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-lammps-mdnpx skills add learningmatter-mit/AtomisticSkills --skill mat-lammps-mdgit 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-lammps-md)<a href="https://agentmods.dev/skills/learningmatter-mit/atomisticskills/mat-lammps-md"><img src="https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/mat-lammps-md.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 | $0.00043 | $0.01198 |
| Opus 5 | $0.00022 | $0.00599 |
| Sonnet 5 | $0.00009 | $0.00240 |
| Haiku 4.5 | $0.00004 | $0.00120 |
Grade A, and why
mat-lammps-md 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 — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LAMMPS Molecular Dynamics with MLIPs
Goal
Run GPU-accelerated LAMMPS molecular dynamics with MLIP backends using three isolated binaries (MACE, MatGL/CHGNet, FairChem) so Python embedding through ML-IAP/mliappy remains stable and reproducible.
Instructions
-
Select the MLIP backend and model family first using the foundation-potential guide:
- ml-foundation-potentials
- This determines which conda env and which LAMMPS binary you must use.
-
Check system prerequisites.
# Env: base-agent
nvidia-smi
nvcc --version
g++ --version
cmake --version
mpicxx --version
- Identify GPU compute capability and set Kokkos arch flag.
# Env: base-agent
nvidia-smi --query-gpu=name,compute_cap --format=csv,noheader
- Example mapping:
8.0->Kokkos_ARCH_AMPERE808.6->Kokkos_ARCH_AMPERE868.9->Kokkos_ARCH_ADA899.0->Kokkos_ARCH_HOPPER90
-
Build the environment-matched LAMMPS binary (choose one of the three paths below).
Path A: MACE
# Env: base-agent
bash conda-envs/mace-agent/install.sh
KOKKOS_ARCH_FLAG=Kokkos_ARCH_AMPERE86 \
LAMMPS_REF="stable_2Aug2023_update2" \
bash conda-envs/mace-agent/install_lammps.sh
- Binary:
./lammps/mace-agent/lmp - Runtime env:
mace-agent
Path B: MatGL/CHGNet
# Env: base-agent
bash conda-envs/matgl-agent/install.sh
KOKKOS_ARCH_FLAG=Kokkos_ARCH_AMPERE86 \
LAMMPS_REF="stable_2Aug2023_update2" \
bash conda-envs/matgl-agent/install_lammps.sh
- Binary:
./lammps/matgl-agent/lmp - Runtime env:
matgl-agent
Path C: FairChem
# Env: base-agent
bash conda-envs/fairchem-agent/install.sh
KOKKOS_ARCH_FLAG=Kokkos_ARCH_AMPERE86 \
LAMMPS_REF="stable_2Aug2023_update2" \
bash conda-envs/fairchem-agent/install_lammps.sh
- Binary:
./lammps/fairchem-agent/lmp - Runtime env:
fairchem-agent
- Run the selected binary with its matching conda environment.
# Env: mace-agent (example; switch env/binary pair as needed)
conda activate mace-agent
./lammps/mace-agent/lmp -h
What ships with it
11 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/fairchem/in.relax_adsorption_fairchem 879 B
- examples/fairchem/README.md 671 B
- examples/fairchem/run_fairchem_co_cu111_adsorption.sh 3.3 KB runs code
- examples/mace/generate_na2si3o7_structure.py 2.1 KB runs code
- examples/mace/in.na2si3o7_quench_mace 1.3 KB
- examples/mace/README.md 1.2 KB
- examples/mace/run_mace_na2si3o7_quench.sh 4.2 KB runs code
- examples/matgl/in.cu_phase_transition_matgl 1.1 KB
- examples/matgl/README.md 829 B
- examples/matgl/run_matgl_cu_phase_transition.sh 4.6 KB runs code
- scripts/three-backends-build-check/README.md 1.1 KB
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 · 105 lines · 43 tokens per session scan A 2a626b97e761
mat-lammps-md is a skill published in the GitHub repository learningmatter-mit/AtomisticSkills (158 stars, last pushed today), licensed MIT. It adds 43 tokens to every session and 1,198 once invoked, about $0.0002 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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