Skill UI

This skill proactively hunts for advanced firmware threats, including UEFI bootkits (e.g., BlackLotus, ESPecter), residing in the EFI System Partition (ESP). It establishes a cryptographic baseline, verifies bootloader signatures, scans for out-of-band changes, and uses YARA rules to detect persistence mechanisms related to pre-OS compromise (MITRE ATT&CK T1542.003). Ideal for forensic investigations and security hardening.

This skill provides a comprehensive workflow for verifying the integrity of a system's boot chain using TPM 2.0. It covers local validation by reading Platform Configuration Registers (PCRs) and replaying the event log, and remote attestation by generating and checking signed quotes. Use cases include enforcing Zero Trust device posture, detecting firmware tampering, or establishing secure secret baselines.

This utility detects and classifies various anomalies (such as spikes, flatlines, drift, or pattern breaks) within telemetry data streamed from IoT devices. It utilizes Z-score anomaly detection to provide actionable insights. This is crucial for investigating unusual device metrics, ensuring system health before deploying major firmware updates (canary advancement), or triaging fleet-wide operational alerts.

This tool orchestrates the entire firmware rollout lifecycle across large device fleets. It supports controlled, gradual deployment strategies, including canary testing and anomaly-gated advancement. Users can deploy new versions, check real-time status, execute rollbacks, or list all ongoing rollout campaigns, ensuring stable and reliable updates.

This tool provides comprehensive management capabilities for Cognitum Seed device fleets. Users can perform various actions such as creating new fleets, listing existing ones, adding or removing individual devices, and deleting entire fleets. It helps maintain firmware policies and manage the lifecycle of connected IoT devices.

A comprehensive guide for configuring and deploying the RuView wireless sensing system. It covers firmware build-time settings (sdkconfig), runtime parameters (NVS provisioning), multi-node mesh setup, and integrating advanced edge intelligence modules (ADR-041). Essential for tuning system behavior and capabilities without modifying core code.

A comprehensive guide for bringing RuView CSI sensing nodes (ESP32-S3/C6) online. This skill covers the full lifecycle: building the embedded firmware, flashing the device, provisioning WiFi credentials, and confirming the Continuous Sensing Image (CSI) data stream. Essential for physical hardware deployment and debugging of complex sensing systems.

This comprehensive guide establishes a rigorous framework for ensuring reproducibility in robot learning research. It details how to document both the "rerunnable half" (software, simulation versions, training configs, seeds) and the "auditable half" (physical hardware specs, sensors, firmware). Adhering to these standards allows experts to verify and rebuild the experimental results.

This comprehensive guide outlines best practices for achieving rigorous scientific reproducibility, especially for research involving storage systems and hardware dependencies. It mandates documenting not only the code but also the specific device models, firmware versions, operating states, and full measurement protocols to ensure verifiable results by peer readers.

Provides a comprehensive guide for packaging complex research artifacts, integrating hardware, firmware, and datasets. It outlines best practices for achieving academic badges (e.g., Available, Functional, Reusable) and winning the Best Research Artifact Award. Learn how to structure analysis paths, manage raw traces, and ensure reproducibility across different evaluation environments.

A comprehensive guide for authors preparing accepted academic papers for final publication. It details the requirements for a dual-publisher format (ACM and IEEE), covering mandatory steps like de-anonymization, completing dual metadata (ORCID, CCS, DOI), integrating reviewer feedback without scope creep, and permanentizing dataset/firmware links using public DOI archives. This ensures the submission meets the highest standards for academic rigor and publishing platforms.

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.