aaai-artifact-evaluati…
Use when packaging AAAI code, data, multimedia appendices, technical appendices, reproducibility evidence, and post-acceptance…
Use when building the reproducibility story for an ATC (ACM SIGOPS Annual Technical Conference, formerly USENIX ATC) systems paper — pinning testbed and software environments, providing a turnkey path from the artifact to the headline numbers, and preparing an
Claude Code
$ npx -y skills add brycewang-stanford/Awesome-Journal-Skills --skill atc-reproducibility --agent claude-code
Codex
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill atc-reproducibility -a codex -y
Cursor
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill atc-reproducibility -a cursor -y
Gemini CLI
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill atc-reproducibility -a gemini-cli -y
GitHub Copilot
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill atc-reproducibility -a github-copilot -y
OpenCode
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill atc-reproducibility -a opencode -y
Windsurf
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill atc-reproducibility -a windsurf -y
name: atc-reproducibility description: Use when building the reproducibility story for an ATC (ACM SIGOPS Annual Technical Conference, formerly USENIX ATC) systems paper — pinning testbed and software environments, providing a turnkey path from the artifact to the headline numbers, and preparing an anonymized-but-runnable review package ahead of the Available/Functional/Reproduced badges.
Build the reproducibility story alongside the system, not at the deadline. ATC has an active artifact culture inherited from USENIX: reviewers expect a runnable, anonymized artifact at review time, and after acceptance an Artifact Evaluation Committee awards **Available / Functional / Reproduced** badges (see `atc-artifact-evaluation`). The through-line is that a systems result other people can re-run is worth more than one they must take on faith — and systems provenance cannot be reconstructed after the fact.
Record these **at collection time**; none can be recovered at the deadline:
[Hardware] CPU/NIC/SSD models, core/memory counts, firmware/BIOS where it matters [OS/kernel] kernel version, distro, relevant sysctl/tuning, hugepages/NUMA settings [Toolchain] compiler, library, and runtime versions; build flags [Workload] trace source + extraction date, generator version + seeds, request mix [Method] warm-up window, measurement duration, run count, aggregation method [Code] commit SHAs for your system and every baseline; patches applied
The single most valuable artifact property is that an evaluator can regenerate your paper's main figures and tables:
runtime.
and plot.
and state clearly which results are full-scale-only and why.
measurement noise.
"install these 30 packages by hand."
so explicitly and document the required host, since a container cannot abstract the hardware away.
them.
At submission the artifact must be **runnable yet double-blind**:
configs, logs, or commit metadata.
identifying, and reconcile it in the camera-ready.
laptop" is the most common Functional failure.
provide the closest reproducible substitute — silence reads as a weakness.
reimplementation) and claim only what you support.
analysis scripts — even when the production system itself cannot ship.
[Provenance] hardware/OS/toolchain/workload/method/code pinned at collection time? gaps? [Turnkey] claim-to-experiment map + one-command runs + small-scale mode present? yes/no [Environment] container or pinned lockfile? hardware dependencies documented? [Anonymity] artifact runnable AND double-blind (no names/hosts/owner strings)? yes/no [Clean-machine] runs from a fresh checkout on a clean host? yes/no [Badge readiness] on track for Available / Functional / Reproduced? blockers?
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