Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/SFETNI/Deep-Matter-Chem-Skillsnpx agentmods add skills/sfetni/deep-matter-chem-skills/mlp-to-lammpsWrote 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-to-lammps)<a href="https://agentmods.dev/skills/sfetni/deep-matter-chem-skills/mlp-to-lammps"><img src="https://agentmods.dev/badge/skills/sfetni/deep-matter-chem-skills/mlp-to-lammps/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-to-lammps"><img src="https://agentmods.dev/badge/skills/sfetni/deep-matter-chem-skills/mlp-to-lammps.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 | $0.00006 | $0.11148 |
| Opus 5 | $0.00003 | $0.05574 |
| Sonnet 5 | $0.00001 | $0.02230 |
| Haiku 4.5 | $0.00001 | $0.01115 |
Grade A, and why
mlp-to-lammps 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 12d 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.
result = subprocess.run( How it starts
The opening of the file, as written. The whole thing — 718 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MLP to LAMMPS Deployment
Description
This skill covers the full pipeline for deploying trained ML interatomic potentials into LAMMPS for production molecular dynamics: LAMMPS compilation with MLP plugins, pair_style and pair_coeff configuration, species-to-atom-type mapping, unit consistency verification, neighbor list tuning, GPU and MPI parallelism considerations, and validation of LAMMPS inference against the Python/ASE reference before any production run. Covers MACE, NequIP/Allegro, and DeePMD-kit deployment patterns. Invoke this skill after passing mlp-validation and before running production LAMMPS simulations with an ML potential.
Domain Context
LAMMPS is the standard platform for large-scale MLP-driven MD because it handles periodic boundary conditions, neighbor list management, ensemble integrators, and output efficiently for systems of thousands to millions of atoms. However, LAMMPS was designed around classical pair potentials; MLP pair styles are added as plugins that wrap TorchScript, TensorFlow, or custom C++ backends. This creates several non-obvious deployment hazards:
The LAMMPS/Python interface is not automatically consistent. The same model file evaluated via MACECalculator (Python/ASE) and via pair_style mace (LAMMPS C++ plugin) should produce identical forces, but will not if: the element ordering in the LAMMPS data file does not match the model's internal species list; the unit system is set incorrectly; or the plugin was compiled against a different version of the model's backend library than the Python environment uses.
LAMMPS units are explicit, not inferred. units metal uses Å, eV, ps, K, bar. A model trained in eV/Å (ASE convention) must be deployed under units metal. units real uses kcal/mol, Å, fs — correct for AMBER/CHARMM classical force fields but fatal for MLPs trained in eV. There is no automatic conversion; LAMMPS passes atom coordinates to the pair style and receives forces back, and if the units declaration does not match what the model expects, forces are silently wrong by a factor of 23.06 (eV vs kcal/mol) with no error message.
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.
- 12d ago First seen · 718 lines · 6 tokens per session scan A 44c6e4965d97
mlp-to-lammps is a skill published in the GitHub repository SFETNI/Deep-Matter-Chem-Skills (6 stars, last pushed 1mo ago), licensed MIT. It adds 6 tokens to every session and 11,148 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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