PyCSP
3 provides a productive way to build constraint models for solving combinatorial constrained problems and export them to XCSP
3, preserving a complete separation between modeling and solving. However, it lacks native support for scheduling abstractions such as interval variables, sequence variables, and resource functions. As a result, scheduling models must be encoded with low-level integer variables and manual channeling constraints, even though PyCSP
3 already provides global constraints like NoOverlap and Cumulative on integer arrays. We present PyCSP
3 Scheduling, a library that adds scheduling abstractions to PyCSP
3 through 53 dedicated constraints and 27 expressions, and compiles them down to standard PyCSP
3/XCSP
3 constraints, maintaining the modeling/solving separation that underpins the PyCSP
3 ecosystem. On 261 paired instances across 17 model families (5 runs each), both formulations produce identical objectives on all 72 doubly-proved optimal pairs and nearly half of the families (8/17) remain structurally unchanged after compilation; however, runtime performance diverges across families, with clear gains on some (up to 5.8x) and regressions on others due to the overhead of compilation decompositions. Code and benchmarks are available at: https://github.com/sohaibafifi/pycsp3-scheduling