  We provide a new perspective on shadow tomography by demonstrating its deep
connections with the general theory of measurement frames. By showing that the
formalism of measurement frames offers a natural framework for shadow
tomography -- in which ``classical shadows'' correspond to unbiased estimators
derived from a suitable dual frame associated with the given measurement -- we
highlight the intrinsic connection between standard state tomography and shadow
tomography. Such perspective allows us to examine the interplay between
measurements, reconstructed observables, and the estimators used to process
measurement outcomes, while paving the way to assess the influence of the input
state and the dimension of the underlying space on estimation errors. Our
approach generalizes the method described in [H.-Y. Huang {\it et al.}, Nat.
Phys. 16, 1050 (2020)], whose results are recovered in the special case of
covariant measurement frames. As an application, we demonstrate that a
sought-after target of shadow tomography can be achieved for the entire class
of tight rank-1 measurement frames -- namely, that it is possible to accurately
estimate a finite set of generic rank-1 bounded observables while avoiding the
growth of the number of the required samples with the state dimension.
