  Non-parametric reconstruction methods, such as Gaussian process (GP)
regression, provide a model-independent way of estimating an underlying
function and its uncertainty from noisy data. We demonstrate how
GP-reconstruction can be used as a consistency test between a given data set
and a specific model by looking for structures in the residuals of the data
with respect to the model's best-fit. Applying this formalism to the Planck
temperature and polarisation power spectrum measurements, we test their global
consistency with the predictions of the base $\Lambda$CDM model. Our results do
not show any serious inconsistencies, lending further support to the
interpretation of the base $\Lambda$CDM model as cosmology's gold standard.
