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Mobisys artifact evaluation

Skill brycewang-stanford/Awesome-Journal-Skills/MobiSys-Skills/skills/mobisys-artifact-evaluation

Use when packaging a MobiSys artifact for the Artifact Evaluation Committee — choosing among the three independent ACM badges (Available, Evaluated–Functional, Results Reproduced), building the workflow scripts the single-blind AEC runs on its own machines, and providing a hardware-optional path for evaluators who lack the exact phone, wearable, or board.From its SKILL.md

Install
npx -y skills add brycewang-stanford/Awesome-Journal-Skills --skill mobisys-artifact-evaluation

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

SKILL.md

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MobiSys Artifact Evaluation

Use this for the MobiSys artifact submission. MobiSys has a strong, script-first artifact culture: the single-blind AEC runs the authors' workflow scripts on the reviewers' own systems, so an artifact that only works on your bench is not an artifact.

The three ACM badges

Authors may seek one, two, or all three independent badges; passing badges are printed on the paper and recorded in the ACM DL:

BadgeWhat it certifiesWhat the AEC does
Artifacts AvailableThe artifact is publicly archived at a stable locationChecks the archival link and license
Artifacts Evaluated — FunctionalThe artifact runs and does what the paper saysRuns your workflow scripts on the AEC's machines
Results ReproducedKey paper results are reproduced independentlyRegenerates headline numbers/figures from your package

Choose badges as claim calibration: Available is a release commitment; Functional needs a runnable package; Results Reproduced needs the headline device results to come back on machines you do not control — the hardest badge for on-device work.

Artifact plan

  • Decide what evidence supports each badge: source, build scripts, model files, workload traces, and the measurement/plotting pipeline.
  • Provide a top-level workflow script and a README that orients an evaluator in one minute: environment, dependencies, one command to smoke-test, one to reproduce a headline figure, expected outputs, and runtime.
  • Anonymize repository history, paths, screenshots, notebook metadata, license headers, and device serial numbers for the review round; de-anonymize only at camera-ready.
  • Pin framework, runtime, and model checkpoint hashes; on-device results drift across framework minor versions.

The hardware-optional path (the MobiSys-specific move)

On-device artifacts fail Functional most often because the evaluator lacks the exact phone, wearable, or board. Design for that from the start:

Hardware-optional package layout:
  /device      full pipeline for the exact hardware (Results Reproduced target)
  /emulated    downscaled or emulator path an evaluator can run without the device
  /logs        recorded runs so plots regenerate even if nothing executes
  /README      states which claims each path can and cannot reproduce

State plainly which headline numbers the emulated path cannot reproduce (typically absolute energy and thermal behavior) so the AEC grades the right badge instead of failing the whole package.

What AEC reviewers open first

  • The README and the top-level workflow script — if these do not run cleanly, the badge stalls regardless of how good the system is.
  • The one-command smoke test — make it fast and hermetic.
  • The plotting pipeline for a headline figure — emit figures directly from logged results so the paper numbers and artifact numbers cannot drift.

Calibration anchors

  • Artifact evaluation is single-blind and separate from paper review; the badge names and the artifact deadline are cycle-specific — verify against the current Artifact Evaluation page rather than past years.
  • Absolute on-device energy and latency are the least portable results; scope Results Reproduced claims to what survives on another evaluator's hardware.

Output format

[Badges sought] Available / Functional / Results Reproduced
[Package contents] <source/scripts/models/traces/plotting>
[Hardware-optional path] present? which claims it cannot reproduce
[Anonymity risks] <paths/screenshots/serials/repo owners>
[Reproduction level] turnkey / scripted / descriptive / weak
[Fixes before upload] <ordered list>

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

Keep looking

Skills are one crate of 325,949. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.