Skill UI

A comprehensive tool to audit Kubernetes cluster security compliance against the CIS Kubernetes Benchmark. It inspects control-plane components, worker nodes, and cluster policies to identify security misconfigurations (PASS/FAIL/WARN). Essential for establishing security baselines, conducting periodic compliance audits (e.g., SOC 2, PCI DSS), and validating hardening efforts in CI/CD pipelines.

This skill guides the definition of a defensible and decision-relevant competitor set. It is the foundational step before any benchmarking, helping users identify, categorize, and scope competitors (Direct, Adjacent, Aspirational) based on criteria like size, niche, market reach, and positioning. It ensures that strategic analysis focuses only on the most relevant rivals.

A comprehensive guide for authors submitting research on software testing, analysis, verification, and fuzzing to ISSTA. It provides strategic advice on framing the paper, establishing necessary evidence (e.g., fault models, benchmarks, real-world data), and ensuring the contribution aligns precisely with the requirements of top-tier CS conferences.

A comprehensive guide for authors responding to reviewer reports and editor decisions (major/minor revision, rejection). It details how to structure a point-by-point rebuttal letter, what new data (e.g., stability, benchmarking) must be added, and how to ensure the revised manuscript maintains a clear scientific advance while adhering to publication guidelines.

Provides strict guidelines for assembling supplementary material for Computer Aided Verification (CAV) papers. It details how to separate decision-critical content (main theorems, proof ideas) into the primary body, while moving voluminous details (full proofs, massive benchmarks) to the optional appendix or artifact, ensuring the paper remains judgeable from the main text alone.

This detailed guide outlines the structure and scientific rigor required for publishing high-quality papers in the domain of Computer Aided Verification (CAV). It instructs authors to transition their work from mere system demonstrations or benchmark reports to formally guaranteed, mathematically precise contributions, emphasizing clear scope definition, stated theorems, and proof ideas.

A comprehensive pre-writing guide for researchers determining the optimal venue and track for a data science contribution. It outlines a two-stage decision process: first, assessing if the work aligns with the KDD focus on data regimes (scale, drift, heterogeneity); and second, selecting the correct track (Research, ADS, Datasets & Benchmarks, etc.) based on the strongest, most honest evidence available—especially emphasizing real-world deployment outcomes.

This guide helps researchers evaluate whether their high-impact optics or photonics manuscript meets the stringent criteria of Optica. It covers ideal scope (e.g., nanophotonics, quantum optics), required experimental rigor (quantitative measurements, state-of-the-art benchmarking), article structure (concise, load-bearing figures), and official submission guidelines, ensuring the work is framed for broad scientific interest.

This skill provides an in-depth, entity-level scorecard for clinical or biomedical Named Entity Recognition (NER) models. It calculates precision, recall, and F1 scores (both strict and partial match modes) using a user-supplied gold corpus. Key features include per-label error breakdown, a confusion matrix, and detailed False Negative/False Positive analysis, essential for debugging model performance in highly sensitive medical domains.