Skill UI

Pacu is an open-source, modular AWS exploitation framework designed for authorized cloud penetration testing and red-teaming. It functions as a cloud-pentest analogue to Metasploit, enabling users to enumerate AWS resources, scan for privilege escalation paths, and automate the exploitation of IAM vulnerabilities, persistence mechanisms, and data exfiltration. Use this skill to assess the blast radius of compromised credentials in a controlled, authorized environment.

This PowerShell toolset performs advanced post-exploitation, reconnaissance, persistence, and data exfiltration against Microsoft 365/Entra ID tenants using the Microsoft Graph API. It operationalizes stolen Graph tokens to enumerate users, dump policies, escalate privileges, and search sensitive data across mailboxes, SharePoint, and Teams. Essential for authorized red teaming engagements.

This skill details the advanced red-teaming technique of exploiting Active Directory Certificate Services (AD CS) via the ESC8 path. By abusing the HTTP web-enrollment endpoint and relaying coerced NTLM credentials, an attacker can compel a domain controller to request a machine certificate. This certificate is then used to compromise the domain via PKINIT and DCSync.

This guide provides a structured approach to red-teaming Large Language Models (LLMs) to detect sensitive data leakage from system prompts. It simulates advanced attacks—including prompt injection, instruction override, and encoding tricks—to identify embedded secrets, API keys, and proprietary business logic. Essential for validating adherence to OWASP LLM07 and securing AI applications against data exfiltration.

A comprehensive red-teaming skill for assessing the security posture of Retrieval-Augmented Generation (RAG) pipelines. It systematically probes two critical injection surfaces: poisoned retrieved context (indirect prompt injection) and embedding manipulation. Use this skill to validate retrieval guardrails, ensure data leak prevention, and demonstrate vulnerabilities in LLM-powered knowledge assistants.

Workthreads analyzes a team's coding history across multiple repositories to generate a structured weekly status report. It clusters raw logs into 'work threads' and automatically tags their lifecycle status (new, open loops, closed, reverted). This is essential for leads needing a high-level, actionable summary of project progress, outstanding tasks, and completed features without reading thousands of lines of raw activity.

This skill provides a comprehensive tech debt assessment, identifying, classifying, and prioritizing core maintainability issues. By drawing on classic engineering principles, it helps teams build a structured refactoring roadmap, optimizing code health and development velocity.

A meta-skill designed to capture hard-won, non-obvious knowledge—such as complex debugging steps, multi-step operational workflows (e.g., deployment, migrations), or project-specific facts—and convert them into reusable Agent Skills. It ensures that future sessions, regardless of the user or team member, start with proven procedures, preventing the need to rediscover solutions from scratch.

This utility executes an autonomous, iterative loop, simulating a comprehensive agent workflow. It systematically manages tasks by checking current status, predicting the optimal next action using specialized tools, executing the required task (e.g., coding, testing), logging the progress, and scheduling the next iteration. It supports task discovery from multiple sources, including team tasks, swarm tasks, and file checklists, ensuring continuous, automated progress towards a defined goal.

This tool is designed to validate the syntax, completeness, and correctness of network configuration files. It helps ensure that network devices are configured according to established standards and best practices, preventing deployment errors and ensuring reliable network operation. Ideal for network engineers and DevOps teams.

This tool initializes a structured multi-agent swarm with built-in anti-drift configuration. It is designed for orchestrating complex, multi-file tasks that require coordinated efforts from three or more specialized AI agents, such as feature implementation, cross-module refactoring, or comprehensive security audits. It supports various topologies (hierarchical, mesh, etc.) to ensure robust and manageable parallel work execution across large teams.

This skill provides a structured framework for managing complex, multi-agent development tasks. It treats agents as a collaborative team, utilizing structured work items, defined ownership, Agent Kanban boards, and explicit merge gates. This ensures that large-scale, multi-tool, or cross-branch development efforts are predictable, highly visible, and result in reliably mergeable, production-ready artifacts.