preparing-tests

You can generate validators and checkers by using this skill to automatically create header files from problem constraints. This ensures your generators and validators stay synchronized with the original problem specifications, maintaining consistency across the suite.

The best way to enforce subtask constraints is by building a validator that strictly checks problem specifications against the source of truth. This skill automates contract enforcement, verifying that every declared bound is reached and that the test suite is legally compliant with subtask rules.

Yes, this skill requires testlib to maintain consistency between problem statements and test data contracts. It integrates testlib with Python tools to support generator development, subtask-specific validation, and reachability analysis for problem constraints.

To verify that your test suite reaches all declared bounds, run the skill's structured validation pipeline. It performs reachability analysis for problem constraints to ensure every declared bound is reached and that the test suite is legally compliant with the problem specifications.

Yes, you can use this skill to generate a diverse set of test cases ranging from random to adversarial. When setting a new problem, it helps build a validator that strictly enforces subtask constraints while fully covering the problem's complexity with varied test data.