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/atc-reproducibility

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

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awesome-journal-skills
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Install
$ npx -y skills add brycewang-stanford/Awesome-Journal-Skills --skill atc-reproducibility --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/atc-reproducibility

Context preview

The summary Claude sees to decide when to auto-load this skill.

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

SKILL.md

atc-reproducibility.SKILL.md
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.

ATC Reproducibility

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.

Pin what you cannot reconstruct

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

A turnkey path to the headline numbers

The single most valuable artifact property is that an evaluator can regenerate your paper's main figures and tables:

  • Ship a **claim-to-experiment map**: paper claim → script → expected figure/table → expected

runtime.

  • Provide a **one-command** entry point per headline result (`./run_fig3.sh`) that does setup, run,

and plot.

  • Give a **small-scale mode** for evaluators who lack your hardware (fewer nodes, a trace sample),

and state clearly which results are full-scale-only and why.

  • Log **expected outputs and tolerances** so an evaluator knows what "reproduced" looks like given

measurement noise.

Pinned, portable environments

  • Prefer a **container (Dockerfile) or a pinned environment** (lockfile, `requirements`, Nix) over

"install these 30 packages by hand."

  • Where the result depends on kernel features or hardware (RDMA, SPDK, io_uring, specific NICs), say

so explicitly and document the required host, since a container cannot abstract the hardware away.

  • Include **traces/datasets** (or documented, durable access), not just the query that produced

them.

Anonymized-but-runnable review package

At submission the artifact must be **runnable yet double-blind**:

  • No owner strings, cluster hostnames, lab or product names, or identity-revealing URLs in code,

configs, logs, or commit metadata.

  • Mirror any linked repository behind an **anonymizing service**; scrub `.git/` from archives.
  • The system's own **name** can de-anonymize you — use a neutral placeholder if the real name is

identifying, and reconcile it in the camera-ready.

  • Verify the package runs from a **clean checkout** on a fresh machine — "works on the author's

laptop" is the most common Functional failure.

Honest reproducibility posture

  • If a result cannot be shared (proprietary trace, confidential deployment), say so and why, and

provide the closest reproducible substitute — silence reads as a weakness.

  • Distinguish **reproducible** (same artifact, same numbers) from **replicable** (independent

reimplementation) and claim only what you support.

  • For experience/deployed-systems papers, provide what you can — configs, anonymized traces,

analysis scripts — even when the production system itself cannot ship.

Output format

[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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