This is an old question, but I thought it would be useful to add that my DHARMa R package (available from CRAN, see here) now provides standardized residuals for GLMs and GLMMs, based on a simulation approach similar to what is suggested by @GregSnow.
From the package description:
The DHARMa package uses a simulation-based approach to create readily
interpretable scaled residuals from fitted generalized linear mixed
models. Currently supported are all 'merMod' classes from 'lme4'
('lmerMod', 'glmerMod'), 'glm' (including 'negbin' from 'MASS', but
excluding quasi-distributions) and 'lm' model classes. Alternatively,
externally created simulations, e.g. posterior predictive simulations
from Bayesian software such as 'JAGS', 'STAN', or 'BUGS' can be
processed as well. The resulting residuals are standardized to values
between 0 and 1 and can be interpreted as intuitively as residuals
from a linear regression. The package also provides a number of plot
and test functions for typical model mispecification problem, such as
over/underdispersion, zero-inflation, and spatial / temporal
@Momo - you may want to update your recommendation 6, it is misleading. Normality of deviance residuals is in general not expected under a Poisson, as explained in the DHARMa vignette or here; and seing deviance residuals (or any other standard residuals) that differ from a straight line in a qqnorm plot is therefore in general no concern at all. The DHARMa package provides a qq plot that is reliable for diagnosing deviations from Poisson or other GLM families. I have created an example that demonstrates the problem with the deviance residuals here.