Skill UI

An expert in designing, implementing, and optimizing scalable, reliable, and cost-effective data pipelines for both batch and streaming data. Covers advanced topics including ETL/ELT patterns, Kappa/Lambda architectures, workflow orchestration (Airflow/Prefect), transformation using dbt/Spark, and managing modern storage formats like Delta Lake and Iceberg. Focuses heavily on data quality, monitoring, and cost optimization strategies.

This skill provides comprehensive methodologies for capturing, analyzing, and documenting network protocols. It is crucial for security researchers, developers ensuring interoperability, and engineers debugging complex network communications by understanding the underlying data exchange mechanisms.

This skill details the process of reverse engineering Go-compiled malware using Ghidra. It involves parsing Go-specific metadata structures like buildinfo and pclntab to recover stripped or obfuscated function names. Users can also extract embedded module paths and dependency strings, providing critical insight into the malware's capabilities and third-party dependencies.

A comprehensive guide for analyzing PCAP files captured during malware execution. It demonstrates advanced techniques using Wireshark, Zeek, Suricata, and Python (scapy) to identify Command and Control (C2) communications, data exfiltration channels, Domain Generation Algorithms (DGA), and periodic beaconing patterns. Essential for malware reverse engineering and network threat detection.

This guide outlines the process of passive external reconnaissance using Open Source Intelligence (OSINT) techniques. Users can map an organization's digital footprint by gathering comprehensive data, including DNS records, subdomains, certificate transparency logs, social media profiles, and leaked credentials. It is critical for the planning phase of penetration testing, asset mapping, or preparing for social engineering, all without actively interacting with target systems.

Open Source Intelligence (OSINT) is the critical first phase of red team engagements. This guide details how to systematically collect publicly available data about a target organization. It covers mapping network assets (domains, IPs), identifying personnel for social engineering, discovering credential leaks, and mapping the entire technology stack. Use this skill during authorized security assessments to build comprehensive attack surface maps and initial access strategies.

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.

This skill analyzes accumulated usage logs from a complex research harness (ARIS) to propose structural optimizations. It identifies areas for improvement in SKILL scripts, default parameters, workflow ordering, and reviewer prompts. It is designed to automate the meta-level engineering process, ensuring that system efficiency is continuously improved based on real-world usage data.

A sophisticated and print-friendly presentation template designed for explaining complex system architectures and data pipelines. Featuring a blueprint grid, cream background, and rust red accents, this deck is ideal for technical deep dives in engineering and system design.

A comprehensive project management guide for submitting data-intensive systems papers to ICDE. It details the two-round submission cycle, setting milestones from initial evidence locking (mechanisms, workloads) to final IEEE Xplore publication. This workflow helps researchers backward-plan the entire data-engineering lifecycle, budgeting time for revisions and mitigating common submission risks.

A comprehensive methodology guide for designing and auditing empirical studies in software engineering, specifically focused on maintenance, evolution, and change impact. It outlines best practices for ensuring evidence is proportional to the claim, managing data provenance (SHAs, criteria), handling statistical confounds (survivorship, effect sizes), and integrating rigorous LLM evaluation protocols.