Skill UI

This skill guides the deployment of decoy files, or "canary tokens," across critical file systems. These files act as tripwires, monitoring for unauthorized changes (modification, deletion, renaming). By tracking these high-value decoys, systems can detect ransomware encryption activity in real-time, providing a low false-positive, high-fidelity detection layer that complements traditional EDR and backup solutions.

This guide provides a deep dive into the cryptographic techniques used by ransomware, including AES, RSA, ChaCha20, and hybrid schemes. It covers identifying algorithms, analyzing key management patterns (strong, weak, flawed), and reverse engineering file encryption routines to assess decryption feasibility and support recovery efforts.

This guide outlines a comprehensive workflow for detecting and preventing the exposure of sensitive AWS credentials (access keys, session tokens) within source code repositories, CI/CD pipelines, and configuration files. It utilizes TruffleHog for deep historical scanning and integrates git-secrets for robust pre-commit hooks, significantly reducing the risk of credential theft and unauthorized account access.

This guide details the architecture and deployment of resilient Command and Control (C2) redirectors. By using tools like Nginx and Apache, operators establish an intermediary layer that filters beacon traffic based on malleable C2 profiles. This setup protects the true team server from detection, takedown, and analysis (OPSEC), enabling advanced techniques like domain fronting and traffic shaping for low attribution.

This skill guides the deployment and strategic use of honeytokens and canarytokens (decoy credentials, files, URLs, etc.). These high-fidelity decoys are designed to detect unauthorized access, insider threats, or adversary reconnaissance by triggering alerts upon any interaction, providing early warning deep within the kill chain.

This skill is designed for proactive firmware forensics and advanced threat hunting. It analyzes the EFI System Partition (ESP) to detect sophisticated, pre-OS persistence mechanisms, such as UEFI bootkits (e.g., BlackLotus, ESPecter). Detection methods include verifying file hashes against a trusted golden baseline, checking Secure Boot signatures, scanning with YARA, and flagging structural anomalies within the ESP.

This skill guides the deployment and operation of the Havoc C2 framework, enabling advanced adversary emulation. It utilizes Yaotl profiles to generate highly evasive agents (Demon) that employ sophisticated techniques like indirect syscalls and sleep obfuscation. The scope covers the full red-teaming lifecycle, including C2 setup, payload generation, post-exploitation, and lateral movement, making it ideal for authorized security testing and validating EDR detection capabilities.

This comprehensive tool suite provides high-fidelity parsers for key Windows artifacts, including MFT, Registry hives, Prefetch files, and ShellBags. Designed for digital forensics and incident response, it converts raw system evidence into structured CSV/JSON formats. The output is optimized for feeding into Timeline Explorer, enabling analysts to build comprehensive, correlated timelines and establish a clear chain of events.

This skill provides a comprehensive defense-in-depth framework for securing autonomous AI agents. It implements least-privilege tool allowlisting, identity binding, and human-in-the-loop controls to mitigate risks like prompt injection and tool poisoning. Use this when building agents that interact with sensitive systems (e.g., payments, file writes) to ensure governance, auditability, and compliance.

KAPE is a powerful, Windows-native triage tool used for Digital Forensics and Incident Response (DFIR). It executes in two phases: 'Targets' collect raw forensic artifacts (like registry hives, event logs, and prefetch files) from live systems, while 'Modules' parse this collected data into normalized, analyst-ready CSV/JSON formats. It enables fast, defensible, and scalable artifact collection, especially when full disk imaging is impractical.

This advanced protocol forces the AI to conduct a thorough context audit before attempting any complex task. It mandates reading every relevant file, identifying architectural gaps, and providing a detailed orientation statement. It is essential for multi-file modifications, system design, and debugging to prevent acting on assumptions and maintain high code accuracy. The AI cannot proceed until all unknown variables are resolved.

This skill guides developers through the complex process of integrating an iOS App Clip target into an existing Expo project. It covers setting up the necessary bundle identifiers, configuring associated domains (AASA file), and deploying the required metadata to allow the lightweight clip to be invoked directly from a URL alongside the parent application.