Skill UI

This skill analyzes intrusion activity against the Lockheed Martin Cyber Kill Chain framework. It identifies which attack phases an adversary has completed, points where defenses failed or succeeded, and recommends controls to disrupt the attack at earlier stages. It is essential for post-incident analysis, threat intelligence reporting, and designing robust defense-in-depth strategies.

ScoutSuite is an open-source, agentless tool designed for comprehensive AWS security posture assessment. It queries AWS APIs to automatically enumerate resources across various services, identify critical misconfigurations (like public S3 buckets or overly permissive IAM roles), and generate an interactive HTML report. It is ideal for conducting cloud security audits, compliance checks, or incident response procedures using read-only credentials.

This skill guides users through performing comprehensive threat modeling using the OWASP Threat Dragon tool. It covers creating Data Flow Diagrams (DFD), identifying vulnerabilities using established methodologies like STRIDE and LINDDUN, defining mitigations, and generating formal PDF reports. Ideal for secure design reviews, architecture validation, and achieving GRC compliance.

Generates a comprehensive, structured, and blameless incident postmortem document. This skill is designed for Root Cause Analysis (RCA), P1/P2 incident reviews, and outage reports, ensuring the focus remains on systemic gaps and preventative action items rather than individual blame. It guides the user through gathering all necessary details, from the timeline and impact to defining concrete action owners and due dates.

KubeEye is a comprehensive cluster inspection tool designed for Kubernetes environments, particularly within KubeSphere. It systematically detects issues across workloads, nodes, and configurations by integrating multiple policy engines, including OPA/Rego, PromQL, file integrity checks, kernel parameter validation, and systemd health checks. Users can define custom inspection rules (InspectRule), schedule checks (InspectPlan), and retrieve detailed reports (InspectResult) to ensure cluster compliance and stability.

This guide provides expert principles for designing and auditing system-level experiments in computer architecture (e.g., for ASPLOS-level conferences). It details the proper usage of various instruments—from real silicon and FPGAs to cycle-level simulators and analytical models—ensuring that every claim is matched to a valid, stated error model. It emphasizes workload selection, establishing strong baselines, performing rigorous ablation studies, and honestly reporting measurement noise and limitations to maintain scientific credibility.

This guide outlines the rigorous standards for evaluating systems research, particularly for top-tier conferences like ATC. It provides detailed instructions on designing reproducible experiments, establishing fair baselines, utilizing realistic workloads, and reporting critical metrics such as tail latency, variance, and resource cost, ensuring the highest level of scientific rigor.

This guide provides a comprehensive checklist for evaluating Electronic Design Automation (EDA) research manuscripts for top-tier conferences like DAC. It emphasizes the necessity of using standard, recognized benchmark suites (e.g., ISPD, EPFL, TAU), comparing results against the true State-of-the-Art (SOTA), reporting full PPA metrics and runtime, and implementing ablation studies to ensure fairness, reproducibility, and generalizability, especially for ML-based methods.

This guide provides comprehensive standards for designing, auditing, and reporting empirical evaluations in database systems research. It emphasizes the principle that evidence must proportionally match the claim, requiring the use of real-world workloads, fair and tuned baselines, thorough measurement of variance and state (warm/cold), and fully reproducible evaluation harnesses. It also outlines the higher bar for 'Experiments & Analysis' papers.

This guide provides comprehensive standards for designing, executing, and reporting systems experiments for top-tier academic venues (like ICDE). It emphasizes methodological rigor, including mandatory ablation studies, reporting variance and tail latency (p99), demonstrating scaling trends (curves over points), disclosing cost trade-offs, and ensuring full hardware and software reproducibility to withstand adversarial review.

A comprehensive guide for designing and reporting rigorous experimental results in robotics research. It details the 'evidence ladder' (from simulation to real-world deployment), statistical best practices (e.g., sample size, confidence intervals), ensuring baseline fairness, and quantifying sim-to-real transfer gaps. Essential reading for researchers submitting to top-tier robotics conferences.

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.