Agent skill

Lammps Deepmd

by Hello-QM in Hello-QM/catgo-LRG

Run LAMMPS molecular dynamics with DeePMD-kit machine learning potentials.

AGPL-3.0Auto-check passedData & Analytics

Install Lammps Deepmd

skills CLI
$ npx skills add Hello-QM/catgo-LRG --skill lammps-deepmd -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install Hello-QM/catgo-LRG lammps-deepmd --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/Hello-QM/catgo-LRG.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/lammps-deepmd .claude/skills/lammps-deepmd && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
lammps-deepmd
GitHub stars
205
Used in
1 other repo
Token cost
~1k tokens
SKILL.md length
323 words
Files
1
Skills in repo
75
Repo updated
First seen
Licence
AGPL-3.0

At a glance

Run LAMMPS molecular dynamics with DeePMD-kit machine learning potentials.

  • Works in 3 steps: Verify structure → Create workflow → Add LAMMPS task
  • The user wants MD simulations driven by a trained DP model
  • SKILL.md covers When to Use, Prerequisites, Workflow Steps and LAMMPS Input Template — NVT, plus 5 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Lammps Deepmd is an agent skill from Hello-QM/catgo-LRG. Run LAMMPS molecular dynamics with DeePMD-kit machine learning potentials. Use when the user wants MD simulations driven by a trained DP model.

Its SKILL.md is about 1k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts. Compatibility notes: Requires LAMMPS compiled with the DEEPMD package. A frozen DeePMD model (.pb) is required.

It sits in Data & Analytics, covering Physical and earth sciences and Machine learning. The repository describes itself as: AI-driven workbench for computational materials science — interactive 3D structure viewer, natural-language CatBot assistant, visual DAG workflow engine, HPC job submission… The licence is AGPL-3.0.

When your agent uses it

  • The user wants MD simulations driven by a trained DP model
  • Tasks that involve Physical and earth sciences
  • Tasks that involve Machine learning

Example prompts

  • “/lammps-deepmd”

Requirements

  • Python 3
  • Compatibility (from SKILL.md): Requires LAMMPS compiled with the DEEPMD package. A frozen DeePMD model (.pb) is required.

Workflow steps

3 steps, taken from the step headings in SKILL.md.

  1. Verify structure
  2. Create workflow
  3. Add LAMMPS task

What it can do on your machine

Read from SKILL.md and the folder at commit fd6291b. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are python).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    Requires LAMMPS compiled with the DEEPMD package. A frozen DeePMD model (.pb) is required.

    From compatibility in the SKILL.md frontmatter.

Context cost

Lammps Deepmd loads about 1k tokens when it runs. Until then it costs about 39 tokens; SKILL.md has 323 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~39
When it runs · the whole SKILL.md, loaded when a task matches
~1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from Hello-QM/catgo-LRG at commit fd6291b, republished under its AGPL-3.0 licence (© Hello-QM). 323 words, ~1,020 tokens.

Download SKILL.mdSave it as .claude/skills/lammps-deepmd/SKILL.md (or your agent's skills folder).
name
lammps-deepmd
description
Run LAMMPS molecular dynamics with DeePMD-kit machine learning potentials. Use when the user wants MD simulations driven by a trained DP model.
compatibility
Requires LAMMPS compiled with the DEEPMD package. A frozen DeePMD model (.pb) is required.
catalog-hidden
true

LAMMPS + DeePMD Potential

When to Use

  • User wants to run MD with a trained DeePMD model
  • User needs large-scale MD (10K-1M atoms) at near-DFT accuracy
  • User wants to study diffusion, phase transitions, or surface reactions with ML potential
  • User has a frozen .pb model file

Prerequisites

  1. LAMMPS compiled with DEEPMD package (lmp -h | grep DEEPMD)
  2. Frozen DeePMD model file (.pb)
  3. Initial structure (LAMMPS data file or from CatGo viewer)
  4. Know the type_map used during model training

Workflow Steps

1. Verify structure
catgo_view(action="get_state")
2. Create workflow
catgo_workflow_engine(action="create", params={"name": "LAMMPS DeePMD NVT 300K"})
3. Add LAMMPS task
catgo_workflow_engine(action="add_task", params={
  "workflow_id": "wf_xxx",
  "task_type": "shell",
  "name": "lmp_dpmd",
  "command": "lmp -in lammps.in > lammps.log 2>&1",
  "input_files": {
    "lammps.in": "<input script>",
    "frozen_model.pb": "{{model_path}}"
  },
  "system_name": "TiO2_md"
})

LAMMPS Input Template — NVT

units           metal
boundary        p p p
atom_style      atomic

read_data       structure.lmp

pair_style      deepmd frozen_model.pb
pair_coeff      * *

neighbor        2.0 bin
neigh_modify    every 1 delay 0 check yes

# Velocities
velocity        all create 300.0 12345 dist gaussian

# NVT thermostat
fix             1 all nvt temp 300.0 300.0 0.1

# Timestep (ps in metal units)
timestep        0.001

# Output
thermo          100
thermo_style    custom step temp pe ke etotal press vol

dump            1 all custom 100 traj.lammpstrj id type x y z fx fy fz
dump_modify     1 sort id

# Restart
restart         10000 restart.*.data

run             100000

LAMMPS Input Template — NPT

Replace the fix line:

fix             1 all npt temp 300.0 300.0 0.1 iso 0.0 0.0 1.0

Model Deviation (Multi-Model)

For active learning or reliability checking, use multiple models:

pair_style      deepmd model_0.pb model_1.pb model_2.pb model_3.pb out_freq 100 out_file model_devi.out
pair_coeff      * *

This writes model_devi.out with per-frame max/min/avg force deviation. Use thresholds:

  • max_devi_f < 0.05 eV/Ang: model is reliable
  • 0.05 < max_devi_f < 0.15: candidate for active learning
  • max_devi_f > 0.15: model is unreliable, do not trust results

Preparing LAMMPS Data File

Convert from CatGo structure to LAMMPS data format:

python
from ase.io import read, write
# Read pymatgen dict, write LAMMPS data
atoms = read('structure.json')
write('structure.lmp', atoms, format='lammps-data')

Or use dpdata (see data/dpdata/SKILL.md).

Parameter Guidance

ParameterTypical valueNotes
timestep0.001 ps (1 fs)Metal units; can use 2 fs for stiff systems
NVT temp damp0.1 psNose-Hoover damping; 100x timestep
NPT press damp1.0 psPressure damping; 1000x timestep
dump frequency100-1000Every 100 steps = 0.1 ps
neighbor skin2.0 AngRebuild neighbor list threshold
run100K-10MDepends on property of interest

Common Pitfalls

  1. Wrong units — DeePMD pair_style requires units metal (eV, Ang, ps). Never use units real.
  2. type_map mismatch — atom types in LAMMPS data file must match the order in the DP model's type_map.
  3. Unfrozen model — pair_style deepmd needs a frozen .pb file. Run dp freeze first.
  4. Too large timestep — 1 fs is safe; 2 fs may cause energy drift for light elements (H).
  5. No equilibration — always equilibrate for 10-50 ps before production run. Discard equilibration data.
  6. Memory for large models — GPU memory limits apply. For 1M+ atoms, use CPU or multi-GPU.

© Hello-QM, AGPL-3.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .claude/skills/lammps-deepmd of Hello-QM/catgo-LRG.

Open the folder on GitHubat commit fd6291b

Used in 1 other repository

We found 3 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in Hello-QM/catgo-LRG, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Lammps Deepmd next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Lammps Deepmd compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Lammps Deepmd this skillHello-QM/catgo-LRG2051 repos~1kAutomated safety check: PassAGPL-3.0
LammpsJCLiuGroup/AI-Computational-Chemist1451 repos~539Automated safety check: PassCustom licence
Optimize For GPUmajiayu000/claude-skill-registry6661 repos~8.5kAutomated safety check: PassMIT
DP-GEN Simplify Workflowjinzhezenggroup/computational-chemistry-agent-skills148—~2.7kAutomated safety check: PassLGPL-3.0-or-later
Journal Of Climatebrycewang-stanford/Awesome-Journal-Skills1.2k—~1.8kAutomated safety check: PassMIT
scikit-survival Time-to-Event Modelingdavila7/claude-code-templates32k12 repos~3.7kAutomated safety check: PassMIT

Similar skills

  • Lammps

    JCLiuGroup/AI-Computational-Chemist

    Prepare, run, and validate LAMMPS molecular dynamics. An agent skill from JCLiuGroup/AI-Computational-Chemist.

    145 GitHub starsUsed in 1 repo~539 tokens
    Data & AnalyticsAuto-check passed
  • Optimize For GPU

    majiayu000/claude-skill-registry

    GPU-accelerate Python code using CuPy, Numba CUDA, Warp, cuDF, cuML, cuGraph, KvikIO, cuCIM, cuxfilter, cuVS, cuSpatial, and RAFT.

    666 GitHub starsUsed in 1 repo~8.5k tokens
    Data & AnalyticsAuto-check passed
  • DP-GEN Simplify Workflow

    jinzhezenggroup/computational-chemistry-agent-skills

    Prepares, validates and runs DP-GEN simplify jobs that thin out repeated or redundant DeepMD datasets, generating param.json and machine.json for local or scheduler runs.

    148 GitHub stars~2.7k tokensUpdated 3 days ago
    Research & ScienceAuto-check passed
  • Journal Of Climate

    brycewang-stanford/Awesome-Journal-Skills

    A skill your agent uses when targeting Journal of Climate or deciding whether a climate-dynamics or climate-variability manuscript fits this venue.

    1.2k GitHub stars~1.8k tokensUpdated 11 days ago
    Research & ScienceAuto-check passed
  • scikit-survival Time-to-Event Modeling

    davila7/claude-code-templates

    Fits and evaluates survival models with scikit-survival: Cox models, Random Survival Forests, boosting, survival SVMs, concordance index, Brier score and competing risks.

    32k GitHub starsUsed in 12 repos~3.7k tokens
    Data & AnalyticsAuto-check passed
  • Neuropixels Analysis

    K-Dense-AI/scientific-agent-skills

    Analyzes Neuropixels extracellular recordings end-to-end with SpikeInterface.

    48k GitHub starsUsed in 1 repo~5k tokens
    Data & AnalyticsAuto-check passed

More from Hello-QM/catgo-LRG

All 75 skills in this repo
  • Campaign Md Orchestration

    Hello-QM/catgo-LRG

    Drive a file-first, agent-in-the-loop computational campaign via a folder + markdown tree (no DB).

    205 GitHub stars~1.6k tokensUpdated 16 days ago
    Auto-check passed
  • Catgo Gibbs Pipeline

    Hello-QM/catgo-LRG

    Compute adsorption/reaction Gibbs free energies, free-energy diagrams, and electrochemical overpotentials (HER/ORR/OER/CO2RR/NRR) with VASP.

    205 GitHub stars~669 tokensUpdated 16 days ago
    Auto-check passed
  • Abinit

    Hello-QM/catgo-LRG

    Generate and manage ABINIT DFT calculations. An agent skill from Hello-QM/catgo-LRG.

    205 GitHub stars~963 tokensUpdated 16 days ago
    Auto-check passed
  • Adsorbate Placement

    Hello-QM/catgo-LRG

    A skill your agent uses when the user asks to place an adsorbate molecule on a surface, find adsorption sites, or set up a surface+adsorbate model for DFT.

    205 GitHub stars~3k tokensUpdated 16 days ago
    Auto-check passed
  • Adsorption Energy

    Hello-QM/catgo-LRG

    A skill your agent uses when the user asks for adsorption energy, binding energy, or wants to compare how strongly a molecule binds to a surface.

    205 GitHub stars~1.4k tokensUpdated 16 days ago
    Auto-check passed
  • Analysis Router

    Hello-QM/catgo-LRG

    A skill your agent uses when the user asks to analyze computational results: Gibbs free energy, OER/HER/CO2RR overpotentials, adsorption energy, convergence tests, DOS/d-band analysis, or Bader…

    205 GitHub stars~869 tokensUpdated 16 days ago
    Auto-check passed

Questions about Lammps Deepmd

What does Lammps Deepmd do?

Run LAMMPS molecular dynamics with DeePMD-kit machine learning potentials. Lammps Deepmd is an agent skill from Hello-QM/catgo-LRG. Run LAMMPS molecular dynamics with DeePMD-kit machine learning potentials.

When should I use Lammps Deepmd?

Lammps Deepmd fits situations like: the user wants MD simulations driven by a trained DP model; tasks that involve Physical and earth sciences; tasks that involve Machine learning.

How do I install Lammps Deepmd in Claude Code?

Run `npx skills add Hello-QM/catgo-LRG --skill lammps-deepmd -a claude-code`. Or copy the skill folder (.claude/skills/lammps-deepmd in Hello-QM/catgo-LRG) into .claude/skills/lammps-deepmd in your project. Claude Code loads it when a task matches its description.

How do I install Lammps Deepmd in Codex?

Run `npx skills add Hello-QM/catgo-LRG --skill lammps-deepmd -a codex`. Or copy the skill folder (.claude/skills/lammps-deepmd in Hello-QM/catgo-LRG) into .agents/skills/lammps-deepmd in your project. Codex loads it when a task matches its description.

Can I use Lammps Deepmd in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add Hello-QM/catgo-LRG --skill lammps-deepmd -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/lammps-deepmd, .gemini/skills/lammps-deepmd, .github/skills/lammps-deepmd and .opencode/skills/lammps-deepmd in your project.

What does Lammps Deepmd need to run?

SKILL.md names no scripts, command-line tools or credentials: Lammps Deepmd is instructions for the agent only. Our summary lists: Python 3. Compatibility (from SKILL.md): Requires LAMMPS compiled with the DEEPMD package. A frozen DeePMD model (.pb) is required. .

Does Lammps Deepmd access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Lammps Deepmd safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Lammps Deepmd use?

Lammps Deepmd is published under the AGPL-3.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Lammps Deepmd use?

About 1k tokens (SKILL.md is roughly 4.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Lammps Deepmd?

Skills that share tags, products or a category with Lammps Deepmd: Lammps (JCLiuGroup/AI-Computational-Chemist, 145 stars), Optimize For GPU (majiayu000/claude-skill-registry, 666 stars), DP-GEN Simplify Workflow (jinzhezenggroup/computational-chemistry-agent-skills, 148 stars) and Journal Of Climate (brycewang-stanford/Awesome-Journal-Skills, 1.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Lammps Deepmd?

Hello-QM (a GitHub user) maintains it in Hello-QM/catgo-LRG, which has 205 GitHub stars. The repository holds 75 skills in this directory. The repository was last updated on September 22, 2026.

Source: Hello-QM/catgo-LRG on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.