Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add vadimcomanescu/codex-skills --skill agents-autogptgit clone --depth 1 https://github.com/vadimcomanescu/codex-skillsWrote 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/vadimcomanescu/codex-skills/agents-autogpt)<a href="https://agentmods.dev/skills/vadimcomanescu/codex-skills/agents-autogpt"><img src="https://agentmods.dev/badge/skills/vadimcomanescu/codex-skills/agents-autogpt/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/vadimcomanescu/codex-skills/agents-autogpt"><img src="https://agentmods.dev/badge/skills/vadimcomanescu/codex-skills/agents-autogpt.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 | $0.00034 | $0.02177 |
| Opus 5 | $0.00017 | $0.01089 |
| Sonnet 5 | $0.00007 | $0.00435 |
| Haiku 4.5 | $0.00003 | $0.00218 |
Grade A, and why
agents-autogpt 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
98% identical to autogpt-agents — 9 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 — 399 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AutoGPT - Autonomous AI Agent Platform
Comprehensive platform for building, deploying, and managing continuous AI agents through a visual interface or development toolkit.
When to use AutoGPT
Use AutoGPT when:
- Building autonomous agents that run continuously
- Creating visual workflow-based AI agents
- Deploying agents with external triggers (webhooks, schedules)
- Building complex multi-step automation pipelines
- Need a no-code/low-code agent builder
Key features:
- Visual Agent Builder: Drag-and-drop node-based workflow editor
- Continuous Execution: Agents run persistently with triggers
- Marketplace: Pre-built agents and blocks to share/reuse
- Block System: Modular components for LLM, tools, integrations
- Forge Toolkit: Developer tools for custom agent creation
- Benchmark System: Standardized agent performance testing
Use alternatives instead:
- LangChain/LlamaIndex: If you need more control over agent logic
- CrewAI: For role-based multi-agent collaboration
- OpenAI Assistants: For simple hosted agent deployments
- Semantic Kernel: For Microsoft ecosystem integration
Quick start
Installation (Docker)
# Clone repository
git clone https://github.com/Significant-Gravitas/AutoGPT.git
cd AutoGPT/autogpt_platform
# Copy environment file
cp .env.example .env
# Start backend services
docker compose up -d --build
# Start frontend (in separate terminal)
cd frontend
cp .env.example .env
npm install
npm run dev
Access the platform
- Frontend UI: http://localhost:3000
- Backend API: http://localhost:8006/api
- WebSocket: ws://localhost:8001/ws
Architecture overview
AutoGPT has two main systems:
AutoGPT Platform (Production)
- Visual agent builder with React frontend
- FastAPI backend with execution engine
- PostgreSQL + Redis + RabbitMQ infrastructure
AutoGPT Classic (Development)
- Forge: Agent development toolkit
- Benchmark: Performance testing framework
- CLI: Command-line interface for development
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 · 399 lines · 34 tokens per session scan A de35e898693c
agents-autogpt is a skill published in the GitHub repository vadimcomanescu/codex-skills (24 stars, last pushed 7mo ago), licensed MIT. It adds 34 tokens to every session and 2,177 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 98% identical to autogpt-agents, differing in 9 lines, and is treated as a copy.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…