Skill UI

This tool provides a comprehensive overview of your project's memory usage across all associated memory systems. It reports metrics such as line counts, topic file counts, capacity limits, stale entries, and actionable recommendations to help maintain memory integrity and optimize performance.

A comprehensive tool designed to audit and ensure the consistency of brand entity data across critical online knowledge sources, including Wikidata, Google Knowledge Panel, and Wikipedia. It systematically checks key properties like founding date, HQ location, and official website against the brand's source of truth profile, flagging discrepancies to protect AI engine trust and maximize digital visibility. Essential for global brand managers and digital marketers.

This skill reconstructs the full narrative behind a piece of code by analyzing local Git history. Instead of just naming the last author (like git blame), it traces the file's origin, tracks subsequent changes, identifies related companion files, and extracts intent clues from commit messages. Use this when you need to know *why* code exists before refactoring, making risky edits, or understanding complex system behavior.

A comprehensive guide for authors submitting to Computer Science journals, detailing how to achieve full experimental reproducibility. It systematically covers locking environments (dependencies, hardware, seeds), preventing data leakage, structuring the submission package, and creating a clear audit trail so reviewers can independently reproduce all main results from raw data.

This guide outlines the strict rules for dividing a research paper into the main body and supplementary materials (artifact/appendices), particularly for database systems conferences like EDBT. It emphasizes that all decision-critical evidence must be presented within the reviewed pages, ensuring reviewers can judge the work without needing to open external files. It covers page budget discipline, reproducibility standards, and practical partitioning techniques.

A comprehensive guideline for establishing and documenting the reproducibility of complex sensor-systems artifacts. It covers necessary components such as firmware sources, detailed hardware Bills of Materials (BOMs) and board files, raw sensor traces, ground truth data, and calibrated measurements. Use this guide to ensure that scientific claims can be verified by a competent reader, detailing the exact dependencies on physical equipment and software toolchains.

This skill provides comprehensive command-line interface (CLI) support for OpenViking, enabling agents to manage external resources, perform filesystem operations, and execute data tasks. It covers adding resources, importing/exporting data, browsing directories, reading/writing files, and advanced searching (find, grep, search). Use cases include data pipeline setup, knowledge graph manipulation, and system maintenance.

This skill generates fully valid Microsoft Threat Modeling Tool (.tm7) files. It translates complex system architectures, including components, data stores, data flows, and external actors, into the required WCF DataContractSerializer XML format. Use it for structured STRIDE threat modeling outputs compatible with Microsoft's official tool.

A specialized guide for assessing whether a storage systems or file system manuscript fits the USENIX FAST conference. It covers venue positioning, required evidence (realistic workloads, fault models), appropriate framing, and comparison methodologies necessary for top-tier systems conference submissions.

This specialized skill traces the complete lifecycle of an uploaded or imported file through complex backend systems. It maps the sequence from ingress to final consumer, tracking every derived artifact, archive extraction, format conversion, parser invocation, and deserialization step. It is crucial for security analysis, vulnerability research, and understanding how a file reaches a decisive backend effect after initial sandbox assumptions are made.

This specialized skill is designed for Digital Forensics and Incident Response (DFIR) scenarios. It correlates evidence from multiple disparate sources—including EVTX, PCAP, registry, memory, and filesystem artifacts—to build a comprehensive, ordered, and replayable forensic timeline. Use it when the goal is to map the full sequence of attacker actions, establishing initial access, lateral movement, and persistence chains based on cross-source evidence correlation.

Orchestrates a robust, multi-stage cyber defense pipeline using specialized AI agents to analyze raw log files (e.g., nginx, auth, syslog). The system ingests logs, detects anomalies, classifies overall risk, and generates a comprehensive, actionable incident report, making it ideal for security investigations and breach analysis.