Skill UI

This tool automates the extraction of embedded configuration details from Agent Tesla Remote Access Trojan (RAT) samples. It is designed to recover crucial exfiltration credentials (SMTP, FTP, Telegram), keylogger settings, and Command and Control (C2) endpoints by analyzing and deobfuscating the .NET assembly structure. Essential for threat intelligence, incident response, and malware reverse engineering.

This utility performs comprehensive forensic analysis on memory dumps. It leverages industry-standard tools like Volatility and Mimikatz to extract sensitive information, including cached credentials, NTLM/LM hashes, Kerberos tickets, and authentication tokens. It is crucial for incident response, assessing the scope of credential compromise, and investigating lateral movement attacks.

This guide outlines the process for forensically acquiring digital evidence from major cloud storage services, including Google Drive, OneDrive, Dropbox, and Box. The method combines API-based remote data extraction (such as audit logs and revision histories) with the collection of local endpoint sync-client artifacts. It is essential for conducting thorough incident response, security assessments, and e-discovery investigations involving cloud-synced data.

A comprehensive tool suite designed to analyze firmware images from embedded devices, including routers, IoT endpoints, and critical system components like UEFI/BIOS. It facilitates deep security analysis by performing firmware extraction, filesystem integrity checks, binary reverse engineering, and sophisticated detection of bootkits, backdoors, and unauthorized modifications.

This tool provides comprehensive security assessments for the entire IoT ecosystem, including physical hardware, embedded firmware, network communications, and cloud APIs. Testers can perform deep dives using hardware debugging (UART/JTAG), firmware extraction and reverse engineering, analyzing protocols (MQTT, CoAP, BLE), and simulating cloud-to-device attacks to identify vulnerabilities across all layers of the smart device stack.

This skill provides a comprehensive workflow for analyzing malicious software to extract actionable Indicators of Compromise (IOCs). It covers static analysis (PE parsing, hashing, string extraction) and dynamic analysis techniques. Users learn how to identify file hashes, network indicators (C2 IPs, domains), registry modifications, and behavioral artifacts, ultimately formatting the findings into standard STIX 2.1 formats for sharing and incident response.

A comprehensive guide to analyzing volatile memory (RAM) dumps using the Volatility 3 framework. This skill enables the extraction of crucial digital evidence, including running processes, network connections, loaded modules, credentials, and encryption keys. It is essential for incident response, detecting rootkits, process hollowing, or identifying malware when traditional disk-based forensics are insufficient.

Zeek is a powerful, open-source network analysis framework that functions as a passive security monitor. It generates highly detailed, structured logs (e.g., HTTP, DNS, TLS) from raw network traffic, allowing for deep metadata extraction. This skill guides users through deploying Zeek, writing custom detection scripts, and integrating the resulting rich security logs with SIEM platforms for advanced threat detection and forensic investigations.

Soft Predict Future is a structured foresight engine that triggers whenever the user asks about future outcomes or scenarios. It enforces a nine-step analytical pipeline (validation, signal collection, scoring, driver extraction, etc.), highlights required searches and inputs, and directs the user to web-enabled reasoning for predictive analysis.

bdistill transforms AI interactions into a structured, queryable knowledge base. It extracts, structures, and quality-scores domain knowledge from both closed-source AI models (in-session) and local open-source models (via Ollama). Users can build comprehensive reference datasets, Q&A pairs, or generate structured training data for traditional machine learning, ensuring validated and exportable domain expertise.

This skill provides comprehensive analysis and structured data extraction for academic research papers hosted on Hugging Face and arXiv. It can read paper content in markdown format, retrieve key metadata (authors, abstracts, GitHub repos), and identify linked assets such as associated models, datasets, and Spaces. Use this when the user provides a Hugging Face or arXiv URL, or explicitly requests a summary or explanation of an AI research topic.

A comprehensive guide detailing the workflow for firmware reverse engineering. It uses tools like binwalk to identify embedded filesystems (e.g., SquashFS, CramFS), perform entropy analysis to locate hidden data, and extract components. The process covers deep inspection, credential discovery, and reporting for IoT and embedded security assessments.