---

name: deepmd-kit description: "Route DeePMD-kit package workflows for installation, data/config preparation, training, inference/model operations, integrations, and repository development." disable-model-invocation: true metadata: disco-role: operating license: LGPL 3.0

DeePMD-kit Repo Skill

Use this skill when the user is working with DeePMD-kit: installing the package, preparing DeePMD data/configs, training or fine-tuning potentials, running inference/model operations, integrating with MD engines/native APIs, or maintaining the repository.

DeePMD-kit is a Python/C++ package for deep learning interatomic potentials and molecular dynamics. It supports TensorFlow, PyTorch, JAX, Paddle, and DP/reference backend routes, plus integrations with LAMMPS, i-PI, C/C++/Node.js, dpdata, and ASE.

Start Here

1. Identify whether the task is package use, data/config preparation, training, inference/model operations, integration/deployment, or repository maintenance.

2. Confirm the installed runtime before executing commands:

   python -c "import deepmd; print(deepmd.__version__)"
   dp -h
   

3. Use explicit backend flags when the backend matters: dp --tf, dp --pt, dp --jax, dp --pd, or dp --backend pytorch-exportable.

4. Avoid long training, C++ builds, downloads, or native MD runs unless the user asks for execution or approves the cost.

5. Read references/repo-provenance.md when deciding whether this skill matches a current checkout or should be refreshed.

Route by Task

User task	Read
Install DeePMD-kit, choose pip/conda/docker/source build, diagnose missing backend, validate dp availability	sub-skills/installation-backends/SKILL.md
Inspect type.raw, type_map.raw, set.*, labels, mixed-type systems, LMDB/HDF5/npy layouts, or draft/repair training JSON/YAML	sub-skills/data-config/SKILL.md
Choose a model family, draft dp train, monitor lcurve.out, restart/fine-tune, handle pretrained/multi-task, or freeze after training	sub-skills/training-models/SKILL.md
Use DeepPot, run dp test, eval-desc, embed, model-devi, show, compress, convert-*, pretrained, or change-bias	sub-skills/inference-model-ops/SKILL.md
Write LAMMPS/i-PI inputs, use C/C++/Node/dpdata/ASE APIs, build native components, or maintain this checkout	sub-skills/integrations-development/SKILL.md

Cross-Cutting References

• references/cli-overview.md summarizes verified dp backend aliases and subcommands.
• references/troubleshooting.md maps common symptoms to the right sub-skill and first checks.
• references/repo-routing-metadata.json contains structured import metadata for repo-skills-router.

Common Command Map

• Install/runtime check: dp --version, dp -h, dp --pt -h.
• Training: dp --tf train input.json, dp --pt train input.json, dp --pd train input.json.
• Freeze: dp freeze -o model.pb, dp --pt freeze -o model.pth, dp --pd freeze -o model.
• Test/inference: dp test -m model.pb -s system -n 30, dp eval-desc -m model.pb -s system -o desc.
• Model ops: dp model-devi, dp compress, dp convert-from, dp convert-backend, dp show, dp pretrained download.
• Integration: generate LAMMPS input with sub-skills/integrations-development/scripts/write_lammps_deepmd_input.py.

Safety and Validation

• Use bounded help/parser/import checks before running long workflows.
• Treat repository examples and tests as evidence; do not rely on original checkout paths in generated user-facing workflows.
• Training examples are small validation fixtures, not production datasets.
• Prefer focused tests such as a single pytest target over the full suite when maintaining the checkout.
• For source/C++ builds, use generous timeouts and do not cancel known long build operations.

Existing Focused Skills

This generated repo skill is a self-contained router for DeePMD-kit package and repository work. The source repository also contains narrower official skills for training, DPA3 fine-tuning, Python inference, and LAMMPS; their concepts were distilled here, but runtime use of this generated skill does not require the original repository skills to remain available.