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/mat-surface-adsorptionnpx skills add learningmatter-mit/AtomisticSkills --skill mat-surface-adsorptiongit 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/mat-surface-adsorption)<a href="https://agentmods.dev/skills/learningmatter-mit/atomisticskills/mat-surface-adsorption"><img src="https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/mat-surface-adsorption.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 | $0.00022 | $0.02274 |
| Opus 5 | $0.00011 | $0.01137 |
| Sonnet 5 | $0.00004 | $0.00455 |
| Haiku 4.5 | $0.00002 | $0.00227 |
Grade A, and why
mat-surface-adsorption scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured today.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 207 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Surface Adsorption Skill
This skill provides tools for calculating adsorption energies ($E_{ads}$) of molecules on crystalline surfaces using Machine Learning Interatomic Potentials (MLIPs).
Goal
To calculate the adsorption energy for a given adsorbate-surface combination, defined as:
$$E_{ads} = E_{adsorbate+slab} - E_{slab} - E_{adsorbate}$$
where:
- $E_{adsorbate+slab}$ is the total energy of the adsorbate adsorbed on the surface
- $E_{slab}$ is the energy of the clean slab
- $E_{adsorbate}$ is the energy of the isolated adsorbate molecule
The skill uses MatCalc's AdsorptionCalc to automate the full workflow: bulk relaxation, slab generation, adsorbate relaxation, site identification, and energy calculations.
Prerequisites
- The appropriate MLIP wrapper must be available (
MACEWrapper,MatGLWrapper, orFAIRCHEMWrapper) matcalc,pymatgen, andasemust be installed in the relevant conda environment- A bulk crystalline structure file (CIF, POSCAR, etc.)
- An adsorbate molecule structure file (XYZ, CIF) or SMILES string
Choosing a Foundation Potential
Adsorption energy calculations require accurate prediction of both energies and forces, particularly for the adsorbate-surface interaction. Models trained on Open Catalyst datasets are especially recommended as they were specifically designed for catalysis and surface chemistry.
[!IMPORTANT] Recommended models (in order of preference):
- Open Catalyst trained models (BEST for surface adsorption):
- FAIRChem:
EquiformerV2-31M-S2EF-OC20-All+MD,EquiformerV2-153M-S2EF-OC20-All+MD- FAIRChem UMA:
uma-s-1p1,uma-m-1p1(universal, includes OC20/OC25 data)- MACE-OMAT:
MACE-OMAT-0-small,MACE-OMAT-0-medium(trained on OC datasets)- MatPES trained models (Good for general surfaces):
CHGNet-MatPES-PBE-2025.2.10-2.7M-PESM3GNet-MatPES-PBE-v2025.1-PESMACE-MatPES-PBE-0- Avoid MPtrj-only models: Models trained primarily on the
MPtrjdataset may suffer from force prediction issues critical for adsorption.
What ships with it
8 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.
- examples/CO_on_Cu111/adsorption_results.json 1.3 KB
- examples/CO_on_Cu111/CO_Cu111_initial.cif 3.2 KB
- examples/CO_on_Cu111/CO_Cu111_relaxed.cif 3.2 KB
- examples/CO_on_Cu111/CO.xyz 66 B
- examples/CO_on_Cu111/Cu_bulk.cif 697 B
- examples/CO_on_Cu111/generate_structures.py 3.5 KB runs code
- examples/CO_on_Cu111/README.md 1.8 KB
- scripts/calculate_adsorption.py 10 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.
- today First seen · 207 lines · 22 tokens per session scan A 53a7c0f019e2
mat-surface-adsorption is a skill published in the GitHub repository learningmatter-mit/AtomisticSkills (158 stars, last pushed today), licensed MIT. It adds 22 tokens to every session and 2,274 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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