11. Agent Skills

DeePMD-kit provides official Agent Skills that help AI agents run DeePMD-kit workflows in a reproducible way. These skills capture project-specific operating knowledge—such as training inputs, model deployment, LAMMPS integration, and Python inference patterns—so an agent can turn a high-level request into concrete files, commands, and validation steps.

The DeePMD-kit skills were initially developed in the Computational Chemistry Agent Skills project as part of the work described below. They are now maintained directly in the DeePMD-kit repository under skills/.

11.1. List of skills

• deepmd-train-dpa3: Train DeePMD-kit models with the DPA3 descriptor and the PyTorch backend, including input generation, neighbor-selection choices, training, freezing, and testing.

• deepmd-finetune-dpa3: Fine-tune DPA3 models from self-trained checkpoints, multi-task pretrained models, or built-in models downloaded by dp pretrained download.

• deepmd-train-se-e2-a: Train classical Deep Potential models with the se_e2_a descriptor, including preparation of training JSON files and post-training validation.

• deepmd-python-inference: Run Python and CLI inference with trained or frozen DeePMD-kit models, including energy, force, virial, descriptor, and model-deviation workflows.

• lammps-deepmd: Prepare, explain, and run LAMMPS simulations with DeePMD-kit potentials, including common NVE, NVT, and NPT setups.

11.2. Related reference

The DeePMD-kit skills were originally developed as part of the following paper:

• Mingwei Ding, Chen Huang, Yibo Hu, Yifan Li, Zitian Lu, Xingtai Yu, Duo Zhang, Wenxi Zhai, Tong Zhu, Qiangqiang Gu, and Jinzhe Zeng. Automating Computational Chemistry Workflows via OpenClaw and Domain-Specific Skills. Journal of Chemical Theory and Computation, 2026.

11.3. Install skills

11.3.1. If you are a user

The easiest way is to send this page to your agent and ask it to install the skills for you. Users usually do not need to perform manual installation.

11.3.2. If you are an agent

If you already have a DeePMD-kit checkout, run this command from the repository root:

npx -y skills add ./skills -a openclaw -y

If you do not have a checkout, the same skills can also be installed directly from GitHub:

npx -y skills add https://github.com/deepmodeling/deepmd-kit/tree/master/skills \
    -a openclaw -y

The examples above require Node.js/npm so that npx is available, and they install the skills for OpenClaw. Replace openclaw with the target agent name when installing for another agent. The GitHub command lets the skill CLI fetch the repository for you. For large repositories or slow networks, this can take longer than installing from an existing local checkout. Refresh or restart the session afterward so the installed skills are reloaded.

11.4. Minimal verification

Ask the agent to perform a small task that exercises the installed skill without launching an expensive calculation. For example:

• “Use the deepmd-python-inference skill to write a minimal Python snippet for loading a frozen DeePMD-kit model and evaluating one frame.”

• “Use the deepmd-train-dpa3 skill to draft a small DPA3 training input for a water dataset, but do not start training.”

• “Use the lammps-deepmd skill to prepare an NVT LAMMPS input file for a DeePMD-kit model, and explain each command.”