Skill UI

Manages and coordinates complex workflows involving multiple AI agents and tasks. It efficiently distributes work across multiple workers, handles runtime errors, and intelligently merges disparate results into a unified output, making it ideal for large-scale, distributed AI computations.

Tycana keeps task context across Claude conversations, capturing new work items, planning the day, tracking patterns, and offering energy-aware recommendations that deepen with each sync through MCP.

A comprehensive tool for security professionals to enumerate and analyze malware persistence mechanisms across critical Windows Auto-Start Extensibility Points (ASEPs). It systematically scans registry run keys, scheduled tasks, services, drivers, and startup locations. Ideal for incident response, threat hunting, and validating the complete removal of malicious autostart entries.

A comprehensive guide to detecting sophisticated rootkit activity and persistent threats that evade standard security tools (like Task Manager or EDR). Techniques include cross-view process analysis, identifying System Service Descriptor Table (SSDT) hooks, kernel module analysis, and network connection forensics using tools like Volatility. Use this when standard monitoring fails, and deep system integrity checks are required.

This comprehensive guide outlines a structured methodology for threat hunting and incident response, focusing on identifying adversary persistence mechanisms across Windows endpoints. It covers critical areas such as Registry Run/RunOnce keys, services, scheduled tasks, WMI event subscriptions, and COM hijacking. Use this workflow to proactively detect unauthorized backdoors, strengthen detection rules, and assess overall security posture against MITRE ATT&CK techniques (T1547).

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.

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.

Automates the complex pre-development workflow for large-scale system transformations, such as major rewrites, migrations, or overhauls. This skill performs comprehensive project analysis, task decomposition, planning document generation, and sets up detailed progress tracking across multiple phases, ensuring a structured approach before any coding begins. It supports adaptive control and integrates robust task tracking via GitHub or local files.

This skill manages comprehensive software development life cycle operations—including issue triage, PR reviews, version releases, bug fixing, and marketing content generation. It operates using a parallel "agent army" of dynamic agents (10-20 per task), requiring the user to adopt an Engineering Manager (EM) mindset. Key principles involve rigorous citation checking (anti-hallucination) and applying multiple roles (Product Owner, Open Source Contributor, Architect) simultaneously to ensure robust, safe, and user-centric outcomes across all platforms.

A cross-protocol platform for discovering and searching 18,000+ MCP servers and AI agents across six or more registries. It allows users to find the necessary endpoint for specific tasks (like database access or file conversion) by searching across MCP, A2A, and agents.txt protocols simultaneously.

CLI for Google Workflow to orchestrate cross-service productivity workflows, inspect resources and methods, and run helper commands like standup reports, meeting prep, email-to-task, weekly digests, and file announcements.

OpenViking Memory enables AI agents to recall and persist long-term semantic knowledge using MCP tools. It supports retrieving prior context at task start via find/search/read and storing durable facts, preferences, and lessons via remember. Ideal for coding, debugging, and multi-step workflows, ensuring continuity across sessions without automatic lifecycle hooks.