Data Engineering is a Data skill for Claude Code, published by Light0305 in Light-skills.
Data Engineering checks whether a dataset can support a research idea, including size, quality, license, versioning, and split strategy. It also audits for train-test leakage before analysis or model training.
Data Engineering is cataloged under Data on DirSkills. Data Engineering comes from a repository tagged academic-writing, agent-skills, ai-agent, ai-skills and claude-code.
Documentation
你是 Light 科研流水线的 DAG 第 2 节点。任务不是"先把数据洗干净再说"，是在提 idea 之前回答院士会枪毙 idea 的 两个硬问题：这数据够不够支撑这个研究（规模/质量/功效）？ 和 这套划分有没有藏着让结果虚高的数据泄漏？ 数据 不足以支撑的 idea 拦在定稿前（带"缺口 + 补法"回 idea-generation，回边 2⊣3）；数据泄漏（顶会拒稿高频雷）是 critical 一票否决。
This is the opening of the README. Read the full README on GitHub.
Data Engineering is one of 23 skills that DirSkills catalogs from Light0305/Light-skills, the repository it ships in. Its siblings there include Citation, Frontend Design and Idea Critique. Each one is a separate skill with its own page in this directory, installs the same way Data Engineering does, and is maintained by Light0305 in that same repository. The rest of the collection is listed on the Light0305/Light-skills page.
Data Engineering ranks #582 by stars among the 668 Data skills in this catalog. The most-starred ones next to it are Benchmark Methodology, Jupyter Notebook and Solana. DirSkills ranks by the star count of the repository each skill ships in, so that order reflects how popular those repositories are rather than any review of Data Engineering against them. Open each page to compare what they document and how they install.
Data Engineering is one of 23 skills cataloged on DirSkills from Light0305/Light-skills.
Data Engineering is one of 668 Data skills cataloged on DirSkills, ranked #582 by stars among them.