Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add HeshamFS/materials-simulation-skills --skill benchmark-and-mms-plannergit clone --depth 1 https://github.com/HeshamFS/materials-simulation-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/heshamfs/materials-simulation-skills/benchmark-and-mms-planner)<a href="https://agentmods.dev/skills/heshamfs/materials-simulation-skills/benchmark-and-mms-planner"><img src="https://agentmods.dev/badge/skills/heshamfs/materials-simulation-skills/benchmark-and-mms-planner/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/heshamfs/materials-simulation-skills/benchmark-and-mms-planner"><img src="https://agentmods.dev/badge/skills/heshamfs/materials-simulation-skills/benchmark-and-mms-planner.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00058 | $0.02644 |
| Opus 5 | $0.00029 | $0.01322 |
| Sonnet 5 | $0.00012 | $0.00529 |
| Haiku 4.5 | $0.00006 | $0.00264 |
Grade A, and why
benchmark-and-mms-planner scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 180 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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_strategyeffective_model— the resolved model family actually used; unknown families fall back togeneral.mms_planbenchmark_casesrefinement_protocol(dimension,levels,spacing_ratio,expected_order,accept_observed_order_min,include_time_refinement)uncertainty_plan(propagate_inputs,report_error_bars,separate_discretization_and_model_error) — propagation/error-bar guidance driven by risk level and reference type.acceptance_criteriawarnings
The accept_observed_order_min is an engineering screening heuristic, not a certified bound: it is the formal expected_order reduced by a fractional tolerance (10% for high risk, 20% otherwise) and floored at first-order convergence (1.0). The relative band keeps strictness consistent across formal orders. See references/vv_patterns.md.
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 180 lines · 58 tokens per session scan A 8f02052b69a3
benchmark-and-mms-planner is a skill published in the GitHub repository HeshamFS/materials-simulation-skills (66 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 58 tokens to every session and 2,644 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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