/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
$ npx -y skills add brycewang-stanford/Awesome-Journal-Skills --skill atc-reproducibility --agent claude-codeHow 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.mdname: 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?
Read more
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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Other skills on awesome-journal-skills.
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Open skill - /aaai-camera-ready
Use when preparing an accepted AAAI paper for camera-ready source submission to AAAI Press, including proceedings page limits, two-column template compliance, copyright transfer, purchased extra technical pages, deanonymization, registration, oral or poster presentation, and
Open skill - /aaai-experiments
Use when designing or auditing AAAI experiments for the broad-AI program committee, including baselines, ablations, statistical significance, robustness, human evaluation, AI-for-Social-Impact and alignment/safety evidence, compute and cost reporting, and
Open skill - /aaai-related-work
Use when positioning an AAAI paper's novelty against archival work, contemporaneous arXiv or workshop papers, and AAAI/IJCAI/NeurIPS/ICML/ICLR neighbors across the broad AI scope, while staying inside AAAI's dual-submission and AI-as-source policy constraints and writing a
Open skill - /aaai-reproducibility
Use when strengthening an AAAI paper's reproducibility checklist (placed after references), experimental traceability, seed and hyperparameter reporting, compute and cost disclosure, dataset access and licensing, code/data ZIP readiness, and the claim-to-evidence map that
Open skill

