Agent Skills

12. 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 installation, training inputs, model selection, 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/.

12.1. List of skills#

  • deepmd-install: Select a pip, conda, dp1s, offline, Docker, or source installation path, load the official documentation matching the requested version, and verify the requested Python, C/C++, or LAMMPS interface. A compact failure-mode reference covers issues not resolved by the install pages.

  • deepmd-train: Choose a DeePMD-kit model family, then train from scratch. The skill uses progressive disclosure: the top-level workflow handles common training steps and model selection, while model-specific configuration lives under skills/deepmd-train/models/ and is read only after a model is chosen. Current references include DPA3 and se_e2_a.

  • 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-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.

12.3. Install skills#

To have an agent install DeePMD-kit itself, send Install with an AI agent and ask it to load deepmd-install.

12.3.1. 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.

12.3.2. 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 --skill '*' -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 \
    --skill '*' -y

If direct GitHub access fails, clone the official Gitee mirror and install from that checkout:

git clone --depth 1 \
    https://gitee.com/deepmodeling/deepmd-kit.git \
    deepmd-kit-skill-source
npx -y skills add ./deepmd-kit-skill-source/skills --skill '*' -y

The examples require Node.js/npm so that npx is available. The Skills CLI installs every official skill for the detected agent. To target one product, add its agent name, for example --agent cursor or --agent claude-code. The GitHub command lets the CLI fetch the repository; installing from an existing checkout avoids that download. Refresh or restart the session afterward so the installed skills are reloaded.

12.4. 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 skill to choose between DPA3 and se_e2_a for a small water dataset and draft a training input, 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.”

  • “Use the deepmd-install skill to install the current stable release with PyTorch and CUDA. Read the matching official documentation before choosing packages, then verify a tensor operation on the GPU.”