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 lammps-workflowgit 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/lammps-workflow)<a href="https://agentmods.dev/skills/sfetni/deep-matter-chem-skills/lammps-workflow"><img src="https://agentmods.dev/badge/skills/sfetni/deep-matter-chem-skills/lammps-workflow/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/lammps-workflow"><img src="https://agentmods.dev/badge/skills/sfetni/deep-matter-chem-skills/lammps-workflow.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 | $0.00005 | $0.08414 |
| Opus 5 | $0.00003 | $0.04207 |
| Sonnet 5 | $0.00001 | $0.01683 |
| Haiku 4.5 | $0.00001 | $0.00841 |
Grade A, and why
lammps-workflow 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 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.
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 — 521 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LAMMPS Workflow
Description
This skill covers end-to-end molecular dynamics simulations using LAMMPS (Large-scale Atomic/Massively Parallel Simulator): input script anatomy, force-field selection and deployment, ensemble control, equilibration protocol, production runs, restart logic, and parallel execution. LAMMPS is the dominant open-source MD code for materials science applications and the standard deployment target for ML interatomic potentials. Invoke this skill when setting up, running, diagnosing, or extending a LAMMPS simulation of any material class from metals to polymers to reactive systems.
Domain Context
LAMMPS integrates Newton's equations of motion for a set of interacting particles under a chosen force field. The equations are solved numerically using a symplectic integrator (Velocity Verlet by default), which conserves a shadow Hamiltonian close to the true Hamiltonian and preserves time-reversibility. Key physical approximations:
- Force field validity domain: Every pair_style encodes specific physical assumptions. EAM is validated for FCC metals near equilibrium; it fails for surfaces, defects with large distortions, or alloys outside the training composition range. Tersoff is validated for covalent semiconductors; it fails for amorphous structures far from the fitting database. ReaxFF can model bond breaking/forming but has known failures for unusual coordination environments. The force field, not the integrator, is the dominant source of error.
- Classical vs. quantum nuclei: LAMMPS integrates classical equations of motion. Zero-point energy and nuclear quantum effects are neglected. This is a good approximation for heavy atoms (≥ C) above ~200 K, but fails for light atoms (H, He, Li) at low temperature and for tunneling-dominated processes. [EXPERT REVIEW NEEDED for precise crossover conditions]
- Boundary conditions: LAMMPS supports periodic (p), shrink-wrapped (s), and fixed (f) boundaries. Periodic boundaries impose artificial periodicity; the simulation box must be large enough that a particle does not interact with its own image through the cutoff.
- Timestep and numerical stability: The timestep must resolve the fastest vibrational frequency in the system. A rule of thumb is dt ≤ period / 20. For stiff bonds (O-H, C-H) in molecular force fields, this requires dt ≤ 0.5 fs or constrained bonds (SHAKE/RATTLE). For metals with EAM, 1–2 fs is typically safe. For ReaxFF with short-range repulsion, 0.1–0.5 fs is required. Exceeding the stability limit produces exponentially growing kinetic energy — the "energy explosion" failure mode.
- Ergodicity and sampling: A single MD trajectory samples the microcanonical or canonical ensemble only if it is ergodic on the timescale of the run. Rare events (phase transitions, defect migration, folding) may not be sampled. Enhanced sampling methods are required for free energy calculations.
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 · 521 lines · 5 tokens per session scan A 4e37dd96f667
lammps-workflow is a skill published in the GitHub repository SFETNI/Deep-Matter-Chem-Skills (6 stars, last pushed 1mo ago), licensed MIT. It adds 5 tokens to every session and 8,414 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.
Other skills, from other repositories
datamol
Pythonic wrapper around RDKit with simplified interface and sensible defaults. Preferred for standard drug discovery including SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, parallel processing. Returns native rdkit.Chem.Mol objects. For advanced control or custom parameters…
smiles-validation
Strict SMILES validation, structural comparison, and modification verification. Catches invalid LLM-generated molecules.
patsnap-biological-modality
Biological sequence and modality intelligence via Patsnap MCP.
patsnap-scientific-translational-evidence
Patsnap Scientific & Translational Evidence MCP for AI agents. Retrieval platform focusing on scientific literature and translational outcomes, covering academic publication queries and translational medicine record tracking.
patsnap-target-disease
Patsnap Target & Disease MCP for AI agents. Target and disease profiling tool, covering target characterization, disease profiling, and epidemiology evidence retrieval.
patsnap-solution-engine
Patsnap TRIZ Concept Solution Engine MCP for AI agents. Generates innovation or product cost-reduction concepts through asynchronous TRIZ and TRIZ/DFMA workflows. Use for engineering problem solving, concept alternatives, cost-reduction analysis, task-progress retrieval, and selected-solution details.