Skill UI

A comprehensive simulation tool designed to pressure-test medical device manufacturers' Quality System Regulation (QSR) compliance against FDA standards (21 CFR 820). It covers six critical areas: complaint handling, process validation, device history records (DHRs), CAPAs, labeling, and handling of Form 483 observations. Essential for pre-inspection readiness, internal audits, and regulatory submission planning.

Designed for complex troubleshooting in home or small-team environments. This meta-skill orchestrates the entire process, guiding users through symptom intake, environment capture, external knowledge lookup, and generating a safe, step-by-step, and reversible repair plan for issues involving networks, Docker, Git, printers, and more.

This module is designed to document, track, and manage quality nonconformance issues found during manufacturing or process audits. It is essential for root cause analysis, implementing corrective and preventive actions (CAPA), and ensuring compliance with industry quality standards. Use it when recording deviations from specified requirements.

A comprehensive guide for researchers in electronics and signal processing preparing submissions to Acta Electronica Sinica (AES). It outlines best practices for proactively structuring, documenting, and disclosing research artifacts—including code, datasets, and hardware designs—to maximize the paper's credibility and chances of acceptance, even without mandatory journal badges. Covers anonymization, reproducibility standards, and proper disclosure in the manuscript.

A detailed model of the ACM/IEEE Design Automation Conference (DAC) research manuscript review process. This guide explains the double-blind, TPC-driven, and single-shot nature of the evaluation, emphasizing the critical balance between genuine novelty and measured Quality-of-Results (QoR). It outlines the high standards for evidence fairness, benchmark credibility, and ablation studies required for acceptance, differentiating DAC's approach from architecture and software venues.

A comprehensive guide for designing and auditing empirical experiments in signal processing, audio, and speech domains. It emphasizes matching standard metrics (e.g., WER, SI-SDR, PSNR) to specific tasks, establishing current strong baselines, and systematically sweeping operating conditions (like SNR or reverberation time) to ensure robust, reliable, and publication-ready results.

A comprehensive guide for designing and auditing rigorous experiments for sensing systems and information processing claims. It emphasizes the necessity of measuring performance on real hardware (not simulation), accurately instrumenting ground truth, quantifying energy consumption, latency, and physical footprint, and establishing fair, skeptical baselines. This ensures scientific rigor and reproducibility in research submissions.

A comprehensive guide for authors preparing artifacts (code, data, models, etc.) for submission to the Journal of Computer-Aided Design & Computer Graphics (JCAD&CG). It details best practices for ensuring research reproducibility, writing honest availability statements, and managing artifacts during the double-blind review process, focusing on ethical standards regardless of specific journal policy.

A comprehensive guide for submitting research artifacts to the MobiSys Artifact Evaluation Committee (AEC). It details the process for achieving three independent ACM badges (Available, Functional, Results Reproduced). Emphasis is placed on creating fully reproducible, script-first workflows, designing for hardware-optional environments, and maintaining strict anonymity during review.

This comprehensive guide outlines best practices for designing rigorous and convincing technical evaluations for networked systems. It structures the process by requiring experiments to answer specific questions, advising authors to climb the evidence ladder from synthetic benchmarks to real-world deployment. Key concepts include reporting tail latencies (p99), establishing strong baselines, and maintaining meticulous provenance records to ensure reproducibility.

Design custom laboratory hardware components using parametric Python models with build123d. Export fabrication-ready files like STEP, STL, and DXF for microfluidic chips, mounts, and adapters. Ensure precise interface dimensions against standards for compatibility with lab equipment. Use for physical parts mating with standardized labware, optical tables, or manufacturing processes.