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/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

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awesome-journal-skills
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$ npx -y skills add brycewang-stanford/Awesome-Journal-Skills --skill ase-reproducibility --agent claude-code

How 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/ase-reproducibility

Context preview

The summary Claude sees to decide when to auto-load this skill.

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

SKILL.md

ase-reproducibility.SKILL.md
name: 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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