Skill UI

This skill implements the BDI (Belief-Desire-Intention) architecture for advanced cognitive agent modeling. It allows agents to reason about context by transforming external knowledge (like RDF triples) into internal mental states (beliefs, desires, intentions) and vice versa. Key features include goal-directed planning, explainable reasoning chains, and T2B2T (Triples-to-Beliefs-to-Triples) flow, making it ideal for multi-agent systems and augmenting LLMs with formal cognitive structures.

This skill orchestrates a structured six-phase board meeting protocol using multiple AI agents to simulate C-suite executives. It prevents groupthink by ensuring independent contributions before synthesis, covering context loading, critic analysis, and founder review. Ideal for strategic decision-making, market expansion, product direction, and organizational planning. It extracts clean, actionable decisions and maintains a two-layer decision memory system for long-term context.

Provides a virtual boardroom for founders with CLI-driven C-suite agents, structured multi-role meetings and routing to deliver strategic recommendations, context capture, and decision logging for high-stakes choices.

This advanced orchestration layer acts as the central point between founders and C-suite advisors. It analyzes complex strategic questions, scores decision complexity, and automatically routes queries to the necessary expert roles (e.g., CFO, CTO). It coordinates multi-agent 'board meetings' and synthesizes conflicting expert opinions into structured, actionable decision logs for major business decisions.

This infrastructure provides a robust solution for running powerful AI agents in remote, sandboxed environments. It overcomes the limitations of local execution by ensuring unlimited concurrency, consistent environments, and collaborative features. Use this to build complex background coding agents, multi-client interfaces (e.g., Slack, Web), or scale agent workloads far beyond local machine constraints.

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.

A comprehensive, multi-phase security audit tool designed to evaluate AI agent skills before deployment. It detects critical vulnerabilities, including prompt injection, malicious code, excessive permissions, secret exposure, and supply chain risks, ensuring the integrity and safety of your agent environment.

Coordinates discovery, matching, and workflow orchestration across agent ecosystems, automatically scanning SKILL.md, ranking relevant skills, and executing multi-step plans for complex requests.

This agent ensures seamless continuity across multiple work sessions. It automatically captures, compresses, and stores critical project information—including summarized discussions, architectural decisions, pending tasks, modified files, and technical discoveries. When a new session starts, it loads a comprehensive briefing, allowing users to quickly resume work without losing track of the project's current state.

A framework for building production-ready, low-latency, multimodal AI agents. It allows developers to orchestrate complex pipelines that seamlessly handle real-time audio, video, and text data. Key capabilities include integrating multiple AI services (LLMs, STT, TTS), managing conversation context, and enabling tool use and function calling for advanced, interactive AI applications.

This utility converts a single, high-level objective into a comprehensive, step-by-step build plan suitable for complex, multi-agent, or multi-session engineering projects. It automatically handles dependency mapping, detects parallel steps, and structures each step with self-contained context briefs, ensuring any new agent can execute it without prior knowledge. Ideal for large-scale refactoring or feature development spanning multiple PRs.

This blueprint guides the creation of F.R.I.D.A.Y., a tactical, low-latency voice assistant inspired by Iron Man. It utilizes the specialized Pipecat framework to orchestrate a comprehensive, real-time audio pipeline: STT (OpenAI Whisper/GPT-4o) -> LLM (Gemini) -> TTS (OpenAI Nova). Ideal for developers needing to build advanced, multi-provider, conversational voice applications.