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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 anonymized-but-runnable review package…

Install / Use

npx skills add brycewang-stanford/Awesome-Journal-Skills --skill atc-reproducibility

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

85/100

Supported Platforms

Zed

Our assessment of atc-reproducibility

atc-reproducibility scores 85/100 on our quality scale, 2613th of 4,607 Development & Engineering skills we index.

Its SKILL.md is 4.3 KB long, split into 7 sections with 2 code examples: a solid amount of guidance for an agent.

With 1,158 GitHub stars, it is one of the more widely adopted skills in the catalogue.

Substance
26/30
Structure
16/20
Description
15/15
Adoption
13/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated 21 days ago, so atc-reproducibility is actively maintained.
  • It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
  • Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

Safety scan

No issues found

Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands.

Automated pattern scan on 2026-10-06. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.

atc-reproducibility compared with similar skills

All 4 of these similar skills score higher than atc-reproducibility; compare them before choosing.

SkillScoreStarsUpdatedFormat
atc-reproducibility (this skill)by brycewang-stanford851.2k21d agoSKILL.md
ai-job-searchby MadsLorentzen10045.1ktodayCLAUDE.md
claude-howtoby luongnv8910041.8k6d agoCLAUDE.md
algorithmic-artby anthropics100177.9k13d agoSKILL.md
pptxby anthropics100177.9k13d agoSKILL.md

Frequently asked questions

How do I install atc-reproducibility?
Run npx skills add brycewang-stanford/Awesome-Journal-Skills --skill atc-reproducibility. The install tabs above show the steps for each supported agent.
Which AI agents does atc-reproducibility work with?
It is written for Zed, as a SKILL.md file. Other agents that read the same format can often use it too.
Is atc-reproducibility safe to use?
Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. It is MIT-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is atc-reproducibility still maintained?
The repository was last updated 21 days ago, so atc-reproducibility is actively maintained.

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?

Related Skills

View on GitHub
GitHub Stars1.2k
CategoryDevelopment
Updated21d ago
Forks153

Languages

Stata

Trust signals

100/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

No cautions