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-reproducibilityInstalls into whichever agent you are using.
SKILL.md
Installable skill definition
Quality Score
Category
Development & EngineeringSupported Platforms
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.
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 foundOur 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.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| atc-reproducibility (this skill)by brycewang-stanford | 85 | 1.2k | 21d ago | SKILL.md |
| ai-job-searchby MadsLorentzen | 100 | 45.1k | today | CLAUDE.md |
| claude-howtoby luongnv89 | 100 | 41.8k | 6d ago | CLAUDE.md |
| algorithmic-artby anthropics | 100 | 177.9k | 13d ago | SKILL.md |
| pptxby anthropics | 100 | 177.9k | 13d ago | SKILL.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.
Skill content
View source on GitHubname: 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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Trust signals
From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.
