SkillAgentSearch skills...

ase-artifact-evaluation

Use when preparing an accepted ASE (IEEE/ACM Automated Software Engineering) paper's tool and data for the Artifact Evaluation track, targeting the ACM Artifacts Available and Artifacts Reusable badges on the track's own deadline, with the badge shown on the paper's front page in both IEEE Xplore an…

Install / Use

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

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

85/100

Category

Automation

Supported Platforms

Universal

Our assessment of ase-artifact-evaluation

ase-artifact-evaluation scores 85/100 on our quality scale, 1913th of 2,889 Automation skills we index.

Its SKILL.md is 4.2 KB long, split into 7 sections with 3 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 ase-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.

ase-artifact-evaluation compared with similar skills

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

SkillScoreStarsUpdatedFormat
ase-artifact-evaluation (this skill)by brycewang-stanford851.2k21d agoSKILL.md
Agent-Reachby Panniantong10092.1k20d agoCLAUDE.md
Scraplingby D4Vinci10085.9ktodayMCP Server
rufloby ruvnet10074.0ktodayMCP Server
algorithmic-artby anthropics100177.9k13d agoSKILL.md

Frequently asked questions

How do I install ase-artifact-evaluation?
Run npx skills add brycewang-stanford/Awesome-Journal-Skills --skill ase-artifact-evaluation. The install tabs above show the steps for each supported agent.
Which AI agents does ase-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 ase-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 ase-artifact-evaluation still maintained?
The repository was last updated 21 days ago, so ase-artifact-evaluation is actively maintained.

name: ase-artifact-evaluation description: Use when preparing an accepted ASE (IEEE/ACM Automated Software Engineering) paper's tool and data for the Artifact Evaluation track, targeting the ACM Artifacts Available and Artifacts Reusable badges on the track's own deadline, with the badge shown on the paper's front page in both IEEE Xplore and the ACM Digital Library.

ASE Artifact Evaluation

Convert the accepted paper's package into badges. ASE runs an Artifact Evaluation track offering the Artifacts Available and Artifacts Reusable badges (ACM scheme). Because ASE proceedings are indexed in both IEEE Xplore and the ACM Digital Library, an earned badge appears on the paper's front page in both. Evaluation happens on the track's own deadline, separate from the research-track notification — stage the package before then.

The two badges (verify the current call)

  • Artifacts Available — the artifact is placed in a publicly accessible archival repository with a DOI (Zenodo, figshare, Software Heritage, or an institutional/ACM repository). A personal GitHub link alone is not archival; mint a DOI.
  • Artifacts Reusable — the artifact significantly exceeds minimal functionality: it is carefully documented and well-structured so a third party can reuse the tool, not merely reproduce your tables. This is the higher bar and where automated-SE tools usually need the most work.
  • Whether Functional and Results Reproduced badges are also offered at a given edition is 待核实 — confirm on the current Artifact Evaluation call.

From the submission artifact to the badge artifact

The review-time (anonymized) artifact and the badge artifact are the same package matured. After acceptance you can de-anonymize it, but the substance should already be there if you followed ase-reproducibility.

[De-anonymize]  restore the real tool name, authors, repository, license.
[Archive]       deposit in a DOI-issuing archive; the DOI is what "Available" certifies.
[Document]      README with exact run path, expected outputs, and a small worked example.
[Environment]   container/lockfile pinning deps + the exact tool commit; note hardware needs.
[Reuse story]   show how to run the tool on a NEW input, not just replay your experiments.

Reusable is about strangers, not your tables

Evaluators judge reusability, so write for someone who wants to use your automation on their own code:

  • A clear entry point and documented inputs/outputs.
  • Instructions to run on a new subject, with a template config.
  • Sensible structure (source vs. data vs. scripts), an open license, and dependency pinning.
  • Removal of dead scripts, secrets, and machine-specific paths.

Evaluator-proofing checklist

[Runs clean]   fresh environment (container) -> documented command -> expected output, no manual patching
[DOI]          archival deposit with a DOI + open license (for Available)
[Docs]         README covers install, run, expected results, and reuse on a new input (for Reusable)
[Provenance]   subject SHAs, dataset version, seeds, model IDs/dates + cached outputs included
[Scope honesty] hardware/time requirements and known limitations stated up front
[No secrets]   API keys, tokens, private paths removed

Timing and scope

  • The Artifact Evaluation deadline follows research-track acceptance; treat it as a real milestone, not an afterthought — a strong tool with a weak package earns no badge.
  • Evaluators are often students and junior researchers on a schedule: an artifact that needs a live API key, unpinned dependencies, or your specific cluster will fail on setup regardless of the underlying quality.
  • Badges are recognition, not re-review of the science; the paper is already accepted. The goal is durable, reusable automation.

Output format

[Target badges] Available / Reusable (Functional/Reproduced 待核实 for this edition)
[Archive] DOI minted? open license?
[Runs clean] fresh-env command -> expected output, no manual fixes?
[Reusable] docs + run-on-new-input path present?
[Provenance] SHAs / dataset version / seeds / model IDs / cached outputs bundled?
[Blockers] <ordered fixes before the AE deadline>

Related Skills

View on GitHub
GitHub Stars1.2k
CategoryAutomation
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
ase-artifact-evaluation — Universal Skill: Install & Safety Check | SkillAgent