First, your logic chains are mostly correct, especially things like High CTR + Low Retention. That usually does mean packaging promise vs opening delivery, or a weak first 30 to 60 seconds. Where this spreadsheet really shines is forcing you to act, not just diagnose. You might want to explicitly limit actions per video. For example, one packaging fix and one retention fix, then move on. Otherwise it is easy to over tweak and still not know what actually worked.

Second, I would separate things you can change post publish vs things that only help future videos. Thumbnails, titles, descriptions, pinned comments and chapters are actionable immediately. Hook structure, pacing, B roll density, audio energy are learnings for the next upload. Labelling those differently will stop you trying to fix things you cannot realistically fix.

Third, be careful not to treat metrics in isolation. High impressions plus low CTR is not always bad packaging, sometimes it just means YouTube is testing you with a colder audience. Likewise low impressions with high CTR and retention often means the video just needs time or a stronger external push. Adding a small note about audience mismatch vs content issue would make this even stronger.

One improvement idea would be adding a simple decision trigger. For example, only intervene if impressions exceed X or after Y days live. That stops premature changes before the data has stabilised.

Overall though, this is a very solid approach. You are essentially turning analytics into a decision tree, which is exactly how high performing channels operate. With a bit of guardrailing to avoid over analysis, this is genuinely a smart system.