Skill UI

This tool analyzes cryptocurrency options flow from major derivatives exchanges (e.g., Deribit). It tracks open interest, calculates put/call ratios, and analyzes implied volatility to identify institutional positioning, gauge market sentiment, and detect significant trading activity (block trades) in BTC and ETH options. It is essential for quantitative traders and market analysts seeking to predict market movements.

This tool analyzes cryptocurrency options flow from major derivatives exchanges (e.g., Deribit). It tracks open interest, calculates put/call ratios, and analyzes implied volatility to identify institutional positioning, gauge market sentiment, and detect significant trading activity (block trades) in BTC and ETH options. It is essential for quantitative traders and market analysts seeking to predict market movements.

A comprehensive tool for querying and analyzing deep blockchain data across multiple EVM-compatible networks, including Ethereum, Polygon, Arbitrum, and Optimism. It supports tracking transactions by hash, checking address balances, inspecting blocks, and resolving ERC-20 token holdings. Ideal for crypto analysts, developers, and researchers requiring programmatic, detailed insights into on-chain financial activity and protocol interactions.

This skill allows users to track large cryptocurrency transactions and the movements of "whale" wallets across multiple blockchains. It monitors exchange inflows and outflows, helping users analyze potential market trends, identifying accumulation or selling pressure from major holders. Ideal for crypto analysts and investors needing real-time market insights.

Programmatically query ChEMBL bioactive molecules, targets, and approved drugs via the Python client; filter by structure, properties, activities, and indications to support inhibitor hunting or SAR studies for medicinal chemistry.

This skill guides users on querying Azure Monitor activity and sign-in logs using KQL to detect sophisticated security threats. It covers identifying suspicious administrative activities, impossible travel scenarios, privilege escalation attempts, and unauthorized resource modifications. Ideal for Security Operations Center (SOC) analysts building advanced threat hunting rules or developing cloud SIEM detections.

Hindsight is an open-source tool designed for digital forensics, parsing artifacts from various Chromium-based browsers (Chrome, Edge, Brave, etc.). It extracts and correlates all web activity data, including browsing history, cookies, cached content, downloads, and saved credentials, into a unified timeline. This tool is essential for incident response, insider threat investigations, and reconstructing user web activity in security and legal contexts.

This skill analyzes DNS query logs to detect sophisticated threats such as data exfiltration via DNS tunneling, Domain Generation Algorithm (DGA) usage, and covert Command and Control (C2) channels. It utilizes advanced techniques like entropy analysis, query volume anomaly detection, and subdomain length analysis within SIEM platforms, helping SOC teams identify threats that bypass traditional network security controls.

This skill details the forensic analysis of Windows LNK shortcut files and Jump Lists. These artifacts record critical user activity, including file access and program execution, even after the original source files have been deleted. It covers using specialized tools (LECmd, JLECmd) and Python parsing techniques to reconstruct user timelines, making it essential for incident response and deep digital forensics examinations.

This guide details a comprehensive workflow for digital forensic investigations focusing on Amcache.hve registry hive. It teaches how to use AmcacheParser to extract evidence of program execution, application installation, and driver loading, including correlating SHA-1 hashes with threat intelligence and reconstructing timelines. This is crucial for DFIR work to identify deleted executables and malicious activity.

This skill parses Windows Prefetch (.pf) files to reconstruct detailed application execution history. It is crucial for digital forensics, allowing analysts to track execution timelines, identify suspicious or renamed binaries, and investigate potential malware activity or lateral movement during incident response. It uses Python to extract run counts and accessed file details.

A comprehensive guide on implementing AWS CloudTrail log analysis for robust security monitoring, threat detection, and forensic investigation. Learn how to configure CloudTrail, use Amazon Athena for SQL querying, and leverage CloudWatch Logs Insights and SIEM integration to identify unauthorized access, privilege escalation, and suspicious API activity across AWS services.