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 SFETNI/Deep-Matter-Chem-Skills --skill neural-microstructure-validationgit clone --depth 1 https://github.com/SFETNI/Deep-Matter-Chem-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/sfetni/deep-matter-chem-skills/neural-microstructure-validation)<a href="https://agentmods.dev/skills/sfetni/deep-matter-chem-skills/neural-microstructure-validation"><img src="https://agentmods.dev/badge/skills/sfetni/deep-matter-chem-skills/neural-microstructure-validation/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/sfetni/deep-matter-chem-skills/neural-microstructure-validation"><img src="https://agentmods.dev/badge/skills/sfetni/deep-matter-chem-skills/neural-microstructure-validation.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 | $0.00006 | $0.07146 |
| Opus 5.5 | $0.00002 | $0.02858 |
| Sonnet 5.5 | $0.00001 | $0.01429 |
| Haiku 4.5 | $0.00001 | $0.00715 |
Grade A, and why
neural-microstructure-validation 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 4d 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 — 417 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Neural Microstructure Validation
Description
This skill is a model-independent protocol for checking learned microstructure evolution against physical reference simulations. It applies to PINNs, neural time integrators, neural operators, and other surrogates of phase-field or grain-growth dynamics. It covers how to pre-register a benchmark, admissibility residuals, permutation-invariant grain matching, interface and kinetics errors, extinction and survivor identity, topology, energy-decay checks, refinement and seed statistics, and fair cost accounting. Invoke it before reporting any accuracy, generalization, or speed-up claim for a neural microstructure model. Its numpy/scipy/scikit-image metric library was executed on synthetic test fields.
Domain Context
A neural microstructure model is only as credible as the comparison used to judge it. Phase-field microstructures are high-dimensional, multiphase, and topologically active: grains shrink, vanish, and change neighbours. A single field-averaged error can therefore hide the failures that matter. A model can reach a small mean absolute error while it:
- delays or advances every extinction;
- retains a grain the reference eliminated;
- nucleates a spurious grain;
- drifts off the simplex of admissible phase fractions.
Validation must therefore combine three kinds of check:
- Field-level errors.
- Grain-level and event-level metrics: areas, survivor sets, extinction timing.
- Constraint and physics checks: bounds, unit sum, monotone interfacial energy where the dynamics are a gradient flow.
Three distinctions recur:
- Reference fidelity versus physical fidelity. Most neural studies compare against direct integration of the same discrete operator. That establishes numerical agreement with that discretization, not agreement with a real material. Material claims additionally need calibrated parameters and experimental or higher-fidelity validation. Treat these as separate evidence tiers.
- Training-horizon extrapolation versus initial-condition generalization. Rolling a model past its training horizon on the training microstructure tests temporal stability. Running it on an unseen initial microstructure tests generalization. These are different claims. Neither implies transfer to a different phase count, resolution, mobility law, or physics.
- Development versus prospective evidence. A case that was inspected while choosing architectures, thresholds, or checkpoints is development evidence, even if it was never trained on. Prospective evidence requires these to be frozen before scoring, and each case scored once:
- cases;
- reference data;
- metrics;
- acceptance thresholds;
- checkpoint (by hash).
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.
- 4d ago First seen · 417 lines · 6 tokens per session scan A ab6013166eff
neural-microstructure-validation is a skill published in the GitHub repository SFETNI/Deep-Matter-Chem-Skills (8 stars, last pushed 5d ago), licensed MIT. It adds 6 tokens to every session and 7,146 once invoked, about $0.0000 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 2026-10-12.
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