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atc-artifact-evaluation

Use when packaging an ATC (ACM SIGOPS Annual Technical Conference, formerly USENIX ATC) artifact for the USENIX-lineage evaluation scheme — earning the Artifacts Available, Artifacts Functional, and Results Reproduced badges from the Artifact Evaluation Committee on its separate post-acceptance dead…

Install / Use

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

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

87/100

Supported Platforms

Universal

Our assessment of atc-artifact-evaluation

atc-artifact-evaluation scores 87/100 on our quality scale, 644th of 1,186 Content & Media skills we index.

Its SKILL.md is 5.4 KB long, well organised into 8 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
18/20
Description
15/15
Adoption
13/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated 21 days ago, so atc-artifact-evaluation 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-artifact-evaluation compared with similar skills

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

SkillScoreStarsUpdatedFormat
atc-artifact-evaluation (this skill)by brycewang-stanford871.2k21d agoSKILL.md
siyuanby siyuan-note10046.6ktodayMCP Server
algorithmic-artby anthropics100177.9k13d agoSKILL.md
pptxby anthropics100177.9k13d agoSKILL.md
designby nextlevelbuilder100130.2k15d agoSKILL.md

Frequently asked questions

How do I install atc-artifact-evaluation?
Run npx skills add brycewang-stanford/Awesome-Journal-Skills --skill atc-artifact-evaluation. The install tabs above show the steps for each supported agent.
Which AI agents does atc-artifact-evaluation work with?
It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
Is atc-artifact-evaluation 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-artifact-evaluation still maintained?
The repository was last updated 21 days ago, so atc-artifact-evaluation is actively maintained.

name: atc-artifact-evaluation description: Use when packaging an ATC (ACM SIGOPS Annual Technical Conference, formerly USENIX ATC) artifact for the USENIX-lineage evaluation scheme — earning the Artifacts Available, Artifacts Functional, and Results Reproduced badges from the Artifact Evaluation Committee on its separate post-acceptance deadline, with evaluator-proof documentation and a turnkey path to the paper's numbers.

ATC Artifact Evaluation

Use this for the artifact track. ATC follows the shared USENIX/SIGOPS artifact-evaluation scheme: an Artifact Evaluation Committee (AEC) runs a separate, post-acceptance process through HotCRP and awards up to three badges — Artifacts Available, Artifacts Functional, and Results Reproduced. Two things to internalize: badges are earned by evaluators actually using your package, and the review artifact (anonymized, for the paper's reviewers) is not the same deliverable as the badge artifact (de-anonymized, permanently archived). ATC's artifact culture is strong and high-participation — for USENIX ATC '25, most evaluated artifacts earned Functional and a large majority earned Reproduced.

The three badges (verify the current set and names)

| Badge | What it certifies | What earns it | |---|---|---| | Artifacts Available | The artifact is permanently, publicly retrievable | Deposit in a DOI-issuing archive (Zenodo, figshare, Software Heritage) | | Artifacts Functional | It is documented, complete, and exercisable — it runs and does what the paper says | Clean-machine install, a demo, scripts + data to run the experiments | | Results Reproduced | An evaluator reproduced the paper's main results | A turnkey path from the artifact to the headline figures/tables |

Available is low-cost, high-value (archive the package). Functional and Reproduced require the evaluator's own run to succeed, so the failure mode is always "did not run on their machine," never "the idea was weak."

What AEC evaluators open first

| Claim type | First thing inspected | Common failure caught | |---|---|---| | A built system / mechanism | The README and one install/run command | Undocumented deps; only-works-on-authors'-hardware | | A performance result | The scripts that turn a run into the paper's figures | Numbers in the PDF no script reproduces | | A result needing specific hardware (RDMA/NVMe/NIC) | The documented host requirements + a small-scale mode | Requires unavailable hardware; no fallback | | A trace-driven study | The traces + the processing scripts | Query shipped, data missing; provenance unpinned |

Assume the evaluator has a bounded time budget on a clean machine and may lack your exact hardware. Design the first ten minutes to succeed and provide a reduced-scale path.

Packaging plan

[Container]   a Dockerfile or pinned environment; document any host/hardware the container cannot abstract
[README]      one-screen orientation: what it is, install, run the demo, reproduce each claim, expected runtime/outputs
[Mapping]     an explicit table: paper claim -> script -> expected figure/table -> tolerance
[Scale]       a full-scale path AND a small-scale mode for evaluators without the hardware
[Data]        the traces/datasets themselves (or durable documented access), not just the query
[Provenance]  kernel/OS versions, hardware models, commit SHAs, workload seeds, run counts
[License]     an OSI-approved license so the artifact can be shared and reused
[Archive]     deposit in a DOI-issuing repository for the Available badge

Anonymized review artifact vs. badge artifact

  • At submission (review): anonymized for the paper's double-blind reviewers — no owner strings, cluster hostnames, lab/product names, or identity-revealing links, and no live repo that discloses authors (see atc-reproducibility).
  • After acceptance (badge): replace anonymized placeholders with the public, licensed, DOI-issuing archive; this is the version the AEC badges and the camera-ready cites.

Worked vignette: a storage-cache policy + measurement

A paper contributes a flash-cache admission policy and a testbed evaluation. To target Functional and Reproduced: ship a container with the modified server pre-built; a run_demo.sh that exercises the policy on a bundled trace sample in a minute; a reproduce/ directory whose scripts regenerate each figure from logged runs; a claim-to-script mapping with tolerances in the README; the production-derived trace sample (with pinned provenance) plus documentation of the full trace's access; a documented note that the full-scale p99 result needs the 12-node testbed, with a single-node small-scale mode; and an MIT/Apache license. State honestly which results are full-scale-only.

Calibration

  • The AEC deadline is after acceptance and independent of the camera-ready — do not conflate them (plan both in atc-workflow).
  • Badge names, the exact set offered, and whether evaluation is single- or double-anonymous can vary by cycle and moved in the SIGOPS transition — confirm on the current call for artifacts.

Output format

[Target badges] Available / Functional / Reproduced
[Artifact role] anonymized review artifact / public badge artifact
[Contents] <system/data/scripts/provenance/license/archive>
[Ten-minute test] install + demo succeed on a clean machine? yes/no
[Reproduce] claim -> script -> expected figure present? small-scale mode for missing hardware? yes/no
[Fixes before upload] <ordered list>

Related Skills

View on GitHub
GitHub Stars1.2k
CategoryContent
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