Skill UI

This skill performs a comprehensive compliance pressure test against the EU AI Act. It systematically assesses any AI system across six critical dimensions: prohibited practices, high-risk classification, conformity assessment, provider role definition, transparency obligations, and systemic risk, ensuring readiness for EU market deployment.

Conducts a comprehensive, structured ethical review for any AI or ML model, feature, or product. It assesses risks related to fairness, bias, transparency, privacy, and societal impact, providing a risk tier score, necessary mitigations, and governance documentation suitable for pre-deployment compliance.

A comprehensive guide for implementing spatial, mixed-reality user interfaces. Inspired by Apple Vision Pro and AR aesthetics, this pattern uses environmental transparency, blurred glass-like panels, and dynamic lighting effects to create deeply integrated, context-aware layouts for web and mobile applications.

This skill details how to monitor Certificate Transparency (CT) logs using industry tools like crt.sh and Certstream. By analyzing publicly issued SSL/TLS certificates, users can proactively detect lookalike domains, unauthorized certificate issuance for owned assets, and early-stage phishing infrastructure targeting their organization. Ideal for SOC analysts and threat hunters.

A comprehensive tool designed to discover and map adversary-controlled assets, including C2 servers, phishing domains, and exploit kits. It utilizes sophisticated pivoting techniques across passive DNS records, Certificate Transparency logs, Shodan/Censys scans, and network fingerprints (JARM/JA3S) to generate actionable threat intelligence, perfect for CTI investigations and security operations.

A sophisticated system designed to automatically select the optimal AI agent and model tier (e.g., Haiku, Sonnet, Opus) for any non-trivial task. It replaces manual decision-making by integrating learned patterns, predictive modeling, and a robust 3-tier routing mechanism. The process is fully auditable, providing a detailed routing rationale to ensure transparency and reliable execution.

A comprehensive guide for preparing scholarly manuscripts involving spatial data. It outlines best practices for documenting data provenance (sources, projections, processing chains), ensuring reproducibility, and adhering to critical ethical standards such as geoprivacy and human-subjects protection. Essential for maintaining research integrity in academic publishing.

A specialized guide for authors targeting the ACM Conference on Fairness, Accountability, and Transparency (FAccT). It helps authors strategically frame their responsible AI research, ensuring claims are rigorously grounded in both technical measurement and social context. Covers necessary evidence bars, scope calibration, and submission best practices for this niche venue.

This comprehensive guide provides stringent methodological standards for authors submitting papers to ACM Multimedia (ACM MM). It details how to rigorously validate cross-modal claims, ensuring that evidence supports the multimedia contributions. Key areas covered include designing ablations to isolate fusion mechanisms, conducting robust user studies, reporting performance against matched baselines, and maintaining transparency regarding datasets, media licenses, and compute costs.

A comprehensive guide for submitting high-quality replication packages for academic journals like the American Economic Journal (AEJ: Micro). This package covers two essential components: self-contained, verifiable proofs for theoretical claims (the Proof Appendix), and a fully reproducible code and data deposit (the Code/Data Repository). It ensures that all numerical results, simulations, and empirical findings can be regenerated by the journal's editor, maintaining the highest standards of scientific transparency.

This guide provides rigorous methodological guidelines for ensuring the identification strategy in high-impact empirical economics papers (AER: Insights). It covers key areas including Difference-in-Differences (DiD), Regression Discontinuity Design (RDD), Instrumental Variables (IV), and structural parameter identification. It emphasizes the need for extreme methodological clarity, transparency, and conciseness required for top-tier academic publication.

A comprehensive guide for reporting rigorous educational and social science analyses, adhering to high standards of warrant and transparency (AERA). It covers advanced quantitative methods (HLM, IRT, quasi-experimental), qualitative coding, and mixed-methods integration, ensuring results are reproducible and evidence-based for publication.