# benchmark-and-mms-planner

Designs verification and validation campaigns for simulation codes using manufactured solutions, canonical benchmarks, and grid refinement.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · ★ 83
- Author: HeshamFS
- License: Apache-2.0 · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/HeshamFS/materials-simulation-skills/tree/HEAD/skills/verification-validation/benchmark-and-mms-planner/SKILL.md
- Updated: 2026-10-21
- HTML: https://affpapa.org/en/nevibecode/skills/benchmark-and-mms-planner

## What it does

This skill builds verification and validation protocols for numerical solvers in continuum mechanics and material physics. It prepares evidence before you trust simulation claims. The guidance builds on ASME V&V 20-2009 standards, Roache grid convergence procedures, and the Method of Manufactured Solutions (MMS).

Under the hood, the skill uses the standalone script `benchmark_mms_planner.py`. You pass the PDE model family, spatial dimension, discretization order, and risk profile. It outputs JSON with systematic grid refinement steps and pass/fail thresholds. For high-risk runs, it mandates four grid levels with a spacing ratio of 2.

Python 3.10+ is required. The planner outputs test parameters, yet it will not run the simulation solver for you.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add HeshamFS/materials-simulation-skills --skill benchmark-and-mms-planner -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/benchmark-and-mms-planner/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/benchmark-and-mms-planner/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add HeshamFS/materials-simulation-skills --skill benchmark-and-mms-planner -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/benchmark-and-mms-planner/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add HeshamFS/materials-simulation-skills --skill benchmark-and-mms-planner -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/benchmark-and-mms-planner/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install HeshamFS/materials-simulation-skills benchmark-and-mms-planner
```

2. **Manually: put the files in place**
- File: `.github/skills/benchmark-and-mms-planner/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add HeshamFS/materials-simulation-skills --skill benchmark-and-mms-planner -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/HeshamFS/materials-simulation-skills --path skills/verification-validation/benchmark-and-mms-planner
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/benchmark-and-mms-planner/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add HeshamFS/materials-simulation-skills --skill benchmark-and-mms-planner -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/HeshamFS/materials-simulation-skills/tree/HEAD/skills/verification-validation/benchmark-and-mms-planner/SKILL.md

## When to use it
- design a grid refinement study for spatial convergence
- formulate manufactured solution plans for code verification
- calculate screening thresholds for observed discretization orders
- flag blocking verification warnings for high-risk simulation runs

## Example prompts
- Plan an MMS verification workflow for a 2D second-order diffusion model with high risk using benchmark-and-mms-planner.
- Run benchmark_mms_planner.py for 3D elasticity with an analytic reference.
- Generate a spatial refinement protocol and acceptance criteria for a phase-field problem.

## Source text · SKILL.md · Apache-2.0

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 28791e002c71af76.

BEGIN THIRD-PARTY FILE 28791e002c71af76
```text
---
name: benchmark-and-mms-planner
description: >
  Plan verification and validation campaigns for simulation codes using
  manufactured solutions, canonical benchmark problems, grid/time refinement,
  uncertainty propagation, and pass/fail acceptance criteria. Use when an
  agent needs to prove a solver, model, or result is trustworthy rather than
  only plausible.
allowed-tools: Read, Bash, Write, Grep, Glob
metadata:
  last_deterministic_eval: "2026-10-20"
  agent_evaluation_status: "not_revalidated"
  author: HeshamFS
  version: "1.1.3"
  security_tier: high
  security_reviewed: true
  tested_with:
    - claude-code
  last_evaluated: "2026-06-23"
  eval_cases: 3
  last_reviewed: "2026-06-24"
  standards:
    - "ASME V&V 20-2009 (Verification and Validation in Computational Fluid Dynamics and Heat Transfer)"
    - "Roache (1998), Verification and Validation in Computational Science and Engineering"
    - "Salari & Knupp (2000), Code Verification by the Method of Manufactured Solutions"
    - "Celik et al. (2008), GCI procedure (Richardson-extrapolation grid-convergence index)"
---

# 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_strategy`
```
END THIRD-PARTY FILE 28791e002c71af76

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