Skill UI

A structured framework for performing initial security incident triage. It guides analysts through classifying incident types using established standards like NIST SP 800-61r3, assigning business impact severity (P1-P4), enriching alerts with threat intelligence, and determining containment and escalation paths. Use when SIEM/EDR alerts require human judgment or when coordinating multiple concurrent security events.

This comprehensive guide provides expert protocols for writing the 'Related Work' section in Machine Learning papers. It teaches authors how to maintain 'versioned honesty' by meticulously triaging the literature into distinct lanes (e.g., method lineage, model artifacts, evaluation infrastructure). It covers advanced topics like managing concurrent work, citing arXiv status accurately, and avoiding self-citation violations, ensuring the paper is positioned rigorously within the rapidly evolving academic field.

This guide offers advanced methodological advice for structuring the 'Related Work' section for top-tier theoretical computer science conferences (like FOCS). It teaches researchers how to treat research history as 'lineage engineering,' requiring precise citation of specific preprints (arXiv version pinning), distinguishing between conference and journal versions, and transparently addressing concurrent results. This ensures the bibliography is rigorous, consistent, and withstands expert review.

A comprehensive guide for authors submitting to top-tier Software Engineering (SE) conferences (like FSE, ICSE). It details how to structure the Related Work section by focusing on the 'delta'—the unique difference—between your work and prior art. It emphasizes covering the full SE literature landscape, maintaining anonymity, and correctly declaring overlaps with concurrent or prior versions.

This guide provides a comprehensive framework for authors to position their work against existing literature, concurrent submissions, and public scrutiny in top-tier AI conferences. It teaches how to separate similar components from identical scientific problems, build verifiable novelty claims, and respond robustly to reviewer challenges.

A comprehensive guide for authors submitting to health economics journals, focusing on pre-submission pre-mortem analysis. It teaches how to anticipate specific, expert-level critiques—such as selection bias, concurrent confounding, or econometric limitations—and proactively structure the manuscript to neutralize these objections before they are raised by reviewers.