continuous-learning

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:

يمكنك استخراج الأنماط تلقائيًا من جلسات البرمجة وتتبع التصحيحات وبناء معرفة قابلة لإعادة الاستخدام مع تسجيل نقاط الثقة المصدر: rohitg00/awesome-claude-code-toolkit.