Skill UI

This tool initializes a dedicated AgentHub session for multi-agent collaborations. It requires defining the core task, specifying the number of parallel agents, and configuring detailed evaluation criteria (including the evaluation command, target metric, and measurement direction). Upon execution, it generates a unique session ID and prepares the entire environment for subsequent agent spawning and competitive evaluation, optionally capturing a performance baseline.

This skill manages the Ouroboros AI framework configuration. It allows users to open the settings GUI via browser or TUI, or configure settings conversationally. Users can edit runtime choices, model selections, and LLM backends. It supports local and remote server modes, enabling customization of agents and execution profiles through ouroboros config commands.

Orchestrator skill for delegating a bounded coding task to the Command Code CLI implementer, then reviewing its diff and landing the commit yourself. Covers brief writing, dispatch, prerequisites and safety limits.

The /goal command converts standard agent prompts into persistent, self-correcting loops (plan → act → test → review → iterate). It is designed for long-running, complex engineering tasks like migrations, coverage lifts, or refactoring, ensuring progress is tracked against a verifiable stop condition and mandated documentation.

Reduces token costs by scoping context into directory-level CLAUDE.md files. Sets up hierarchical memory, tracks savings via dashboard, routes agents to sub-contexts, and provides audit and insights commands.

The mmx-cli tool enables users to interact with the MiniMax AI platform directly from the terminal. It supports generating text, images, videos, speech, and music, as well as performing web searches and managing API resources. Ideal for developers and creators needing media content generation or AI model interaction via command line interfaces.

Plan and create a bounded RuFlo AI team from a reviewed goal, including roles, tasks, budget, and risks, without external side effects or arbitrary commands.

This skill enables splitting extracted text into optimized chunks for LLM context windows or RAG ingestion pipelines. It supports configurable chunk sizes, overlap settings, and various chunker types including text, markdown, YAML, and semantic splitting. Users can utilize token-based sizing for specific models or standalone commands for pre-existing text. Ideal for preparing documents for vector stores or summarization tasks.