I am trying to automate a system for checking if data is approximately normally distributed.
I know for a fact that the data is not strictly normal (there are small deviations from normality) so I only want to check if it is "normal enough".
I have large sample sizes so I avoid using a hypothesis test. I always use a QQ plot to check for approximate normality, I'm aware of the problem with hypothesis tests like the Sapiro-Wilk test when the sample size is very big.
I'm searching for a numerical measure of normality which has an equal expected value for any sample size. A measure like the p value of a test will converge to $0$ for large sample sizes and the variance of the $p$ value also tends towards $0$. I'd like a measure which has the same expected value for any sample size but a lower variance for larger samples.
Since I'm looking at QQ plots I tried to use the $R^2$ value of the line on the QQ plot but this didn't work because it measures the quality of the correlation without caring if the slope of the line is $1$ and the intercept is $0$. In the chart below I've made a QQ plot of an exponentially distributed variable (transformed to have $0$ mean and unit variance), the $R^2$ was high because most of the values are between -4 and -3 where the line is very linear.
I also tried to use the slope of the QQ plot line (where a slope near 1 indicates normality). However, after some testing I found that any symmetrical distribution will have a QQ plot with an expected slope equal to 1 so this is a measure of symmetry not normality.
Is there a smarter way to approach this problem?