Browse reusable Agent Skills, each with a clear purpose and practical guidance.
Use when pursuing the Graphics Replicability Stamp (GRSI) or Code Replicability in Computer Graphics (CRCG) recognition for an accepted SIGGRAPH / TOG paper, covering how graphics replicability differs from ACM artifact badging, what volunteers actually run, deterministic result reproduction, Software Heritage archiving, and the separate post-acceptance timing.
Use when building the reproducibility story for a SIGGRAPH / TOG paper, covering deterministic result regeneration, scene/mesh/weight provenance, hardware and timing disclosure, floating-point and GPU non-determinism, and a code/data release that a reader or a Graphics Replicability Stamp volunteer can actually run.
Use when packaging an ACM SIGMETRICS artifact for the ACM Artifact Review and Badging scheme (Artifacts Available, Evaluated Functional and Reusable, Results Reproduced), covering what performance-evaluation evaluators check first (does the simulation regenerate the figures and match the analysis?), DOI-issuing archives, evaluator-proof documentation, and confirming whether an artifact track runs this cycle.
Use when strengthening ACM SIGMETRICS reproducibility, covering proofs and their assumptions as reproducible artifacts, seeded simulators whose figures regenerate and match the analysis, measurement/trace provenance, claim-to-evidence mapping, honest degrees of reproducibility, and consistency between what the paper proves/measures and what the artifact contains.
Use when designing or auditing the evaluation of a SIGMOD paper, covering workload realism and standard benchmark usage, baseline tuning fairness, scalability and tail-latency methodology, ablations that isolate the mechanism, and the setup disclosure a data-systems PC demands before trusting any speedup.
Use when packaging an ACM SoCC artifact for the ACM Artifact Review and Badging scheme (Artifacts Available, Evaluated Functional and Reusable, Results Reproduced), covering what a cloud-systems evaluator checks first, reproducing tail-latency and cost results on a testbed, DOI-issuing archives, and the fact that whether SoCC runs a dedicated artifact-evaluation track for a given edition must be verified.
Use when designing or auditing ACM SoCC evaluations, covering real or realistic deployments over simulation, production or representative workloads and traces, tail-latency and cost as first-class metrics, fair tuned baselines, scale and multi-tenancy behavior, reproducible measurement pipelines, and matching evidence to the shape of each cloud claim.
Use when strengthening ACM SoCC reproducibility, covering the testbed and workload description, released code and traces, provenance pinning for measurement studies, reproducing tail-latency and cost (not just the mean), claim-to-evidence mapping, honest degrees of reproducibility, and consistency between what the paper reports and what the artifact regenerates.
Use when hardening the verifiability of a SODA (ACM-SIAM Symposium on Discrete Algorithms) paper, where reproducibility means checkable mathematics — complete proofs in the submitted full version, stable statement-proof correspondence, explicit constants and model assumptions, and certificates for any machine-checked step.
Use when designing or auditing the evaluation of a SOSP paper — mapping every claim to an experiment, choosing baselines a systems PC will accept as fair, mixing microbenchmarks with end-to-end and failure runs, reporting tails and overheads honestly, and isolating the mechanism the design credits.