reflect:cost

You can monitor AI reflection process costs by tracking token usage and expenses for cached, uncached writes, and IO operations. This skill evaluates cache reuse health and recall followup rates to ensure cost-effective reflection.

AI reflection cache reuse health identifies inefficient caching patterns in your system. Monitoring this metric helps highlight wasted resources and ensures that token usage remains cost-effective during reflection processes.

To get a 7-day cost report for AI reflection, use the reflect:cost skill. It analyzes token usage, expenses for IO operations, and subscription quota usage to provide detailed resource insights.

Yes, you need the reflection plugin and appropriate access to cost logs to analyze AI reflection token expenses. The skill relies on these dependencies to track cached and uncached write operations.

Yes, you can check subscription quota usage and writer gate status for AI infrastructure. The skill reports these metrics alongside token costs to help you understand resource limits and optimize reflection spend.

The best way to optimize AI infrastructure costs is by analyzing cache reuse health and recall followup rates. Identifying inefficient caching in uncached writes allows you to reduce token usage and optimize reflection spend.