executing-plans
executing-plans
Use when executing an implementation plan in the current session as the implementer yourself — your human partner chose inline execution, or no subagent tool is available
physoUse PhySO for symbolic regression, class symbolic regression, toolkit-level expression workflows, and benchmark problem loaders.
Independent GitHub snapshot · Automated classification · Original documentation governs
physo, PhySO, symbolic regression,
ClassSR, toolkit expression helpers, or benchmark problem loaders."
disable-model-invocation: true
metadata:
disco-role: operating
license: MITUse this skill when working with the physo package, the scientific Python symbolic-regression library.
Run scripts/physo_skill_doctor.py to confirm the installed package, key public exports, and the CPU scientific stack used by the verified baseline. The checked baseline was PhySO 1.2.0 on CPU; this skill does not claim CUDA verification. Import-time LaTeX warnings are expected when optional display tools are missing.
X, y, units, y_weights, candidate_wrapper, run presets, logging, or Pareto inspection: use sub-skills/sr/SKILL.md.sub-skills/class-sr/SKILL.md.Program/VectPrograms, random sampling, constant optimization, display, or result reloads: use sub-skills/toolkit/SKILL.md.sub-skills/benchmarks/SKILL.md.references/install-and-smoke.md: install/import check and smoke commands.references/troubleshooting.md: cross-cutting import, display, backend, and routing failures.references/repo-provenance.md: source version, dirty-state snapshot, and evidence paths.references/repo-routing-metadata.json: router placement metadata for compatible agents.This skill is self-contained. It does not depend on opening the source checkout at runtime, and it does not cover repo maintenance, release automation, or long benchmark campaigns.