Physics Lab Mathematica Modeling

Physics Lab Mathematica Modeling

Overview

Use this skill for artifact-only physics modeling batches that should stay reproducible, machine-readable, and independent from the older report-controller skill.

This skill is standalone with local copied tools. It is batch-first by default, can expand one run_config.json into the union of discovered picture-result cases plus handout-only cases, retries up to 3 Mathematica attempts and 3 Python attempts per case, and fails a strict batch when any required case still violates explicit handout expectations or default physics-informed sanity checks.

When to Use

• The experiment already has formulas, parameters, boundary conditions, or case definitions ready for modeling.
• The workflow should be driven by a reusable run_config.json contract rather than one-off shell commands.
• The run should auto-discover required cases from picture-result folders and merge them with extra handout-only cases.
• The run needs generated or patched workflow files such as .wl or .py.
• The output should be artifact-only: plots, tables, summaries, snapshots, case manifests, and batch manifests.
• The run may need Python fallback after Mathematica proves unsatisfactory across bounded retries.

Do not use this skill to write report prose, captions, discussion text, or handout-decoding output.

Core Contract

• Keep all required tooling local to /root/.codex/skills/physics-lab-mathematica-modeling/.
• Use /root/.codex/skills/physics-lab-mathematica-modeling/scripts/validate_modeling_config.py to validate run_config.json.
• Use /root/.codex/skills/physics-lab-mathematica-modeling/scripts/discover_required_cases.py to expand the required case batch from the matching picture-result folder plus any handout-only cases.
• Use /root/.codex/skills/physics-lab-mathematica-modeling/scripts/build_or_patch_workflow.py to write run-local generated workflow copies.
• Use /root/.codex/skills/physics-lab-mathematica-modeling/scripts/run_modeling_case.py as the top-level orchestrator.
• Treat Mathematica-first execution as the default path.
• Retry each required case with up to 3 Mathematica attempts and then up to 3 Python attempts when fallback is allowed.
• Require strict batch success by default: every required discovered or handout-only case must pass.
• If explicit handout expectation checks fail, or default physics-informed sanity checks reject the result, the case fails even when an engine exits successfully.

Primary Commands

Validate the run config:

python3 /root/.codex/skills/physics-lab-mathematica-modeling/scripts/validate_modeling_config.py \
  --config "/path/to/run_config.json"

Build or patch a run-local workflow:

python3 /root/.codex/skills/physics-lab-mathematica-modeling/scripts/build_or_patch_workflow.py \
  --config "/path/to/run_config.json" \
  --engine mathematica

Discover the required case batch:

python3 /root/.codex/skills/physics-lab-mathematica-modeling/scripts/discover_required_cases.py \
  --config "/path/to/run_config.json"

Run the full modeling batch:

python3 /root/.codex/skills/physics-lab-mathematica-modeling/scripts/run_modeling_case.py \
  --config "/path/to/run_config.json"

Boundary Rules

• Keep the skill artifact-only.
• Keep source workflow files unchanged; write generated copies into per-case output directories.
• Record one run_config.snapshot.json, one batch_run_result.json, and one case_run_result.json per required case.
• Prefer Mathematica-first execution.
• Use Python fallback only after Mathematica has exhausted its bounded retries.
• Fail the batch when any required case violates explicit handout expectation checks.
• Fail the batch when default sanity checks catch unrealistic geometry, broken radial ordering, mismatched mode topology, or cross-case inconsistency.

Resources

• scripts/common.py: local copied helper functions for JSON and path handling
• scripts/validate_modeling_config.py: run-config validator
• scripts/discover_required_cases.py: required-case discovery and union logic
• scripts/build_or_patch_workflow.py: run-local workflow generator and patcher
• scripts/run_wolfram_expr.py: local copied Wolfram runner
• scripts/run_python_model.py: Python fallback runner
• scripts/run_modeling_case.py: top-level batch orchestrator