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This skill automates the Design Space Exploration (DSE) process, crucial for computer architecture and Electronic Design Automation (EDA). It runs complex simulations, analyzes performance metrics, and systematically tunes parameters (e.g., cache size, pipeline width) across a defined parameter space. It iterates until the objective metric is optimized or a defined timeout is reached, making it ideal for complex hardware tuning and system design.

Executes authorized, advanced attack simulations against Active Directory environments to identify critical misconfigurations, weak credentials, and exploitable trust relationships. Utilizing industry tools like BloodHound, Mimikatz, and Impacket, this process simulates complex attacks such as Kerberoasting, Golden Ticket forging, and domain compromise path analysis, helping organizations validate their security posture against real-world threats.

This guide details how to use GoPhish, an open-source framework, to conduct authorized phishing simulations. You will learn the complete lifecycle, from deploying the tool and configuring SMTP profiles, to creating realistic email templates and cloning secure landing pages. The skill culminates in analyzing campaign results to assess organizational security posture and improve employee resilience against social engineering attacks.

This technique exploits vulnerable Active Directory Certificate Services (AD CS) HTTP web-enrollment endpoints (ESC8) that lack Extended Protection for Authentication (EPA). By coercing a Domain Controller (DC) to authenticate and relaying the captured NTLM credentials, an attacker can obtain a DC machine certificate. This certificate is then used via PKINIT to request a TGT, recover the DC's NT hash, and ultimately achieve full domain compromise via DCSync. Essential for advanced AD red-teaming simulations.

Provides rigorous guidelines for conducting robust, verifiable, and reproducible evaluations in networking research. It emphasizes moving beyond simple simulations by demanding the use of real target platforms, documenting full system provenance (traces and configs), establishing honestly tuned baselines, and reporting detailed measurement statistics and uncertainty. Essential for authors aiming for top-tier conference submissions.

A comprehensive guide for designing, auditing, and reporting reliable robotic experiments, particularly for major conferences like IROS. It moves beyond simple success rates, detailing how to map every claim to verifiable evidence (trials, simulations, field data). Key areas covered include defining pre-set success criteria, reporting failure taxonomies, ensuring fair baselines, and quantifying the critical sim-to-real gap using advanced small-n statistical methods.

A comprehensive guide on achieving scientific reproducibility for complex computer architecture and system simulations. It details how to manage and pin all dependencies—including simulator commit hashes, local patches, workload binaries, hardware states, and post-processing scripts—to ensure that every reported figure and number can be accurately regenerated, regardless of time or environment changes.

This comprehensive guide provides best practices and an audit checklist for designing robust, real-world experiments in mobile and wireless communication systems. It emphasizes moving beyond simulations, detailing necessary RF setups, quantifiable energy metrics, and reporting statistical distributions to ensure high academic credibility.

This guide helps authors determine if their manuscript is suitable for submission to Progress in Aerospace Sciences. It emphasizes that the journal requires comprehensive, critical review articles that synthesize existing knowledge, rather than presenting original primary research, simulations, or experiments. Use this tool for fit assessment, scope definition, and structural re-framing before submission.

This guide provides a comprehensive audit checklist for designing and evaluating sensor-based systems (SenSys). It mandates rigorous, real-world measurements across five core axes: energy efficiency, on-device latency distribution, ground truth accuracy, deployment realism, and embedded AI footprint. It ensures that research results are based on measured hardware performance, not simulations or idealized baselines.

This guide provides comprehensive best practices for designing and auditing cloud system evaluations, particularly for academic submissions. It mandates that claims must be supported by measured evidence from real or realistic deployments, moving beyond simple simulations. Key metrics emphasized include tail latency (p99), operational cost modeling, and verifiable behavior under scale and multi-tenancy.

A skill for designing evidence and validation strategies for academic manuscripts submitted to the Journal of Systems Engineering. It covers standards for numerical experiments, simulations, real data, case studies, and empirical research, ensuring claims are supported by appropriate evidence rather than generic robustness checks.