Skill UI

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.

KAPE (Kroll Artifact Parser and Extractor) is a specialized, rapid triage tool for incident response. It collects specific, volatile forensic artifacts (like registry hives, event logs, and browser data) from compromised Windows systems. It is designed for remote, at-scale artifact collection when full disk imaging is impractical, enabling analysts to quickly build a defensible, parseable dataset for initial investigation and containment.

This skill provides methods to verify artifact signatures and SLSA provenance using tools like cosign and slsa-verifier. It is crucial for building secure CI/CD pipelines, detecting supply-chain attacks (e.g., tampering, compromised builds), and ensuring that deployed artifacts originate from trusted sources and build environments. Key features include enforcing keyless OIDC identity pinning and validating SLSA Build Levels.

This skill facilitates the systematic triage and closure of open promises or commitments within a project context. It guides the user to reliably close these commitments using verifiable, real-world outcomes via the acc_act(runtime="outcome") command. It emphasizes maintaining rigorous provenance discipline, ensuring that status updates are based only on external or runtime validated results, thereby maximizing data integrity and accountability.

The Anti-Deception Harness detects manipulative user prompts, such as manufactured urgency, pressure to validate, or undue reliance on authority. It calls an external tool to provide a structured scaffold detailing integrity procedures and counter-evidence, ensuring responses are honest and robust, preventing compromised or sycophantic answers. Ideal for high-stakes decision support.

A comprehensive guide for authors transitioning an accepted, anonymous conference submission into the final, public camera-ready version for publication. It covers de-anonymization steps, including restoring author details, updating proceedings metadata, reflowing figures for multiagent papers, and integrating rebuttal promises without expanding the core contribution. This ensures adherence to strict publishing standards.

A comprehensive guide for drafting a technical author response (rebuttal) for peer-reviewed academic papers (CAV). It teaches authors how to structure a rebuttal by addressing specific objections related to proof soundness, benchmark fairness, novelty, and scope, using only verifiable evidence, not future promises.

This comprehensive guide assists researchers in converting an accepted, anonymous draft for the Conference on Learning Theory (COLT) into the final, polished camera-ready version for publication in PMLR proceedings. It covers critical steps such as de-anonymization (restoring author details, acknowledgements), consolidating all promised revisions into the main text, verifying PMLR metadata and BibTeX consistency, and preparing the full archival PDF.

A comprehensive guide for authors preparing their accepted ECCV papers for multiple publication venues, including Springer LNCS, ECVA open-access portals, and arXiv. It details critical steps such as managing page budgets, ensuring metadata accuracy, fulfilling rebuttal promises, and adhering to strict publication deadlines.

This guide details the strategic process of writing a rebuttal for top-tier academic conferences (such as HRI). It provides a framework for triaging reviewer critiques, addressing methodological concerns (e.g., sample size, confounding), and making credible, actionable promises for the final camera-ready version, all while maintaining academic integrity and anonymity.

This comprehensive guide details the rigorous process of preparing a double-blind academic paper for its final, public, dual-published status across ACM and IEEE platforms. It systematically covers de-anonymization (restoring names, affiliations, and acknowledgments), ensuring all rebuttal promises are met within strict page limits, and managing complex metadata requirements (ORCID, CCS concepts, rights blocks). It serves as a final production checklist for authors.

This comprehensive guide outlines the critical steps for authors preparing their accepted ICLR paper for camera-ready submission. It covers de-anonymization procedures, incorporating all fixes promised during the discussion period, completing OpenReview metadata, and assembling all necessary presentation assets, including posters, slide decks, and videos, according to current conference guidelines.