Thus the society of mind remark - Minsky's thesis as I understand it is that the mind is a kludge of tailor-made components that perform nicely in their domain but are basically useless outside of it (which seems to me incompatible with the phenomenon of neuroplasticity).

In a complex ANN or a brain, you start with a really simple hierarchical prior over the network and a general purpose optimizer. After training you may get a 'kludge of tailor-made components' that perform really well on the domain you trained on. The result may be specific, but the process is very general.

A much more compelling performance would be the ability for a system to, say, read a textbook on topology and then pass an exam paper on the subject,

Yes, but that probably requires a large number of precursor capabilities AI systems do not yet possess.

I generally agree that a proper "agi-hard" metric will include a large battery of tests to get coverage over a wide range of abilities. We actually already have a good deal of experience on how to train AGIs and how to come up with good test metrics - in the field of education.

However you could view the various AI benchmarks in aggregation as an AGI test battery - each test measures only a narrow ability but combine enough of those tests and you have something more general. The recent development of textual QA benchmarks is another next step in that progression. Game environment tests such as Atari provide another orthogonal way to measure AGI progress.