Absolutely - this is one of the reasons that we made Kale open sourced so that people can see what we consider an anomaly, and adapt for their own use cases if needed. If your anomaly detection contains secret sauce, it'll be very hard for people to have confidence in it.
I'll definitely have a look at doing that - the initial specs were designed around the metric volumes we use the tools for, but I realise that might not be practical for smaller workloads :)
Oculus author here - Oculus detects other metrics that look similar, ie that have a simlar anomaly or shape in the same time span. It doesn't pick up metrics that have other, "dissimilar" anomalies - that part is left to Skyline
Oculus treats all metrics that it gets from Skyline equally at the moment, ie it doesn't know if what it's looking at is an aggregation, or a single set of data points. It just takes the data as it's presented. It would be totally possible, however, to add 10 and 20 minute averages (for example) for the same metric into Skyline so that Oculus would treat them separately.