Skill UI

This guide outlines advanced methodologies for achieving high reproducibility in networked-systems research. It details capturing crucial provenance ledgers—including topology, traffic traces, system configuration, and run details—to ensure that published results are verifiable and not dependent on ephemeral experimental conditions. It emphasizes characterizing inherent variance rather than demanding absolute determinism, providing a rigorous framework for academic publication.

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 details the process of achieving ACM badges (Available, Functional, Reproduced) for academic artifacts at SenSys. It emphasizes creating a hardware-optional evaluation path, allowing reviewers to verify results and functionality without access to the original testbed. Key steps include implementing graded replay tiers, documenting data provenance (energy, traces), and performing smoke-runs.

A comprehensive guide for submitting networking research artifacts for SIGCOMM conferences. It dictates how to package code, downscale topologies, substitute traces, and pin environments so that reviewers can independently verify and reproduce the paper's headline results, establishing verifiable scientific claims.

A specialized skill for analyzing the discrepancy between static Kubernetes/Compose manifests and the actual live operational state of a container runtime. It traces complex dependencies, such as secrets consumption, sidecar/init container modifications, network routing, and mounted volume paths. Use this when the core challenge involves proving how the live execution path differs from the documented intent, forming a minimal, verifiable evidence chain.

This specialized skill is designed for deep forensic analysis in complex Windows environments, crucial for CTF and red teaming. It traces identity flows, including Active Directory (AD) origins, Kerberos ticket lifecycles, and OAuth authorization claims. Use it to correlate host artifacts (LSA, DPAPI, Sysmon) with identity evidence to map complete lateral movement and privilege escalation chains.