Skill UI

Guides engineers through initializing MCUs, crafting RTOS-aware drivers, and tuning power/timing for STM32, ESP32, and bare-metal targets; covers constraint analysis, interrupt safety, resource tracking, and validation with static analysis and real-time debugging.

This skill provides a comprehensive, expert guide to the entire reverse engineering lifecycle. It covers advanced techniques for binary analysis, including static analysis (disassembly, metadata extraction, control flow mapping) and dynamic analysis (API hooking, memory tracing). Mastery includes utilizing industry-leading tools such as IDA Pro, Ghidra, radare2, and specialized frameworks like pwntools and angr. The methodology is structured for secure research, CTF challenges, and vulnerability assessment, emphasizing best practices and ethical use.

COBRApy is a Python library for constraint-based reconstruction and analysis of genome-scale metabolic models, letting you load SBML/JSON/YAML models, perform FBA/FVA/sampling, and study gene or reaction knockouts for systems biology and metabolic engineering.

Reverse engineer stripped Go malware in Ghidra with GoResolver and Python helpers to recover function metadata, strings, and dependencies while giving SOC analysts actionable steps for threat hunting and detection tuning.

Guide to detect and exploit blind Server-Side Request Forgery risks via out-of-band callbacks, DNS/timing analysis, and cloud metadata enumeration when assessing webhooks, document processors, or internal services.

Deploy Zeek to passively capture network packets, parse protocol metadata, and derive high-fidelity logs for threat hunting, anomaly detection, and SIEM ingestion during security assessments or incident response.

Use steganalysis and metadata tools to detect concealed payloads inside images, audio, and video, guiding investigators through metadata review, automated scans, LSB statistics, and extraction attempts for covert channel detection.

COBRApy is a Python library for constraint-based reconstruction and analysis of genome-scale metabolic models, letting you load SBML/JSON/YAML models, perform FBA/FVA/sampling, and study gene or reaction knockouts for systems biology and metabolic engineering.

PyOpenMS provides comprehensive Python bindings for the OpenMS library, enabling advanced computational mass spectrometry analysis. It supports end-to-end workflows covering proteomics, metabolomics, and general spectral data processing. Key features include handling diverse file types (mzML, mzXML), signal processing, feature detection, and peptide/protein identification using established search engines.

A comprehensive debug bundle designed to collect diagnostic evidence from AppFolio property management API integrations. It captures essential data points including API connectivity status for properties, tenants, and work orders; authentication status using client credentials; recent integration error logs; and environment metadata. This allows support engineers to perform root cause analysis and diagnose issues like connectivity failures or authentication rejections without needing live environment access.