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benchmark-and-mms-planner

HeshamFS
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Testingai

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

Recommended
Primary
npx skills add HeshamFS/materials-simulation-skills -a claude-code
Plugin CommandAlternative
/plugin add https://github.com/HeshamFS/materials-simulation-skills
Git CloneAlternative
git clone https://github.com/HeshamFS/materials-simulation-skills.git ~/.claude/skills/benchmark-and-mms-planner

Copy 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

InputDescriptionExample
PDE or model classGoverning familydiffusion, elasticity, phase-field
Quantity of interestMetric to validateinterface velocity, L2 temperature error
Dimension1, 2, or 32
Expected orderFormal discretization order2
Reference availabilityAnalytic, benchmark, or noneanalytic
Risk levelCost or consequence of wrong resulthigh

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_strategy
  • mms_plan
  • benchmark_cases
  • refinement_protocol
  • acceptance_criteria
  • warnings

Workflow

  1. Collect the governing model, quantity of interest, and risk level.
  2. Run benchmark_mms_planner.py --json.
  3. Treat warnings as blockers for high-risk claims.
  4. 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 Bash only to run its bundled script.

References

  • See references/vv_patterns.md for MMS, benchmark, and uncertainty planning notes.

Version History

  • 1.0.0: Initial benchmark and MMS planning skill.

GitHub Repository

HeshamFS/materials-simulation-skills
Path: skills/verification-validation/benchmark-and-mms-planner
0
agent-skillsagentscli-toolscomputational-sciencellmmaterials-science

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