autogen-setup
Microsoft AutoGen multi-agent configuration for conversational AI systems
Best use case
autogen-setup is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Microsoft AutoGen multi-agent configuration for conversational AI systems
Teams using autogen-setup should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/autogen-setup/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How autogen-setup Compares
| Feature / Agent | autogen-setup | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Microsoft AutoGen multi-agent configuration for conversational AI systems
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# AutoGen Setup Skill ## Capabilities - Configure AutoGen agents (AssistantAgent, UserProxyAgent) - Set up agent conversations and group chats - Implement code execution capabilities - Design human-in-the-loop patterns - Configure nested agent architectures - Implement custom reply functions ## Target Processes - multi-agent-system - autonomous-task-planning ## Implementation Details ### Agent Types 1. **AssistantAgent**: LLM-powered assistant 2. **UserProxyAgent**: Human proxy with code execution 3. **GroupChatManager**: Multi-agent orchestration 4. **ConversableAgent**: Base class for custom agents ### Configuration Options - LLM configuration (models, temperatures) - Code execution settings - Human input mode - Max consecutive auto-replies - Function calling configuration ### Patterns - Two-agent conversations - Group chats with selection - Nested conversations - Teachable agents ### Best Practices - Proper termination conditions - Safe code execution sandboxing - Clear agent system messages - Monitor conversation flow ### Dependencies - pyautogen
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