什么是 continuous-learning?
从编码会话中自动提取模式、跟踪更正并通过置信度评分构建可重用的知识 来源:rohitg00/awesome-claude-code-toolkit。
从编码会话中自动提取模式、跟踪更正并通过置信度评分构建可重用的知识
通过命令行快速安装 continuous-learning AI 技能到你的开发环境
来源:rohitg00/awesome-claude-code-toolkit。
After every significant coding session, extract and categorize learnings into three buckets:
| 0.95+ | Verified across multiple projects | Apply automatically | | 0.80-0.94 | Confirmed in this codebase | Apply and mention | | 0.60-0.79 | Observed but not fully validated | Suggest with caveat | | 0.40-0.59 | Hypothesis based on limited data | Ask before applying | | <0.40 | Speculative, needs validation | Document but do not apply |
At the end of each session or before context compaction:
从编码会话中自动提取模式、跟踪更正并通过置信度评分构建可重用的知识 来源:rohitg00/awesome-claude-code-toolkit。
为搜索与 AI 引用准备的稳定字段与命令。
npx skills add https://github.com/rohitg00/awesome-claude-code-toolkit --skill continuous-learningBrowse more skills from rohitg00/awesome-claude-code-toolkit
从编码会话中自动提取模式、跟踪更正并通过置信度评分构建可重用的知识 来源:rohitg00/awesome-claude-code-toolkit。
打开你的终端或命令行工具(如 Terminal、iTerm、Windows Terminal 等) 复制并运行以下命令:npx skills add https://github.com/rohitg00/awesome-claude-code-toolkit --skill continuous-learning 安装完成后,技能将自动配置到你的 AI 编程环境中,可以在 Claude Code、Cursor 或 OpenClaw 中使用
https://github.com/rohitg00/awesome-claude-code-toolkit