Skill UI

This comprehensive skill guides the deployment and configuration of Wazuh SIEM/XDR for robust endpoint monitoring. Users will learn to manage agents via the API, create custom decoders and rules (XML), query security alerts, and test rule logic, enabling organizations to establish strong security controls and automate incident response.

Learn how to implement Runtime Application Self-Protection (RASP) agents for Java and Python web applications. This skill covers deploying OpenRASP, detecting OWASP Top 10 attacks (SQLi, XSS, RCE), configuring security policies, and integrating real-time security alerts with SIEM platforms for comprehensive defense in depth.

This comprehensive guide details the deployment of HashiCorp Vault for robust, centralized secrets management in hybrid and multi-cloud environments. It covers configuring dynamic secret engines for databases (PostgreSQL) and cloud providers (AWS), integrating diverse authentication methods (OIDC, AppRole, Kubernetes), and securely delivering short-lived secrets to Kubernetes workloads via agents. The primary goal is to enforce zero-trust principles by eliminating hardcoded, long-lived credentials from application code and CI/CD pipelines.

This skill covers performing comprehensive vulnerability assessments across diverse environments, including on-premises infrastructure, cloud VMs, and container registries. It leverages standard network protocols like SSH (Linux) and WMI (Windows), coupled with advanced cloud provider APIs for analyzing disk snapshots without requiring local endpoint agents. Ideal for security auditing and compliance checks.

Automates complex red team operations using the Covenant C2 framework. This guide covers the entire lifecycle of authorized adversary simulation, including configuring HTTP/HTTPS listeners, deploying agents (grunts), executing tasks remotely, and tracking lateral movement. It is essential for performing comprehensive penetration testing and security auditing.

An interactive tool designed to discover, select, and assemble specialized teams of AI agents. It supports structured agent collections across different domains (e.g., Engineering, Marketing). Users select multiple roles for a specific task, and the skill launches them in parallel, synthesizing their independent findings into a unified report, including consensus and conflict analysis.

AgentHub spawns parallel git worktree agents that compete on tasks, evaluates their outputs via metrics or LLM judges, and merges the top branch for optimizations, research, or content variations.

This module provides a centralized interface for the AgentHub message board. It allows multiple AI agents and the coordinator to communicate, track tasks, and log results through structured markdown posts across dedicated channels (Dispatch, Progress, Results). It is essential for monitoring complex multi-agent workflows and ensuring transparent collaboration and auditing.

Automatically reverse-engineers any codebase (frontend, backend, or fullstack) into a comprehensive Product Requirements Document (PRD). It analyzes complex elements like routes, components, API integrations, state management, and business logic across major stacks (React, Vue, Next.js, Django, FastAPI). The resulting PRD is detailed enough for both product managers (understanding 'what') and engineers/AI agents (understanding 'how' to reconstruct the system).

This tool allows users to systematically evaluate and rank the outputs of multiple agents within an AgentHub session. It supports metric-based evaluation (running custom commands for measurable metrics like speed or resource usage), or an advanced LLM Judge Mode. The LLM Judge compares results based on criteria like correctness, simplicity, and overall quality, helping users determine the best-performing agent for the 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.

The /hub:spawn command launches N subagents that work on the same task simultaneously. Each agent operates within an isolated git worktree, ensuring true parallelism and resource separation. Users can optionally specify templates (e.g., `optimizer`, `refactorer`) to guide agents toward specific strategies for improving code quality, performance, or test coverage. Ideal for complex software engineering tasks requiring diverse, competing approaches.