Agent Skillsbrycewang-stanford/Awesome-Journal-Skills › icdm-artifact-evaluation

icdm-artifact-evaluation

GitHub

指导为ICDM会议准备可复现的代码与数据仓库,重点处理匿名化、依赖固定及冒烟检查,确保符合单盲/双盲投稿要求。

ICDM-Skills/skills/icdm-artifact-evaluation/SKILL.md brycewang-stanford/Awesome-Journal-Skills

触发场景

准备ICDM会议论文提交物 代码或数据仓库匿名化处理 构建可复现的学术评估包

安装

npx skills add brycewang-stanford/Awesome-Journal-Skills --skill icdm-artifact-evaluation -g -y
更多选项

非标准路径

npx skills add https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICDM-Skills/skills/icdm-artifact-evaluation -g -y

不安装直接使用

npx skills use brycewang-stanford/Awesome-Journal-Skills@icdm-artifact-evaluation

指定 Agent (Claude Code)

npx skills add brycewang-stanford/Awesome-Journal-Skills --skill icdm-artifact-evaluation -a claude-code -g -y

安装 repo 全部 skill

npx skills add brycewang-stanford/Awesome-Journal-Skills --all -g -y

预览 repo 内 skill

npx skills add brycewang-stanford/Awesome-Journal-Skills --list

SKILL.md

Frontmatter
{
    "name": "icdm-artifact-evaluation",
    "description": "Use when packaging code, data, and logs for an ICDM (IEEE International Conference on Data Mining) paper - building the anonymized, history-scrubbed repository that the PDF must cite for a triple-blind Research Track submission, how the single-blind Applied Track changes what may be revealed, and the smoke checks that make an ICDM artifact reviewer-usable."
}

ICDM Artifact Evaluation

Package the artifact so a reviewer can actually use it, under ICDM's anonymity rules. ICDM does not run a separate stamped artifact-badging track the way some venues do (verify per edition); instead, the artifact's job is to be the cited, anonymized evidence that supports the paper. Because the Research Track is triple-blind and traditionally offers no rebuttal, the repository must be complete and anonymous at submission time — there is no later chance to reveal it.

The repository the PDF must cite

  • Reference the code/data repository inside the submitted PDF. A repository not cited at submission is invisible to reviewers for the entire cycle (no rebuttal to add it later).
  • For the Research Track, the link must resolve to an anonymized location, not a named account, and the contents must reveal no identity.
  • For the 2026 Applied Track (single-blind), anonymization of the artifact is not required the same way — but confirm the current call, and still avoid shipping secrets or private data.

Anonymize for the triple-blind regime (Research Track)

Leak surface Fix
Git history (author names, emails) Export a fresh repo with no history
File paths (/home/alice/..., cluster hostnames) Rewrite to relative, generic paths
Internal dataset/system names Rename to public source + version
README acknowledgements, funding Remove until camera-ready
Hosting account that identifies you Use an anonymized hosting option

A triple-blind leak in the artifact is as fatal as one in the PDF, and it is the surface authors most often forget.

Make it reviewer-usable

  • Ship a single entry point and pinned dependencies so a reviewer reproduces a headline table in one command.
  • Include the seeds and configs behind the reported variance (see icdm-reproducibility).
  • Provide a small runnable slice for methods whose full run is expensive, plus instructions to scale up.
# smoke-check an anonymized ICDM reproduction package before citing it in the PDF
python3 ../../../shared-resources/ml-conference-methods/code/check_repro_package.py \
  /path/to/anonymized-repo
# then manually confirm: no .git, no author paths, no internal dataset names,
# one entry script, pinned deps, seed list present, README free of identity.

Handle un-releasable data honestly

  • If data cannot be released, ship the code plus a synthetic proxy that runs end to end, and document the protocol so the private-data numbers are attested rather than opaque.
  • State the scope of what the artifact does and does not reproduce; an honest boundary beats an artifact that silently omits the main result.

Vignette: the commit that would have unmasked the authors

A team built a clean anonymized zip of their code, but linked their normal lab repository whose first commit read "initial import — Alice, BigState University." Under triple-blind that is an identity leak that could invalidate the submission. The fix: export a fresh repository with no history, rewrite absolute paths to relative, rename the internal dataset to its public source and version, strip the acknowledgements from the README, host it anonymously, and cite that link in the PDF. Same artifact, now safe for a triple-blind reviewer.

Output format

[Cited in PDF] repository referenced in the submitted paper: yes / no
[Regime] Research(triple-blind) -> anonymized required | Applied(single-blind)
[Anonymization] no history / no author paths / no internal names: pass / leaks
[Usability] one-command headline table + pinned deps + seeds: yes / no
[Un-releasable data] synthetic proxy + attested protocol: yes / N-A
[Top fix] <single most important artifact fix before submission>

版本历史

  • 9f86f09 当前 2026-07-19 15:55

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元信息

文件数
0
版本
5717eab
Hash
6bfb7515
收录时间
2026-07-19 15:55

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