Result Analysis is a Data skill for Claude Code, published by Light0305 in Light-skills.
Result Analysis interprets experiment outputs with significance tests, effect sizes, confidence intervals, and multiple-comparison correction. It is used when results need to be tied to claims, evidence strength, and reproducibility without p-hacking.
Result Analysis is cataloged under Data on DirSkills. Result Analysis 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 7 节点。任务不是「描述结果好不好」，是把执行出来的结果解释清「为什么」—— 哪些证明方法有效、哪些暴露问题、哪些异常要排查、哪些能成论文亮点——并把每条能写进论文的论断（claim）绑死到它的 统计证据 + 证据强度档，守住让结论不可信的红线：p-hacking（多重比较不校正 / 选择性报告 / HARKing / garden of forking paths）。统计错误/p-hacking = critical；过度解读、效应量缺失 = warn。显著性看 q 不看 p。
This is the opening of the README. Read the full README on GitHub.
Result Analysis is one of 23 skills that DirSkills catalogs from Light0305/Light-skills, the repository it ships in. Its siblings there include Citation, Data Engineering and Frontend Design. Each one is a separate skill with its own page in this directory, installs the same way Result Analysis does, and is maintained by Light0305 in that same repository. The rest of the collection is listed on the Light0305/Light-skills page.
Result Analysis ranks #584 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 Result Analysis against them. Open each page to compare what they document and how they install.
Result Analysis is one of 23 skills cataloged on DirSkills from Light0305/Light-skills.
Result Analysis is one of 668 Data skills cataloged on DirSkills, ranked #584 by stars among them.