Skill UI

rclone is a powerful, feature-rich command-line utility designed to manage files across diverse cloud storage backends, including S3-compatible services. It provides robust, reliable equivalents to core Unix commands (sync, copy, move, mount). Use this skill for all terminal-based cloud operations, such as syncing, backing up, downloading, or managing remote data securely.

KubeEye is a comprehensive cluster inspection tool designed for Kubernetes environments, particularly within KubeSphere. It systematically detects issues across workloads, nodes, and configurations by integrating multiple policy engines, including OPA/Rego, PromQL, file integrity checks, kernel parameter validation, and systemd health checks. Users can define custom inspection rules (InspectRule), schedule checks (InspectPlan), and retrieve detailed reports (InspectResult) to ensure cluster compliance and stability.

A structured presentation deck designed for rigorous review of safety incidents, security risks, and policy compliance. It utilizes distinct visual cues, including hazard stripes, L1/L2/L3 severity tiers, and dedicated code blocks for policy review (e.g., YAML). Ideal for red team exercises, post-incident reviews, and high-stakes technical briefings.

A structured workflow designed to prevent 'reinventing the wheel.' Before writing custom code, this skill guides the user through analyzing needs, conducting parallel searches across multiple channels (npm, PyPI, GitHub, MCP), evaluating candidate solutions based on maintainability and community support, and making a decision (Adopt, Extend, or Custom Build). It ensures that solutions leverage existing, proven libraries and patterns.

A comprehensive pre-submission tool designed to guide authors targeting ACS Nano. It assesses whether a nanoscience or nanotechnology manuscript meets the journal's high standards across scope, method rigor, structural presentation, and conceptual framing. Use this skill to re-frame your work, understand the required evidence (e.g., single-molecule studies, quantitative benchmarks), and ensure optimal fit before official submission.

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.

This guide provides expert principles for designing and auditing system-level experiments in computer architecture (e.g., for ASPLOS-level conferences). It details the proper usage of various instruments—from real silicon and FPGAs to cycle-level simulators and analytical models—ensuring that every claim is matched to a valid, stated error model. It emphasizes workload selection, establishing strong baselines, performing rigorous ablation studies, and honestly reporting measurement noise and limitations to maintain scientific credibility.

This guide outlines the rigorous standards for evaluating systems research, particularly for top-tier conferences like ATC. It provides detailed instructions on designing reproducible experiments, establishing fair baselines, utilizing realistic workloads, and reporting critical metrics such as tail latency, variance, and resource cost, ensuring the highest level of scientific rigor.

Provides guidelines for creating robust, verifiable, and reusable Electronic Design Automation (EDA) artifacts for submission to academic conferences like DAC. The focus shifts from chasing conference badges to ensuring community trust and scientific reproducibility. Key elements include pinning the toolchain, using standard benchmarks, providing clear setup instructions, and archiving the release in a DOI-issuing repository.

A comprehensive guide for establishing research reproducibility in Electronic Design Automation (EDA). It details best practices for pinning benchmark suites, disclosing specific tool versions (open/commercial), documenting technology nodes (PDKs), and handling stochastic data flows (seeds, variance). Essential for ensuring that semiconductor design claims and ML-for-EDA results are credible and verifiable by the academic community.

This guide assists in structuring the design of Environmental Science & Technology (ES&T) studies, including lab, mesocosm, field sampling, or modeling experiments. It ensures methodological rigor by planning for environmental relevance, necessary controls (e.g., abiotic loss, sorption), proper replication, mass/energy balance, and a comprehensive QA/QC plan from the outset, helping the study withstand expert peer review.

This guide provides comprehensive standards for designing, executing, and reporting systems experiments for top-tier academic venues (like ICDE). It emphasizes methodological rigor, including mandatory ablation studies, reporting variance and tail latency (p99), demonstrating scaling trends (curves over points), disclosing cost trade-offs, and ensuring full hardware and software reproducibility to withstand adversarial review.