Skill UI

A comprehensive, pre-deployment, defense-in-depth security audit tool for Solidity smart contracts built with Foundry. It combines static analysis (Slither, Aderyn), symbolic execution (Mythril), property-based fuzz testing, and key hygiene checks to proactively detect critical vulnerabilities like reentrancy, access control flaws, and arithmetic bugs before deployment to any EVM chain.

A comprehensive guide detailing the systematic workflow for analyzing malicious Linux ELF binaries. It covers essential techniques including static analysis (using readelf, elftools), dynamic tracing (strace, ltrace), and deep reverse engineering (GDB) of various threats like botnets, cryptominers, and ransomware targeting Linux environments, containers, and cloud infrastructure.

An AI assistant for generating and analyzing Chinese Bazi (Four Pillars) and Ziwei Dou Shu charts using precise algorithms, supporting individual analysis, cross-validation, and structured report generation.

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.

Plaso (log2timeline) is an open-source engine used to generate forensic super-timelines. This skill guides the process of normalizing hundreds of diverse artifact types—including MACB times, registry keys, EVTX logs, and browser history—into a single, cohesive chronological view. The workflow covers extraction, filtering, and importing the timeline into Timesketch for collaborative analysis, which is crucial for incident response, correlating complex activity chains, and detecting anti-forensic techniques like timestomping.

A comprehensive security tool designed to detect and defend against subtle prompt injection attacks hidden within untrusted multi-modal content, such as web pages, PDFs, and images. It scans for obfuscated payloads (e.g., hidden CSS, metadata, zero-width characters) by combining content normalization, heuristic analysis, and dedicated ML detectors, ensuring that LLM agents only consume sanitized, trustworthy input.

Provides a comprehensive workflow and tooling (GuardDog) to triage npm packages and dependency trees for signs of malicious activity. It focuses on static analysis of install scripts, detecting credential exfiltration, reverse shells, and worming behavior, thereby safeguarding against software supply-chain attacks.

This skill detects malicious, misspelled, or brand-jacked package names (typosquatting) across major ecosystems (npm, PyPI, crates.io). It uses advanced techniques like edit-distance and keyboard-proximity analysis, powered by tools like typomania and OSSGadget, to flag potential supply chain attacks *before* a dependency is installed. It is essential for implementing robust CI/CD security gates and proactive dependency screening.

Chainsaw is a fast, Rust-based tool for digital forensics and threat hunting. It enables analysts to rapidly perform Sigma rule checks and keyword searches across collected Windows Event Logs (.evtx) and specialized artifacts (like shimcache and SRUM). Ideal for first-response and offline analysis, it provides detection capabilities without needing a SIEM ingestion pipeline, allowing quick hypothesis testing in the field or air-gapped environments.

A comprehensive guide for threat hunting and detection engineering focused on identifying SSO and OAuth token abuse. It detects 'pass-the-cookie' and session replay attacks where attackers use stolen tokens (like cookies or refresh tokens) to move laterally across federated SaaS platforms (Entra ID, Okta) without needing credentials. Essential for post-incident scope analysis and validating T1550.001 coverage.

This static analysis skill audits SQL queries across major data warehouses (BigQuery, Snowflake, Redshift, Postgres) for performance and cost anti-patterns (e.g., SELECT *, full scans, Cartesian joins). It provides a health score (0-100) and generates a prioritized, actionable cost-reduction plan, detailing the 'why' and the 'how' to fix expensive queries.