ase-reproducibility
Use when building the open-science and reproducibility story for an ASE (IEEE/ACM Automated Software Engineering) submission, covering the mandatory Data Availability Statement, anonymized-but-runnable tools, tool and subject-system provenance pinning, cached LLM outputs, and staging for the ACM Ava…
Install / Use
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill ase-reproducibilityInstalls into whichever agent you are using.
SKILL.md
Installable skill definition
Quality Score
Category
AutomationSupported Platforms
Tags
Our assessment of ase-reproducibility
ase-reproducibility scores 83/100 on our quality scale, 2260th of 2,889 Automation skills we index.
Its SKILL.md is 4.2 KB long, split into 7 sections with 1 code example: 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 ase-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.
ase-reproducibility compared with similar skills
All 4 of these similar skills score higher than ase-reproducibility; compare them before choosing.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| ase-reproducibility (this skill)by brycewang-stanford | 83 | 1.2k | 21d ago | SKILL.md |
| Agent-Reachby Panniantong | 100 | 92.1k | 20d ago | CLAUDE.md |
| Scraplingby D4Vinci | 100 | 85.9k | today | MCP Server |
| rufloby ruvnet | 100 | 74.0k | today | MCP Server |
| algorithmic-artby anthropics | 100 | 177.9k | 13d ago | SKILL.md |
Frequently asked questions
- How do I install ase-reproducibility?
- Run
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill ase-reproducibility. The install tabs above show the steps for each supported agent. - Which AI agents does ase-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 ase-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 ase-reproducibility still maintained?
- The repository was last updated 21 days ago, so ase-reproducibility is actively maintained.
Skill content
View source on GitHubname: ase-reproducibility description: Use when building the open-science and reproducibility story for an ASE (IEEE/ACM Automated Software Engineering) submission, covering the mandatory Data Availability Statement, anonymized-but-runnable tools, tool and subject-system provenance pinning, cached LLM outputs, and staging for the ACM Available/Reusable artifact badges.
ASE Reproducibility
Build the reproducibility story at data-collection time, not at submission. ASE requires a mandatory Data Availability Statement in the paper and expects an anonymized, runnable artifact at review time; automated-SE artifacts are usually tools, so "runnable" means a reviewer can actually execute the automation on stated subjects. What is not pinned when you collect it cannot be reconstructed later.
The mandatory Data Availability Statement
- Required, placed after the Conclusions and inside the 10-page limit (it is not free appendix space).
- State what exists — the tool, the dataset, the subject systems, the scripts, the logs — and where it will live after acceptance (an archival DOI target).
- Provide an anonymized link or upload now; "available upon request" reads as a scored weakness, not a neutral placeholder.
- Match the statement to what the archive actually contains — an overclaiming statement is worse than a modest, honest one.
Anonymized-but-runnable tools
- Re-host the tool and dataset behind an anonymizing service; strip repository owner, commit
author metadata, and any path revealing your identity (
/home/<you>/, institutional URLs). - Include a minimal run path: exact commands, expected inputs, and a small sample so a reviewer can execute the automation without your machine.
- Pin the environment: dependencies with versions, a container or lockfile, and the exact tool commit — automated-SE tools rot fast against moving toolchains.
Provenance pinning (do this at collection time)
For the tool:
- Exact commit SHA, build instructions, dependency versions, and configuration/flags used in the experiments (including seeds for randomized components).
For subject systems and datasets:
- Names, versions, and SHAs of every subject; the corpus extraction date; query/filter criteria; and any manual labeling protocol with inter-rater agreement.
- A regeneration script and a versioned snapshot — live scraping re-samples a moving target.
For LLM-based components:
- Model identifiers and dates, prompts, decoding settings, and cached raw outputs so the artifact reproduces rather than calls a live, drifting API.
Reproducibility failure modes (ASE-specific)
| Failure | Consequence | Prevention | |---|---|---| | Tool needs your exact machine | Reviewers cannot run it; artifact fails | Container/lockfile + minimal run path | | Subjects unpinned (branch, not SHA) | Numbers cannot be reproduced | Record SHAs + extraction date at collection | | LLM outputs uncached | Re-runs drift; comparison invalid | Cache outputs; record model IDs/dates | | Data Availability outside the 10 pages | Policy violation | Place it after Conclusions, inside the budget | | Identity leak in artifact | Anonymity violation | Scrub owner/metadata; re-host anonymized |
From submission to the ACM badges
The submission-time artifact and the post-acceptance badge artifact are the same package matured.
ASE offers Artifacts Available and Artifacts Reusable badges (ACM scheme); staging for them
now avoids a scramble later (see ase-artifact-evaluation):
- Available — deposit in a DOI-issuing archive (Zenodo / figshare / Software Heritage) with an open license.
- Reusable — documentation, a clear run path, and structure that lets a stranger reuse the tool beyond reproducing your tables.
Output format
[Data Availability] present, after Conclusions, inside 10pp? matches the archive?
[Tool] commit pinned, deps versioned, container/lockfile, minimal run path?
[Subjects/data] SHAs + extraction date + selection/labeling protocol recorded?
[LLM] model IDs/dates, prompts, cached outputs?
[Anonymity] owner/metadata scrubbed; anonymized re-host?
[Badge readiness] Available (DOI+license) / Reusable (docs+run path) staged?
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Languages
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.
