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 magnus919/agent-skills --skill autogengit clone --depth 1 https://github.com/magnus919/agent-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/magnus919/agent-skills/autogen)<a href="https://agentmods.dev/skills/magnus919/agent-skills/autogen"><img src="https://agentmods.dev/badge/skills/magnus919/agent-skills/autogen/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/magnus919/agent-skills/autogen"><img src="https://agentmods.dev/badge/skills/magnus919/agent-skills/autogen.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00068 | $0.01396 |
| Opus 5 | $0.00034 | $0.00698 |
| Sonnet 5 | $0.00014 | $0.00279 |
| Haiku 4.5 | $0.00007 | $0.00140 |
Grade A, and why
autogen 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.
How it starts
The opening of the file, as written. The whole thing — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AutoGen Expert Skill
AutoGen (by Microsoft Research) is a framework for conversational multi-agent AI. Unlike LangGraph's explicit graph topology or CrewAI's role-based crews, AutoGen uses agent-to-agent conversations as the orchestration primitive. Agents communicate through structured chat, with built-in patterns for nested conversations, group chat with routing, and code execution.
Core Paradigm
from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.ui import Console
from autogen_ext.models.openai import OpenAIChatCompletionClient
model_client = OpenAIChatCompletionClient(model="gpt-4o-mini")
assistant = AssistantAgent(
name="assistant",
system_message="You are a helpful assistant.",
model_client=model_client,
)
⚠️ UserProxyAgent is NOT a human user. It is an automated proxy that can execute code. Despite the name, it runs autonomously unless
human_input_modeis set toALWAYS.
Core Principles
- Conversations are the orchestration primitive. Agents send messages, receive replies, and the conversation structure determines the workflow.
- UserProxyAgent is a code executor, not a human. Despite the name, it runs autonomously by default. Set
human_input_mode="ALWAYS"for actual human-in-the-loop. - GroupChat routes between agents. RoundRobinGroupChat cycles fixed-order. SelectorGroupChat uses an LLM to pick the next speaker.
- Nested chats delegate work. An agent can spawn a sub-conversation between specialist agents and return the result.
- Docker is the safe code execution mode. Local code execution (
LocalCommandLineCodeExecutor) runs LLM-generated code on your machine — use Docker in production. - Cancellation tokens stop runaway agents. Always pass
CancellationTokenfor long-running tasks.
Where to Start
| You already have... | Start here |
|---|---|
| Nothing — exploring AutoGen | Create a two-agent chat (Assistant + UserProxy) |
| Agents that need to coordinate | Build a GroupChat with multiple agents |
| Agents that need code execution | Configure Docker code executor |
| A complex multi-step task | Use nested chats for sub-tasks |
What ships with it
14 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.
- evals/evals.json 2.7 KB
- README.md 1.8 KB
- references/agent-types.md 1.3 KB
- references/code-execution.md 1.1 KB
- references/conversation-patterns.md 1.5 KB
- references/faq-and-troubleshooting.md 1.3 KB
- references/group-chat.md 1.2 KB
- references/tool-integration.md 968 B
- references/v04-migration.md 2.8 KB
- references/validation-audit.md 1.7 KB
- scripts/check-setup.py 708 B runs code
- templates/code-execution.py 1011 B runs code
- templates/group-chat.py 1.0 KB runs code
- templates/two-agent-chat.py 678 B runs code
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.
- 8d ago Changed · +1 lines · +14 tokens per session 418fe3769d7f
- 12d ago First seen · 114 lines · 54 tokens per session scan A 71f6bb562508
autogen is a skill published in the GitHub repository magnus919/agent-skills (76 stars, last pushed yesterday), licensed MIT. It adds 68 tokens to every session and 1,396 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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