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Hpc Lammps

Organization
SciMate-AI
hpc-lammps

Generate, review, debug, and recover LAMMPS molecular dynamics input scripts. Use when working with LAMMPS command ordering, data files, force fields, ensembles, neighbor settings, thermo output, or common runtime errors such as lost atoms and non-numeric pressure.

Overview

PublisherSciMate-AI
RepositoryHPC-Skills
Skill namehpc-lammps
Stars
97
Forks
10
Bundled files
18
LicenseMIT
Links
Skill sourceRepository
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 18 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by SciMate-AI on GitHub. Read the source before you install it.

Installation

Install the Hpc Lammps AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/SciMate-AI/HPC-Skills/tree/main/skills/hpc-lammps
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/SciMate-AI/HPC-Skills.git /tmp/HPC-Skills
mkdir -p .claude/skills
cp -r /tmp/HPC-Skills/skills/hpc-lammps .claude/skills/hpc-lammps
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Hpc Lammps in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Hpc Lammps on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Hpc Lammps is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

HPC LAMMPS

Treat LAMMPS input generation as a strict staged workflow.

Start

  1. Read references/script-structure.md before editing any in.* file.
  2. Read references/units-neighbor-and-timestep.md when choosing units, timestep scale, and neighbor settings.
  3. Read references/data-forcefield-and-groups.md when using read_data, multiple atom types, or complex assembly workflows.
  4. Read references/forcefield-cookbook.md when choosing between Lennard-Jones, EAM/MEAM, Tersoff, ReaxFF, or hybrid styles.
  5. Read references/thermostat-and-barostat-matrix.md when selecting nve, nvt, npt, Langevin, or deforming-box workflows.
  6. Read references/input-deck-templates.md when assembling a fresh LJ, EAM, or molecule-style input script.
  7. Read references/potentials-and-ensembles.md when choosing force fields, thermostatting, or barostatting.
  8. Read references/output-and-restarts.md when designing machine-readable logs, dumps, averaging, or restart behavior.
  9. Read references/cluster-execution-playbook.md when staging a LAMMPS deck for scheduler-backed cluster execution.
  10. Read references/error-recovery.md when the log shows instability, missing potentials, or neighbor-list failures.

Work sequence

  1. Fix the unit system first. Everything downstream depends on it.
  2. Keep command ordering strict:
    • initialization
    • system definition
    • force fields and neighbor settings
    • minimization, velocity setup, fixes, output, run
  3. Decide whether the structure comes from procedural generation or read_data. Do not mix both box-building paths.
  4. Match pair_style and pair_coeff syntax to the actual potential family and atom-type mapping.
  5. Minimize before aggressive dynamics unless the user explicitly provides a relaxed starting structure.
  6. Keep one time-integration fix per atom group unless the combination is explicitly non-overlapping and intended.

Guardrails

  • Do not write read_data before units and atom_style.
  • Do not use a timestep copied from another unit system.
  • Do not launch NPT on a fragile fresh structure without an equilibration stage.
  • Do not ignore neighbor-list settings on hot or highly deforming runs.

Additional References

Load these on demand:

  • references/minimization-and-box-relax.md for energy minimization, pressure relaxation, and pre-production setup
  • references/computes-chunks-and-analysis.md for RDF, MSD, chunk workflows, and averaging outputs
  • references/variables-loops-and-control-flow.md for script automation, loops, branching, and multi-run control
  • references/units-fix-compute-matrix.md for unit-system, ensemble-fix, and analysis-command matrices
  • references/cluster-execution-playbook.md for launch style, output-volume control, and restart handoff on clusters

Reusable Templates

Use assets/templates/ when a concrete input-deck scaffold is needed, especially:

  • lj_fluid_minimal.in
  • eam_metal_minimal.in
  • lammps-mpi-slurm.sh

Outputs

Summarize:

  • unit system
  • structure source
  • force field and atom-type mapping
  • ensemble and damping choices
  • log-derived failure mode if the script is under repair

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Hpc Lammps AI skill do?

Generate, review, debug, and recover LAMMPS molecular dynamics input scripts. Use when working with LAMMPS command ordering, data files, force fields, ensembles, neighbor settings, thermo output, or common runtime errors such as lost atoms and non-numeric pressure.

Why use Hpc Lammps on TypingMind?

Because you install it once and use it with any model. Hpc Lammps is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Hpc Lammps in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/SciMate-AI/HPC-Skills/tree/main/skills/hpc-lammps. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Hpc Lammps?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Hpc Lammps?

As many as you like. As long as a model supports skills, you can use Hpc Lammps with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Hpc Lammps AI skill free?

Yes. It is published on GitHub by SciMate-AI under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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