Skill UI

A comprehensive guide detailing the entire workflow for analyzing embedded firmware. It covers hardware-level extraction methods (UART, JTAG, SPI dump), filesystem analysis (SquashFS, JFFS2), binary reverse engineering using tools like Ghidra, and identifying common vulnerabilities (Buffer Overflow, Command Injection). Ideal for security researchers and embedded systems engineers.

A comprehensive guide for advanced threat hunting, focusing on detecting DLL side-loading and search-order hijacking (MITRE T1574). It instructs users to analyze Sysmon Event ID 7 logs, validate file signatures and hashes, and identify path anomalies to uncover APT persistence mechanisms. Essential for incident response and proactive threat detection in enterprise environments.

This skill guides the setup of a robust, centralized logging pipeline using Fluent Bit and Fluentd. It covers collecting logs from diverse sources (syslog, files, applications), routing, filtering, and enriching them before sending them to destinations like Elasticsearch, S3, or Splunk. Ideal for centralizing observability and building secure logging architectures across distributed infrastructure.

This skill performs in-depth literature reviews across complex communications domains, including 5G/6G, Wi-Fi, satellite networking, and advanced protocols. It systematically searches user-defined knowledge bases (Zotero, Obsidian, local files) and major academic databases (IEEE, ACM, ScienceDirect) to provide comprehensive survey summaries, related work analyses, and technical landscape overviews.

This guide outlines the rigorous methodology for ensuring that computational research results, especially those involving hardware architecture and simulation, are fully reproducible. It emphasizes pinning the entire result chain—from simulator commits and configuration files to workloads, sampling recipes, and statistics scripts—and mandates creating a manifest for every figure. Key practices include recording machine state for silicon runs and containerizing the environment to prevent data drift.

This guide details the comprehensive process for preparing security research artifacts for post-acceptance evaluation at major conferences like IEEE S&P. It covers the three types of evaluation badges (Available, Functional, Results Reproduced), strict handling constraints for exploits and malware, and best practices for packaging the submission (e.g., Dockerfiles, README structure) to ensure external evaluators can reliably reproduce findings within a limited time frame.

A comprehensive guide for structuring the evidence bundle required for robotics research submissions. It details the critical components—including hardware ledgers, time-stamped logs, configuration files, code, and data—necessary to ensure that scientific claims are fully auditable and reproducible, addressing the needs of skeptical reviewers.