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 parameter-optimizationgit 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/parameter-optimization)<a href="https://agentmods.dev/skills/heshamfs/materials-simulation-skills/parameter-optimization"><img src="https://agentmods.dev/badge/skills/heshamfs/materials-simulation-skills/parameter-optimization/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/parameter-optimization"><img src="https://agentmods.dev/badge/skills/heshamfs/materials-simulation-skills/parameter-optimization.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 | $0.00127 | $0.03333 |
| Opus 5.5 | $0.00051 | $0.01333 |
| Sonnet 5.5 | $0.00025 | $0.00667 |
| Haiku 4.5 | $0.00013 | $0.00333 |
Grade A, and why
parameter-optimization 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 1mo 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 — 231 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Parameter Optimization
Goal
Provide a workflow to design experiments, rank parameter influence, and select optimization strategies for materials simulation calibration.
Requirements
- Python 3.10+
- No external dependencies (uses Python standard library only)
Inputs to Gather
Before running any scripts, collect from the user:
| Input | Description | Example |
|---|---|---|
| Parameter bounds | Min/max for each parameter with units | kappa: [0.1, 10.0] W/mK |
| Evaluation budget | Max number of simulations allowed | 50 runs |
| Noise level | Stochasticity of simulation outputs | low, medium, high |
| Constraints | Feasibility rules or forbidden regions | kappa + mobility < 5 |
Decision Guidance
Choosing a DOE Method
Is dimension <= 3 AND full coverage needed?
├── YES → Use factorial
└── NO → Is sensitivity analysis the goal?
├── YES → Use quasi-random (preferred; "sobol" is accepted but deprecated)
└── NO → Use lhs (Latin Hypercube)
| Method | Best For | Avoid When |
|---|---|---|
lhs |
General exploration, moderate dimensions (3-20) | Need exact grid coverage |
quasi-random |
Sensitivity analysis, uniform coverage (preferred) | Very high dimensions (>20) |
sobol |
Deprecated alias of quasi-random (emits a warning) |
New code (use quasi-random) |
factorial |
Low dimension (<4), need all corners | High dimension (exponential growth) |
Factorial sizing: the factorial grid is
levelsevenly spaced values per parameter, producing exactlylevels ** paramssamples. Set the resolution explicitly with--levels(e.g.--params 2 --levels 4-> 16 samples). If you use--budgetinstead, the script back-computeslevels = round(budget ** (1/params))and warns whenever the realized sample count differs from the requested budget (e.g.--budget 20 --params 2realizes 16 samples). For an exact design, pass a perfect power (--budget 16) or, preferably,--levels.
What ships with it
10 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.
- CHANGELOG.md 3.4 KB
- evals/evals.json 8.4 KB
- references/doe_methods.md 4.3 KB
- references/optimizer_selection.md 5.4 KB
- references/sensitivity_guidelines.md 4.6 KB
- references/surrogate_guidelines.md 5.7 KB
- scripts/doe_generator.py 6.7 KB runs code
- scripts/optimizer_selector.py 2.7 KB runs code
- scripts/sensitivity_summary.py 2.9 KB runs code
- scripts/surrogate_builder.py 7.6 KB runs code
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.
- 1mo ago First seen · 231 lines · 127 tokens per session scan A c3441ef9657c
parameter-optimization is a skill published in the GitHub repository HeshamFS/materials-simulation-skills (74 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 127 tokens to every session and 3,333 once invoked, about $0.0005 per session on Opus 5.5. 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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