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 agentskillexchange/skills --skill autogen-microsoft-multi-agent-frameworkgit clone --depth 1 https://github.com/agentskillexchange/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/agentskillexchange/skills/autogen-microsoft-multi-agent-framework)<a href="https://agentmods.dev/skills/agentskillexchange/skills/autogen-microsoft-multi-agent-framework"><img src="https://agentmods.dev/badge/skills/agentskillexchange/skills/autogen-microsoft-multi-agent-framework/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/agentskillexchange/skills/autogen-microsoft-multi-agent-framework"><img src="https://agentmods.dev/badge/skills/agentskillexchange/skills/autogen-microsoft-multi-agent-framework.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 | $0.00046 | $0.00506 |
| Opus 5.5 | $0.00018 | $0.00202 |
| Sonnet 5.5 | $0.00009 | $0.00101 |
| Haiku 4.5 | $0.00005 | $0.00051 |
Grade A, and why
AutoGen — Microsoft Multi-Agent Conversation Framework 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 1mo 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.
What it actually says
AutoGen — Microsoft Multi-Agent Conversation Framework
AutoGen is Microsoft's open-source framework for building multi-agent systems where AI agents converse with each other and humans to solve tasks, with support for tool use and human-in-the-loop workflows.
Prerequisites
Python 3.10+, pip, OpenAI API key or compatible LLM
Installation
Use the upstream install or setup path that matches your environment:
- pip install -U "autogen-agentchat" "autogen-ext[openai]"
- pip install -U "autogenstudio"
-
First run npm install -g @playwright/mcp@latest to install the MCP server.
Requirements and caveats from upstream:
- AutoGen requires Python 3.10 or later.
- python
- Core API implements message passing, event-driven agents, and local and distributed runtime for flexibility and power. It also support cross-language support for .NET and Python.
Basic usage or getting-started notes:
-
bash
-
The current stable version can be found in the releases. If you are upgrading from AutoGen v0.2, please refer to the [Migration Guide](https://microsoft.github.io/autog...
-
The following samples call OpenAI API, so you first need to create an account and export your key as export OPENAI_API_KEY="sk-...".
-
Extracted from upstream docs: https://raw.githubusercontent.com/microsoft/autogen/HEAD/README.md
Documentation
Source
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.
- 1mo ago First seen · 51 lines · 46 tokens per session scan A f1e23f01551c
AutoGen — Microsoft Multi-Agent Conversation Framework is a skill published in the GitHub repository agentskillexchange/skills (55 stars, last pushed today), licensed MIT. It adds 46 tokens to every session and 506 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-09-03.
Other skills, from other repositories
oma-scholar
Search academic literature and generate, validate, or compare Knows paper sidecars. Use for claim/evidence analysis and literature synthesis.
data-charts-tako
Search and visualize the world's data - get charts, insights, and embeddable knowledge cards for finance, economics, demographics, sports, and more.
pkf-writing
Author, update, convert, validate, persist, and render PKF (.pkf) content through message-inline, Library Asset, or Memory Page carriers. Use for structured reports, knowledge pages, delivery cards, or any .pkf body. NOT for recall, memory-graph placement, or curation decisions — those belong to the memory skill.
monorepo-management
Master monorepo management with Turborepo, Nx, and pnpm workspaces to build efficient, scalable multi-package repositories with optimized builds and dependency management. Use when setting up monorepos, optimizing builds, or managing shared dependencies.
render-airdrop-carousel
Assemble a viral iOS "AirDrop" notification-carousel video ad (≈6–8s, 9:16) from a brand line plus 6–16 real product photos — a native AirDrop share-sheet card ("Brand would like to share a · Decline / Accept") springs up and its preview window CYCLES through the products, landing on a range/lineup payoff with an…
browse-and-evaluate
Use when exploring the ai-agent-skills catalog to find, compare, and evaluate skills before installing. Always use --fields to limit output size and --dry-run before committing to an install.