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 mlp-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/mlp-validation)<a href="https://agentmods.dev/skills/sfetni/deep-matter-chem-skills/mlp-validation"><img src="https://agentmods.dev/badge/skills/sfetni/deep-matter-chem-skills/mlp-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/mlp-validation"><img src="https://agentmods.dev/badge/skills/sfetni/deep-matter-chem-skills/mlp-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 | $0.00003 | $0.13105 |
| Opus 5 | $0.00002 | $0.06552 |
| Sonnet 5 | $0.00001 | $0.02621 |
| Haiku 4.5 | $0.00000 | $0.01311 |
Grade A, and why
mlp-validation scanned grade A with 1 finding 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.
Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
subprocess.run(cmd, check=True) How it starts
The opening of the file, as written. The whole thing — 926 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MLP Validation
Description
This skill covers the systematic validation of machine-learned interatomic potentials (MLPs) before production deployment: static test-set error analysis, physical property reproduction (equation of state, phonons, elastic constants, surface and defect energies), MD stability testing, extrapolation detection, and deployment consistency verification between training code, ASE, and LAMMPS. Invoke this skill after completing MLP training (see mace-training) and before using the MLP for production MD or property prediction. A low test-set error is necessary but not sufficient for a production-quality MLP — this skill documents the additional checks required to catch the failure modes that pass the training metrics.
Domain Context
An MLP is a function approximator fit to a finite set of DFT-labeled structures. The training loss and test-set error measure how well the model interpolates within the sampled configuration space. They do not measure:
- Whether the sampled space is representative of the target application (MD at a given temperature and pressure, surface chemistry, defect migration)
- Whether the model produces physically correct macroscopic properties (thermal expansion, phonon frequencies, elastic moduli) that emerge from the collective behavior of the potential energy surface
- Whether the model is numerically stable over nanosecond timescales, where rare high-energy configurations may be sampled with non-negligible probability
- Whether the LAMMPS and ASE implementations of the same model file produce identical forces (they should, but unit conversions and element-order conventions have introduced silent bugs in practice)
The validation trinity structures this skill:
- Static errors: Energy, force, and stress MAE on held-out DFT structures, decomposed by chemical environment and configuration family. This is the fastest check and narrows down systematic biases.
- Physical property reproduction: Comparison of MLP-predicted properties against DFT references computed at the same level of theory. EOS, phonon frequencies, elastic constants, and defect energies are the most diagnostic. These catch errors in the many-body and stress response that per-atom force errors miss.
- MD stability: Short NVE energy conservation runs, NVT temperature stability, and NPT density and structural reproduction. MD integrates the potential repeatedly and amplifies any nonphysical region of the PES that was not covered by training.
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 · 926 lines · 3 tokens per session scan A 10d012e0eec0
mlp-validation is a skill published in the GitHub repository SFETNI/Deep-Matter-Chem-Skills (6 stars, last pushed 1mo ago), licensed MIT. It adds 3 tokens to every session and 13,105 once invoked, about $0.0000 per session on Opus 5. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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