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 agentmods add skills/learningmatter-mit/atomisticskills/ml-foundation-potentialsnpx skills add learningmatter-mit/AtomisticSkills --skill ml-foundation-potentialsgit clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkillsWrote 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/learningmatter-mit/atomisticskills/ml-foundation-potentials)<a href="https://agentmods.dev/skills/learningmatter-mit/atomisticskills/ml-foundation-potentials"><img src="https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/ml-foundation-potentials.svg" alt="Measured on agentmods" 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 | $0.00021 | $0.01299 |
| Opus 5 | $0.00010 | $0.00649 |
| Sonnet 5 | $0.00004 | $0.00260 |
| Haiku 4.5 | $0.00002 | $0.00130 |
Grade A, and why
ml-foundation-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 yesterday.
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 — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Foundation Potentials Selection
Goal
Select the appropriate machine learning interatomic potential (MLIP) for a given atomistic simulation task, balancing accuracy, computational cost, and material composition.
Model Selection Guide
[!NOTE] This list is not exhaustive. For a full list of available pre-trained checkpoints, refer to the
load_modelfunction documentation for each respective MCP server.
MatGL Models
Environment: matgl-agent
- CHGNet-MatPES-r2SCAN-2025.2.10-2.7M-PES:
- Use for r2SCAN-level inorganic materials simulation.
- Recommended when charge information and magnetic moments are involved (e.g., calculating transition metal valence states).
- CHGNet-MPtrj-2023.12.1-2.7M-PES:
- Use for compatibility with standard Materials Project (GGA/GGA+U) data.
- Recommended when working with legacy MP data.
- TensorNet-MatPES-r2SCAN-v2025.1-PES:
- Use for r2SCAN-level inorganic materials simulation.
- Smaller and faster than CHGNet, suitable for dynamic simulations (MD, NEB, phonons).
FAIRCHEM Models
Environment: fairchem-agent
- uma-s-1p1:
- Use for organic and inorganic simulations.
- Note: UMA models are typically slower and more expensive. Avoid for dynamic simulations with systems >500 atoms.
- uma-m-1p1:
- Use for organic and inorganic simulations with <100 atoms.
- esen-md-direct-all-omol:
- Use for organic ionic relaxation (ground state calculations).
MACE Models
Environment: mace-agent
- MACE-MH-1:
- Latest multi-head foundation model. Use as default for most tasks.
omat_pbehead (default): General materials, balanced performance.matpes_r2scanhead: High-accuracy materials simulation.omolhead: Molecular systems, organic chemistry, organometallics.spice_wB97Mhead: Molecular systems and organic chemistry.oc20_usemppbehead: Surface catalysis, adsorbates.
- MACE-MATPES-r2SCAN-0:
- Specialized for r2SCAN-level inorganic systems.
- MACE-OMAT-0-small:
- Small, efficient model for materials.
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.
- yesterday First seen · 108 lines · 21 tokens per session scan A 00a6583cda9e
ml-foundation-potentials is a skill published in the GitHub repository learningmatter-mit/AtomisticSkills (158 stars, last pushed yesterday), licensed MIT. It adds 21 tokens to every session and 1,299 once invoked, about $0.0001 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 2026-09-03.
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