autogen-setup
Microsoft AutoGen multi-agent configuration for conversational AI systems
npx skills add majiayu000/claude-skill-registry --skill autogen-setup --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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
- Capabilities
- Target Processes
- Implementation Details
- Agent Types
- Configuration Options
- Patterns
- Best Practices
- Dependencies
What does the autogen-setup skill do?
Microsoft AutoGen multi-agent configuration for conversational AI systems
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill autogen-setup --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.