Skill UI

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.

This guide provides comprehensive steps for deploying and configuring the Rapid7 InsightVM platform. It covers setting up the Security Console, deploying distributed Scan Engines, configuring various scan templates (e.g., CIS Policy, PCI ASV), and implementing deep credentialed scanning using WMI/SMB for thorough vulnerability visibility across enterprise environments. Use this when building robust security architectures or achieving compliance.

This assistant provides comprehensive performance profiling for complex computing systems. It analyzes various targets, including Python scripts, running processes, GPU utilization, and memory bottlenecks. Utilizing both external tools (like nvidia-smi, cProfile) and advanced code instrumentation, it measures CPU, memory, and interconnect performance, generating a structured report with actionable insights and a mandatory changelog of all modifications.

This guide provides a comprehensive framework for investigating production issues on the Nexu platform using the Datadog Logs API. It details how to query crash events, analyze OpenClaw stderr output, check gateway startup status, and review API request logs (including 5xx errors). It includes best practices for authentication, filtering by pod/time, and parsing raw JSON logs using Python for actionable insights.

This extension component is integral to the KubeSphere Observability Platform. It provides robust mechanisms for collecting, processing, and storing Kubernetes events using the kube-events-exporter. Users can leverage the detailed event query API to gain deep insights into cluster activity, making it essential for comprehensive system monitoring and observability setup.

This guide helps researchers determine the suitability of their energy-related manuscripts for the Applied Energy journal. It shifts the focus from single-device performance or component novelty to generalizable, systems-level insights. Key areas include energy-systems modeling, decarbonization pathways, techno-economic analysis (TEA), life cycle assessment (LCA), and assessing integrated energy transitions across sectors.

A comprehensive set of reusable patterns for structured incident triage in AWS environments. Includes pre-built Logs Insights queries for detecting errors, latency spikes, cold starts, and OOM events. Also provides systematic correlation methods linking CloudWatch alarms to deployment events, and a decision tree to narrow the blast radius scope from account to resource.

A comprehensive guide for authors targeting high-impact chemical engineering venues. It helps authors re-frame their work from simple material synthesis to demonstrating an applied advance with clear mechanistic insight. Focus areas include catalysis, separation, functional materials, and performance benchmarking under realistic process conditions.

This comprehensive guide details the academic review pipeline for CoNEXT conference submissions. It covers double-anonymous double-blind review, the two-round TPC process, and the critical nature of the one-shot "major revision," which functions similarly to a journal's revise-and-resubmit cycle. It provides authors with strategic insights to structure their research for rigorous academic scrutiny.

A comprehensive guide for drafting and revising high-level microarchitecture papers for top-tier conferences. It mandates the rigorous 'four-beat argument' structure: starting with characterization evidence, defining the insight, detailing the mechanism, and completing with thorough cost accounting. This ensures the paper is a defensible engineering argument.

This skill provides comprehensive guidance for submitting research to npj Computational Materials. It defines the journal's scope, requiring rigorous computational methods (DFT, MD, ML, high-throughput screening) that yield generalizable materials insights or predictive capabilities. It educates authors on the required standards, including methodological rigor, reproducibility, and adherence to FAIR data principles, helping authors re-frame their work for a high-impact venue.