videocut-self-update

Agent self-improvement is automated by recording user feedback incidents and updating guidelines. The skill integrates corrections directly into CLAUDE.md and tips/*.md files, ensuring rules evolve with real usage without blind appends.

In-place rule integration updates CLAUDE.md by applying structured feedback directly to the policy documents. It avoids duplicate rules by capturing events and tracing changes, ensuring the method guidelines remain clean and aligned.

Automatic updates to policy documents are triggered by specific user terms such as 更新规则, 记录反馈, or 改进skill. This trigger-driven workflow captures the feedback event and initiates the structured update flow.

The best way to log structured feedback for iterative AI workflows is capturing discrete events instead of duplicate rules. This approach records user corrections and automatically traces changes to update rule sets and method guidelines.

The feedback documentation process supports automatic change tracing for tips/*.md files. It captures user feedback incidents and integrates them into updated rule sets, keeping the method guidelines structured and traceable.

Automated self-updating skills prevent blind appends and duplicate rules by capturing structured feedback events. Manual editing lacks automatic change tracing, making it harder to keep governance rules aligned with real iterative AI usage.