  Reservoir computing is a neuromorphic architecture that potentially offers
viable solutions to the growing energy costs of machine learning. In
software-based machine learning, neural network properties and performance can
be readily reconfigured to suit different computational tasks by changing
hyperparameters. This critical functionality is missing in ``physical"
reservoir computing schemes that exploit nonlinear and history-dependent memory
responses of physical systems for data processing. Here, we experimentally
present a `task-adaptive' approach to physical reservoir computing, capable of
reconfiguring key reservoir properties (nonlinearity, memory-capacity and
complexity) to optimise computational performance across a broad range of
tasks. As a model case of this, we use the temperature and magnetic-field
controlled spin-wave response of Cu$_2$OSeO$_3$ that hosts skyrmion, conical
and helical magnetic phases, providing on-demand access to a host of different
physical reservoir responses. We quantify phase-tunable reservoir performance,
characterise their properties and discuss the correlation between these in
physical reservoirs. This task-adaptive approach overcomes key prior
limitations of physical reservoirs, opening opportunities to apply
thermodynamically stable and metastable phase control across a wide variety of
physical reservoir systems, as we show its transferable nature using
above(near)-room-temperature demonstration with Co$_{8.5}$Zn$_{8.5}$Mn$_{3}$
(FeGe).
