Skill UI

Develop and review laboratory automation resources, liquid-handling plans, and offline simulations. Use for PyLabRobot protocols or API questions with explicit operator safety gates for physical execution. Supports hardware-agnostic frontends and device-specific backends for lab automation development.

Qiskit is a Python framework for building, simulating, and executing quantum circuits. It supports local simulation, noisy simulation, and IBM Quantum hardware execution. Users can utilize V2 primitives like Sampler and Estimator for variational algorithms and quantum machine learning tasks.

A hands-on methodology for testing IoT and embedded devices, covering hardware reconnaissance, firmware extraction and analysis, bootloader access, runtime exploitation, wireless protocol attacks, companion app review, and cloud API abuse.

A comprehensive guide for developers building AI demos and GPU compute tasks on Hugging Face Spaces using ZeroGPU hardware. It covers best practices for writing and reviewing code that utilizes the `@spaces.GPU` decorator, managing runtime duration and quota, and understanding the specific CUDA availability model and process isolation rules required for reliable ML deployment.

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 guide provides a comprehensive onboarding process for RuView (WiFi-DensePose). It details three deployment tiers: Docker simulation for quick testing and UI exploration, source code build verification (Rust/Python), and live sensing setup utilizing ESP32 hardware. Ideal for new users or rapid prototyping.

A comprehensive guide for authors preparing an artifact appendix (ae.tex) for major conferences like ASPLOS. Learn how to structure your submission to achieve Available, Functional, and Reproducible badges. Covers managing complex dependencies (hardware, software, data), structuring the repository for evaluators, and optimizing collaboration protocols to ensure successful post-acceptance validation.

This guide details best practices for ensuring scientific rigor and reproducibility in complex system research. It outlines the necessity of capturing the full execution state—including hardware steppings, BIOS revisions, kernel configurations, toolchain versions, and dataset provenance—to prevent data decay and validate findings against environmental drift.

A comprehensive guide for authors developing systems papers for major conferences (like ATC). It details how to build a reproducible story by pinning all environments (hardware, OS, toolchain, workload, code), providing turnkey one-command execution paths, and preparing a runnable, anonymized artifact package for academic review and evaluation.

This guide details how to prepare high-quality, verifiable system artifacts (code, data, hardware) for academic conferences like EuroSys. It outlines the requirements for achieving multiple evaluation badges—Available, Functional, and Reproduced—by ensuring the artifact is self-contained, portable, and verifiable by an external evaluator without prior context.

This guide details the USENIX FAST artifact evaluation process, providing systematic advice for researchers submitting system papers. It explains the requirements for earning the three USENIX badges (Available, Functional, Reproduced). Key topics include managing hardware dependencies in storage systems, packaging best practices (e.g., Docker containers, raw logs, comprehensive READMEs), and ensuring the final artifact is maximally reproducible for external evaluation.

This guide establishes standards for reporting reproducibility in robotics research, addressing the difference between traditional ML and physical systems. It mandates that every claim must be classified as either 'rerunnable' (executable code/simulation) or 'auditable' (detailed logs, hardware specs, and protocols). It covers creating a complete evidence map, detailing the hardware specification ledger, explicitly reporting the sim-to-real gap, and writing an honest availability statement, all within the main paper body.