error-analysis
This skill should be used when the user asks to "分析错题", "错因分析", "整理错题本", "我这道题做错了", "帮我分析错因", "归类薄弱知识点", "错误模式", "巩固练习", "这道题怎么错了", "为什么做错了", "帮我看看这道题", "我的答案和标准答案不一样", "老师说我这题错了", "这几道题都错了帮我看看", "我总是这类题错", or when the user submits wrong answers, homework corrections, marked papers, screenshots of mistakes, or wants error pattern analysis and targeted consolidation exercises.
Profile →npx skills add mingchen666/Reviva --skill error-analysis --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 错题与薄弱点 接收错题，分析错因，归类薄弱知识点，输出错题本和巩固练习。 ## Purpose 将错误转化为学习机会。不仅指出"错了什么"，更要诊断"为什么错"和"怎么避免再错"。 可分析： - 文字描述的错题 - 截图 / 照片中的错题 - 手写解题过程 - 老师批改标记 - 多道错题的共性模式 可输出： - 单题错因分析 - 图片错题分析 - 快速诊断 - 错题模式分析（多题） - 错题本记录 - 巩固练习 ## When to Use Use this skill when the user asks to: - 分析一道错题的错因 - 对比自己的答案和正确答案 - 找出做错的根本原因 - 整理错题本 - 识别反复出现的错误模式 - 归类薄弱知识点 - 生成针对性巩固练习 - 分析老师批改或反馈 - 分析截图或照片中的错题 - 批量分析多道错题 示例： - "我这题为什么错？"
What does the error-analysis skill do?
This skill should be used when the user asks to "分析错题", "错因分析", "整理错题本", "我这道题做错了", "帮我分析错因", "归类薄弱知识点", "错误模式", "巩固练习", "这道题怎么错了", "为什么做错了", "帮我看看这道题", "我的答案和标准答案不一样", "老师说我这题错了", "这几道题都错了帮我看看", "我总是这类题错", or when the user submits wrong answers, homework corrections, marked papers, screenshots of mistakes, or wants error pattern analysis and targeted consolidation exercises.
How do I install it?
Run `npx skills add mingchen666/Reviva --skill error-analysis --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 mingchen666/Reviva, a repository with 181 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.