Skill UI

A comprehensive guide for digital forensics professionals to investigate compromised Docker containers. It covers best practices for preserving container state, analyzing image layers, reviewing runtime logs, and examining host system artifacts to identify evidence of malicious activity, container escapes, or misconfigurations.

This skill guides the deployment of canary tokens and honeytokens across critical systems, such as fake AWS credentials, DNS records, and document beacons. These decoys are designed to trigger immediate alerts via webhooks when accessed by attackers, serving as a powerful early warning system for breach detection and intrusion detection.

A comprehensive guide and workflow for digital forensic investigators to analyze ransomware artifacts. This process involves preserving volatile evidence (memory dumps), identifying the ransomware variant from file extensions and ransom notes, establishing the attack timeline using Prefetch and Event Logs, and extracting critical Indicators of Compromise (IoCs) such as Bitcoin addresses and Tor sites. Essential for incident response and recovery planning.

This skill outlines the architecture and methodology for deploying canary files (honeytokens) across critical file systems. By placing decoy documents with known content in high-value locations, this system monitors for unauthorized modifications, deletions, or encryption attempts. It detects ransomware by triggering high-fidelity alerts via File Integrity Monitoring (FIM) systems, serving as a crucial deception layer that complements existing EDR/AV solutions.

A comprehensive tool for aggregating and displaying system metrics (counters, gauges, histograms) from the observability namespace. It computes baselines and flags anomalies by detecting deviations exceeding standard deviations, providing a clear snapshot of system health, error rates, and performance degradation trends.

This tool guides authors in assessing whether their fluid mechanics manuscript is a suitable fit for the Journal of Fluid Mechanics (JFM). It emphasizes the need for fundamental physical understanding (e.g., new scaling laws, instability mechanisms) over mere demonstration of applied methods or devices. It covers rigorous standards for computation, experimentation, and theoretical analysis, serving as a crucial pre-submission framing and venue-selection resource.

Provides comprehensive guidelines for achieving high reproducibility in Machine Learning Systems (MLSys) performance claims. It details how to rigorously control sources of non-determinism (e.g., thermal noise, random seeds) and pin the entire technical stack—from hardware and drivers to frameworks and serving policies—to ensure results can be validated by third parties.