Skill UI

A rigorous, multi-phase auditing skill designed for blockchain protocols. It verifies whether the implemented smart contract code strictly adheres to the detailed specifications provided in whitepapers, design documents, or technical specifications. It performs exhaustive, line-by-line semantic analysis to identify gaps, inconsistencies, and deviations across logic, invariants, and security guarantees, making it essential for formal compliance checking in decentralized systems.

This skill provides a comprehensive guide to deploying a Software-Defined Perimeter (SDP) compliant with the CSA v2.0 specification. It details implementing core zero trust mechanisms, including Single Packet Authorization (SPA) and mutual TLS (mTLS), using a controller and gateway architecture. Use this when establishing invisible, identity-centric network access (dark cloud) to harden infrastructure, enforce strict zero trust policies, or meet advanced compliance requirements.

This tool discovers and inventories 'shadow APIs'—endpoints that operate outside documented specifications (OpenAPI/Swagger). It utilizes advanced traffic analysis against API gateways (e.g., Kong, AWS API Gateway), cloud configuration scanning, and source code mining. Essential for assessing the complete API attack surface, auditing forgotten services, or establishing robust API governance policies.

A comprehensive guide for generating highly detailed and structured embodiment descriptions for patent specifications. It outlines the necessary workflow for describing how an invention is made and used (method, system, apparatus), ensuring the patent meets enablement requirements while strictly differentiating technical descriptions from empirical experimental results.

This skill performs a rigorous, adversarial review of implemented code. It verifies that the task implementation is complete, structurally sound, respects defined boundaries, and aligns perfectly with approved requirements and design specifications. Use it after an implementer finishes work, or before final acceptance, to ensure code quality and architectural integrity.

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.

This guide details how to prepare the mandatory video attachment for robotics conference submissions. It covers strict technical specifications (e.g., 180 seconds, 20 MB), the crucial self-containment rule, effective storyboarding techniques, and necessary steps for anonymization. Learn to transform your video from a mere demonstration into robust, undeniable scientific evidence.

A comprehensive guide for authors submitting papers to the IROS conference. It details critical formatting rules, including page limits (references counted in), double-anonymous requirements, video specifications (60s/10MB), and procedural traps (PIN collection, compliance testing). Essential for ensuring submission acceptance in major robotics venues.

Provides a rigorous framework for ensuring the reproducibility of systems research presented at conferences like OSDI. It mandates maintaining a comprehensive, machine-readable provenance ledger that records hardware specifications, software dependencies, workload traces, and measurement policies throughout the entire experimental lifecycle, preventing 'artifact drift' and ensuring verifiable results.

This guide outlines the necessary steps to ensure that parallel computing performance papers are scientifically reproducible. Since parallel results depend heavily on machine architecture, topology, and runtime conditions, proper documentation must cover everything from CPU/GPU specifications and interconnects to thread pinning policies, random seeds, and compiler flags. The focus is on proving the scaling trend, not just the absolute numbers, allowing results to hold true across different hardware platforms.

A comprehensive guide for authors in data systems and related fields on achieving rigorous, verifiable reproducibility for academic papers. It mandates detailing the provenance of every claimed result by pinning five critical layers: code version, configuration parameters, dataset source, workload characteristics, and hardware specifications. Best practices include reporting distributions (e.g., percentiles, variance) and maintaining a detailed 'repro debt ledger' to withstand intense peer review.