  We present a novel method to compute \emph{permissive winning strategies} in
two-player games over finite graphs with $ \omega $-regular winning conditions.
Given a game graph $G$ and a parity winning condition $\Phi$, we compute a
\emph{winning strategy template} $\Psi$ that collects an infinite number of
winning strategies for objective $\Phi$ in a concise data structure. We use
this new representation of sets of winning strategies to tackle two problems
arising from applications of two-player games in the context of cyber-physical
system design -- (i) \emph{incremental synthesis}, i.e., adapting strategies to
newly arriving, \emph{additional} $\omega$-regular objectives $\Phi'$, and (ii)
\emph{fault-tolerant control}, i.e., adapting strategies to the occasional or
persistent unavailability of actuators. The main features of our strategy
templates -- which we utilize for solving these challenges -- are their easy
computability, adaptability, and compositionality. For \emph{incremental
synthesis}, we empirically show on a large set of benchmarks that our technique
vastly outperforms existing approaches if the number of added specifications
increases. While our method is not complete, our prototype implementation
returns the full winning region in all 1400 benchmark instances, i.e., handling
a large problem class efficiently in practice.
