计划执行与检查节点
executing-plans
按既定实施计划完成工作,保持过程检查和反馈。
customization-and-constraints【效率与协作】Customize PySR search space, custom operators, losses, constraints, units, and mutation or plugin behavior.
独立 GitHub 快照 · 自动分类 · 以原作者文档为准
Use this sub-skill when the user wants to change how PySR searches, scores, or rejects expressions rather than simply fitting a standard model.
operatorsNaN guards for invalid operator domainsextra_sympy_mappings for custom operatorselementwise_loss, loss_function, or loss_function_expressionnested_constraints, or complexity shapingX_units, y_units, or dimensionless_constants_onlyfit-and-diagnosestructured-expressionsexport-and-artifactsruntime-and-scalingNaN, not Python-style exceptions.loss_scale="linear" when negative losses are possible.Inf whenever evolution needs a gradient toward compliance.maxsize, operator constraints, and units so intermediate expressions remain reachable.mutations / default_mutations and plugins / default_plugins when the user wants runtime tuning objects instead of legacy weight knobs.references/operators-and-losses.md for custom operators and loss modes.references/constraints-and-units.md for constraints, complexity shaping, and units.references/api-reference.md for current constructor and fit signatures.references/troubleshooting.md for common failures and fixes.scripts/validate_custom_operator.py for a safe static checklist before implementation.Return a concrete customization plan or validated operator/loss/constraint draft, and state any unresolved domain, export, or unit caveats explicitly.