (39 stars, last pushed 5mo 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.