CDO

I'd like to calculate two values from HadISST data: 1) mean annual SST and 2) mean annual SST for the coolest 6 months.
(dataset here: https://www.metoffice.gov.uk/hadobs/hadisst/data/HadISST_sst.nc.gz)

For various reasons, my current workflow is this:
i) set missing values to nan
ii) cut timeseries to 2010-2019
iii) set missing values to distance-weighted average
iv) regrid to a 0.5x0.5 degree grid using bilinear interpolation

For calculating mean annual SST the following code works perfectly:

cdo -L -O -yearmean -select,name=sst -remapbil,global_0.5 -setmisstonn -selyear,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019 -select,name=sst -setmissval,nan HadISST_sst.nc HadISST_baseline_SST_ts_annual.nc

For calculating the mean annual SST for only the coolest 6 months of the year, I've been using the following code:

cdo -L -O -select,name=sst -remapbil,global_0.5 -setmisstonn -selyear,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019 -select,name=sst -setmissval,nan HadISST_sst.nc HadISST_baseline_SST_ts_monthly.nc

Then importing to R tidyverse and using slice_min(n=6) to select the minimum SST values in each year, then group_by() and summarise() to calculate annual means.
This workflow (CDO to R) is massively( inefficient as R/tidyverse takes a *long time to process the data.

I'm sure there's a better approach, but I'm not familiar enough with CDO to figure out how to do this!

In summary, here's the missing step in CDO
monthly SST > cut to 6 lowest monthly SST in each year > average to annual

Can I calculate this step directly in CDO, and if so, how to approach this?