Skill UI

This skill assists in generating robust Behavior Driven Development (BDD) tests. It supports writing feature files using Gherkin syntax (Given/When/Then) and automatically generates corresponding step definitions in multiple languages, including Java, JavaScript, and Ruby. Ideal for structuring and automating end-to-end functional tests.

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.

Monitors the acceleration trend of production spending (burn rate) over defined time windows. It compares the latest spending bucket against the historical average, providing an early warning system. This is crucial for detecting sudden, unexpected cost spikes, such as a 'hot loop,' independent of overall budget limits.

This skill consumes federation_spend events to aggregate per-peer spending across rolling time windows (1h, 24h, 7d). It implements a crucial circuit breaker mechanism by monitoring if any peer's spending exceeds a predefined threshold, ensuring the stability and resource management of the federation. Used to establish spending baselines and verify budget constraints.

A comprehensive guide for drafting the Related Work section for top-tier networking conferences (e.g., CoNEXT, SIGCOMM). It emphasizes moving beyond simple literature summaries to create a 'delta argument'—clearly defining what the paper contributes over the closest prior work. Guidelines cover covering specific networking 'lanes' (like IMC, NSDI, SIGMETRICS), maintaining double-anonymous citations, and ensuring the contribution is concrete and testable.

A detailed guide on how to structure and position the 'Related Work' section for high-stakes academic conferences like DAC, ICCAD, and DATE. It teaches authors to move beyond simple surveys and instead create a 'delta statement,' explicitly defining the limitations of prior art and quantifying the unique advances of the current work in terms of Quality of Results (QoR).

A comprehensive guide for researchers preparing academic artifacts for voluntary, post-acceptance evaluation (e.g., HPCA). It outlines best practices for structuring submissions for cold-start evaluators, defining reproducibility tiers (full reproduction, regeneration from logs, inspection), and managing computational budgets to successfully earn IEEE reproducibility badges.

A comprehensive guide for auditing and improving the scientific rigor of hardware architectural evaluations (e.g., HPCA style). It emphasizes establishing a clear 'fidelity contract' defining tools, scope, and configurations. It requires that all performance claims are backed by credible instruments, mandates capturing full machine state provenance, ensuring honest baseline tuning, and reporting results via per-workload distributions rather than single means.

A comprehensive guide for authors submitting to major signal processing conferences like ICASSP. It details how to audit, define, and precisely position a paper's novelty by comparing it against specific literature lanes (SPS journals, conferences, and ML venues). Learn to articulate the technical delta against the nearest work in a constrained, four-page format.

This guide outlines the rigorous standards for achieving robust reproducibility in robotics research papers, especially those involving physical hardware. It details a two-tier approach, defining results as either fully *rerunnable* (code/simulation) or fully *auditable* (hardware logs/protocols). Key requirements include creating comprehensive hardware specification ledgers, logging detailed sensor data (rosbags), separating deterministic from stochastic claims, and structuring the entire repository for peer review.

A comprehensive guide for designing, auditing, and reporting reliable robotic experiments, particularly for major conferences like IROS. It moves beyond simple success rates, detailing how to map every claim to verifiable evidence (trials, simulations, field data). Key areas covered include defining pre-set success criteria, reporting failure taxonomies, ensuring fair baselines, and quantifying the critical sim-to-real gap using advanced small-n statistical methods.

This guide provides a comprehensive framework for positioning robotics research papers for top-tier conferences like IROS, ICRA, and CoRL. It teaches authors how to audit novelty by comparing their work against multiple related systems—not just the nearest component—and how to handle archival overlap, concurrent work, and anonymity requirements in double-blind review processes. It emphasizes defining the system contribution across multiple robotics lanes.