benchmark-and-mms-planner
About
This Claude Skill helps developers create verification and validation plans for simulation codes to ensure trustworthy results. It provides structured methodologies including manufactured solutions, benchmark problems, and refinement studies with pass/fail criteria. Use it when you need to rigorously prove a solver's correctness rather than just demonstrating plausibility.
Quick Install
Claude Code
Recommendednpx skills add HeshamFS/materials-simulation-skills -a claude-code/plugin add https://github.com/HeshamFS/materials-simulation-skillsgit clone https://github.com/HeshamFS/materials-simulation-skills.git ~/.claude/skills/benchmark-and-mms-plannerCopy and paste this command in Claude Code to install this skill
Documentation
Benchmark And MMS Planner
Goal
Design a verification and validation plan before trusting simulation results. The skill helps agents choose manufactured solutions, benchmark cases, refinement protocols, uncertainty checks, and pass/fail criteria.
Requirements
- Python 3.10+
- No external dependencies
- Works on Linux, macOS, and Windows
Inputs to Gather
| Input | Description | Example |
|---|---|---|
| PDE or model class | Governing family | diffusion, elasticity, phase-field |
| Quantity of interest | Metric to validate | interface velocity, L2 temperature error |
| Dimension | 1, 2, or 3 | 2 |
| Expected order | Formal discretization order | 2 |
| Reference availability | Analytic, benchmark, or none | analytic |
| Risk level | Cost or consequence of wrong result | high |
Decision Guidance
- Use MMS when code correctness is uncertain and an analytic solution can be injected.
- Use canonical benchmarks when physical model validation matters more than code verification.
- Use grid/time refinement whenever the result is used for a claim, design decision, or comparison.
- Use uncertainty propagation when inputs are calibrated, noisy, or experimentally measured.
Script Outputs
scripts/benchmark_mms_planner.py emits inputs and results with:
verification_strategymms_planbenchmark_casesrefinement_protocolacceptance_criteriawarnings
Workflow
- Collect the governing model, quantity of interest, and risk level.
- Run
benchmark_mms_planner.py --json. - Treat warnings as blockers for high-risk claims.
- Convert the returned protocol into tests, simulation runs, or review checklist items.
python3 skills/verification-validation/benchmark-and-mms-planner/scripts/benchmark_mms_planner.py \
--model diffusion \
--quantity "L2 error in temperature" \
--dimension 2 \
--expected-order 2 \
--reference analytic \
--risk high \
--json
Error Handling
- If the dimension or expected order is invalid, stop and correct the model description.
- If no reference exists, use conservation and convergence checks but do not call the result validated.
Limitations
This skill plans verification work; it does not run the solver or prove that a physical model is appropriate for an experiment.
Security
- Inputs are scalar strings and finite numeric values only.
- The script does not execute external solvers.
- File writes are not performed.
- The skill uses
Bashonly to run its bundled script.
References
- See
references/vv_patterns.mdfor MMS, benchmark, and uncertainty planning notes.
Version History
- 1.0.0: Initial benchmark and MMS planning skill.
GitHub Repository
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