Agent skill · Backend & API

jcrd-reproducibility

在为《计算机研究与发展》(Journal of Computer Research and Development, JCRD) 稿件构建可复现性与实验可重复证据时调用。覆盖环境与依赖版本固定、随机种子与非确定性控制、数据集版本与抽取日期钉死、大模型/API 输出缓存、硬件与度量口径记录、双盲下的匿名可运行材料、诚实的可用性说明,以及让外审专家在不联系作者情况下重跑主结果的组织方式。适用于把一份 JCRD 中文稿件的实验做到可被第三方在受控条件下重复、并经得起外审可复现质疑的场景。

brycew6m878★ · +32/wk · 1 repos on radarProfile →
claude-codeMIT
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jcrd-reproducibility --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 1
SKILL.md size: 3 KB
Bundled scripts: none
Path: Journal-of-Computer-Research-and-Development-Skills/skills/jcrd-reproducibility/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 909 · +31 this week
Language: Stata
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# 《计算机研究与发展》可复现性与实验可重复 (JCRD Reproducibility) 本技能帮助为《计算机研究与发展》(Journal of Computer Research and Development, JCRD) 稿件建立 **可复现性**证据。JCRD 实行**双盲多轮同行评审**,外审专家常就「结果能否重复」提出质疑;一份在 设计期就钉死来源、控制随机性、并提供匿名可运行材料的稿件,能显著缩短评审轮次。可复现不是事后补的 包装,而是**采集期**就要做的工程纪律。 ## 一、可复现的三个层级 | 层级 | 含义 | JCRD 关注点 | |---|---|---| | 可重复 (repeatable) | 同一团队同一环境重跑得同一结果 | 种子、环境、脚本固定 | | 可复现 (reproducible) | 他人用你的材料重跑得同一结果 | 匿名材料 + READM

More from Awesome-Journal-Skills
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About this skill
What does the jcrd-reproducibility skill do?

在为《计算机研究与发展》(Journal of Computer Research and Development, JCRD) 稿件构建可复现性与实验可重复证据时调用。覆盖环境与依赖版本固定、随机种子与非确定性控制、数据集版本与抽取日期钉死、大模型/API 输出缓存、硬件与度量口径记录、双盲下的匿名可运行材料、诚实的可用性说明,以及让外审专家在不联系作者情况下重跑主结果的组织方式。适用于把一份 JCRD 中文稿件的实验做到可被第三方在受控条件下重复、并经得起外审可复现质疑的场景。

How do I install it?

Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jcrd-reproducibility --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From brycewang-stanford/Awesome-Journal-Skills, a repository with 909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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