  We present a coherent, re-usable python framework which further builds on the
cosmological emulator code CosmoPower. In the current era of high-precision
cosmology, we require high-accuracy calculations of cosmological observables
with Einstein-Boltzmann codes. For detailed statistical analyses, such codes
often incur high costs in terms of computing power, making parameter space
exploration costly, especially for beyond-$\Lambda$CDM analyses. Machine
learning-enabled emulators of Einstein-Boltzmann codes have emerged as a
solution to this problem and have become a common way to perform fast
cosmological analyses. To enable generation, sharing and use of emulators for
inference, we define standards for robustly describing, packaging and
distributing them, and present software for easily performing these tasks in an
automated and replicable manner. We provide examples and guidelines for
generating your own sufficiently accurate emulators and wrappers for using them
in popular cosmological inference codes. We demonstrate our framework by
presenting a suite of high-accuracy emulators for the CAMB code's calculations
of CMB $C_\ell$, $P(k)$, background evolution, and derived parameter
quantities. We show that these emulators are accurate enough for both
$\Lambda$CDM analysis and a set of single- and two-parameter extension models
(including $N_{\rm eff}$, $\sum m_{\nu}$ and $w_0 w_a$ cosmologies) with
stage-IV observatories, recovering the original high-accuracy
Einstein-Boltzmann spectra to tolerances well within the cosmic variance
uncertainties across the full range of parameters considered. We also use our
emulators to recover cosmological parameters in a simulated cosmic-variance
limited experiment, finding results well within $0.1 \sigma$ of the input
cosmology, while requiring typically $\lesssim1/50$ of the evaluation time than
for the full Einstein-Boltzmann computation.
