The Agent Skills Directory

[EXTERNAL_DOWNLOADS]: The skill references and requires the installation of the pymoo library along with standard scientific dependencies such as numpy, scipy, matplotlib, autograd, joblib, and optuna. These are well-known, legitimate packages from official registries.
[COMMAND_EXECUTION]: The documentation and scripts include patterns for parallelizing function evaluations using multiprocessing.Pool and the joblib library. This is a standard and expected feature for handling computationally expensive objective functions in optimization workflows.
[INDIRECT_PROMPT_INJECTION]: The skill establishes an interface for defining and evaluating custom optimization problems via the _evaluate method.
Ingestion points: scripts/custom_problem_example.py and references/problems.md define how custom logic is integrated into the solver.
Boundary markers: The skill does not implement explicit boundary markers for mathematical expressions, which is typical for domain-specific scientific frameworks.
Capability inventory: The skill utilizes multiprocessing and joblib for parallelization as documented in references/parallelization.md.
Sanitization: Evaluation is performed on mathematical logic without specific sanitization, consistent with the framework's intended scientific use case.

pymoo