Skill UI

Use this command to set up a structured multi-agent collaboration environment within AgentHub. It allows you to define the core task, specify the required number of agents, and configure detailed evaluation criteria (metrics, directions, and commands) for both quantitative benchmarking and qualitative LLM judging. This is the starting point for any agent competition or complex AI workflow.

This command executes the entire AgentHub lifecycle in a single, chained invocation. It automates complex development tasks by sequentially handling initialization, capturing performance baselines, spawning multiple specialized agents, evaluating the results (via metrics or LLM judging), and finally presenting a winner for merging. Use this when running an end-to-end competition or complex refactoring task.

This command merges the results of the highest-ranked agent from a specified or latest session into the base branch. It automates the finalization of a development cycle by ensuring a clean and traceable history. Functionality includes merging the winner's branch, archiving all losing agents' commits using Git tags, and cleaning up temporary worktrees.

Monitor the real-time progress and overall state of an AgentHub session. This tool displays detailed information including agent branch activity, commit counts, frontier status, and recent board updates, allowing users to effectively track complex multi-agent workflow execution.