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 universal-potentialsgit 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/universal-potentials)<a href="https://agentmods.dev/skills/sfetni/deep-matter-chem-skills/universal-potentials"><img src="https://agentmods.dev/badge/skills/sfetni/deep-matter-chem-skills/universal-potentials/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/universal-potentials"><img src="https://agentmods.dev/badge/skills/sfetni/deep-matter-chem-skills/universal-potentials.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 | $0.00004 | $0.14380 |
| Opus 5 | $0.00002 | $0.07190 |
| Sonnet 5 | $0.00001 | $0.02876 |
| Haiku 4.5 | $0.00000 | $0.01438 |
Grade A, and why
universal-potentials 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 — 910 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Universal Machine-Learned Interatomic Potentials
Description
This skill covers the selection, evaluation, and deployment of pretrained universal machine-learned interatomic potentials (universal MLPs) — models trained on large multi-element datasets that can be applied to new chemical systems without additional DFT labeling. It covers the major model families (MACE-MP, CHGNet, M3GNet, MatterSim, ORB, SevenNet), their supported chemistry and known limitations, practical use for relaxation, screening, pre-equilibration, structure ranking, and dataset triage, and the decision framework for choosing between a universal potential, fine-tuning, and training from scratch. Invoke this skill before committing to any new DFT dataset generation campaign, to establish whether a universal potential already achieves acceptable accuracy for the target application.
Domain Context
Universal MLPs are trained on databases of DFT calculations spanning a large fraction of the periodic table and a wide range of crystal structure types. The two dominant training databases are the Materials Project (MP) — approximately 150,000 relaxed inorganic crystal structures computed with PBE+U — and the Alexandria/OMAT24 databases that extend coverage to higher-energy configurations, surfaces, and molecular systems. [EXPERT REVIEW NEEDED: database coverage evolves rapidly; verify against the model release notes before deployment]
What universal training databases cover. Most universal potentials are trained on near-equilibrium inorganic crystals at 0 K (or modest finite-temperature MD snapshots) computed at the PBE or PBE+U level of DFT with PAW pseudopotentials. The training set is diverse in composition but concentrated in low-energy, periodic, charge-neutral, diamagnetic or ferromagnetic phases. Coverage of molecules, surfaces, defects, liquids, high-pressure polymorphs, mixed-valence compounds, and charged supercells is sparse or absent in the original training data of most models, though newer models (MatterSim, ORB-v2, MACE-MP-0b) have broader coverage. [EXPERT REVIEW NEEDED]
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 · 910 lines · 4 tokens per session scan A 52d25a62e854
universal-potentials is a skill published in the GitHub repository SFETNI/Deep-Matter-Chem-Skills (6 stars, last pushed 1mo ago), licensed MIT. It adds 4 tokens to every session and 14,380 once invoked, about $0.0000 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 2026-08-31.
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