deepmd-python-inference SKILL.md skillA
deepmd-python-inference from jinzhezenggroup/computational-chemistry-agent-skills is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.
mdr add jinzhezenggroup/computational-chemistry-agent-skills/deepmd-python-inference@v1.1Pins v1.1 in mdr.lock. Pin the fingerprint instead.
- Audit grade
- A, 17 of 17 checks passed
- Kind
- SKILL.md skill
- Repository
- jinzhezenggroup/computational-chemistry-agent-skills
- Path
machine-learning-potentials/deepmd-python-inference/SKILL.md- Versions recorded
- 4
- Latest version
- v1.1, committed Oct 15, 2026
- First version recorded
- Mar 21, 2026
- Size
- 10,104 bytes
- Repository stars
- 156
- License
- LGPL-3.0
Run Python inference with DeePMD-kit models using the DeepPot API. Use when the user wants to load a checkpoint, frozen model (.pb or .pth), DPA4/SeZM AOTInductor deployment archive (.pt2), or built-in pretrained model in Python; predict energy/force/virial; evaluate supported descriptors; calculate model deviation; or use `dp test` against labeled data.
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
# DeePMD-kit Python Inference
Load a trained DeePMD-kit model in Python and predict energy, forces, and virial for atomic configurations. Also covers CLI-based testing with `dp test`.
## Quick Start
```python
from deepmd.infer import DeepPot
import numpy as np
dp = DeepPot("model.pth")
coord = np.array([[1, 0, 0], [0, 0, 1.5], [1, 0, 3]]).reshape([1, -1])
cell = np.diag(10 * np.ones(3)).reshape([1, -1])
atype = [1, 0, 1]
e, f, v = dp.eval(coord, cell, atype)
```
## Agent Responsibilities
1. Determine the model source:
- Frozen model file (`.pth` for conventional PyTorch, `.pb` for TensorFlow, or `.pt2` for DPA4/SeZM)
- Built-in pretrained model name (e.g., `DPA-3.2-5M`)
- PyTorch checkpoint (`.pt`), whose stored model configuration must be inspected before choosing an inference or export path
1. Read `references/model-artifacts.md` for `.pt`/`.pt2` models or whenever the artifact route is unclear.
1. Read `references/held-out-evaluation.md` for complete labeled evaluation used for checkpoint selection or production admission.
1. Determine the inference task:
- Single-frame prediction (energy, force, virial)
- Batch prediction over multiple frames
…Read the whole file at its exact version.
How to install
mdr add jinzhezenggroup/computational-chemistry-agent-skills/deepmd-python-inference@sha256:1257e5a6777e8488The line at the top of this page pins the label v1.1, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
[](https://markdownregistry.com/a/art_oaifnd24jhiqz5yd)
2 badge image fetches in 30 days. What the badge shows.
Versions
The registry has recorded 4 versions of this file, between Mar 21, 2026 and Oct 15, 2026, about one every 69 days.
| version | committed | commit | size | audit | |
|---|---|---|---|---|---|
| v1.1 latest | 2026-10-15 | eac2e82 | 10,104 B | A | view · diff |
| v1.0 | 2026-06-03 | 567c266 | 9,155 B | A | view · diff |
| v1.0 | 2026-03-23 | 622da72 | 9,107 B | A | view · diff |
| v1.0 | 2026-03-21 | 0a9651d | 9,098 B | A | view |
Audit of the latest version
- pass: Frontmatter block present
- pass: Frontmatter declares a name
- pass: Frontmatter declares a description
- pass: Size between 200 bytes and 200 KB (10104 bytes)
- pass: No zero-width or bidi control characters
- pass: No instruction hidden inside an HTML comment
- pass: No link to an exfiltration or paste host
- pass: No credential-shaped string
- pass: No instruction to send local credentials anywhere
- pass: No text hidden with inline styles
- pass: No prompt-injection phrasing
- pass: No curl or wget piped into a shell
- pass: No recursive delete of root, home or parent
- pass: No instruction to read or print local credentials
- pass: No base64 blob over 200 characters
- pass: No link to a raw IP address
- pass: No script tag
Source
jinzhezenggroup/computational-chemistry-agent-skills · 156 stars · license LGPL-3.0 · pushed 2026-11-02 · branch master
API
GET https://markdownregistry.com/api/v1/artifacts/art_oaifnd24jhiqz5yd GET https://markdownregistry.com/api/v1/resolve?ref=jinzhezenggroup/computational-chemistry-agent-skills/deepmd-python-inference GET https://markdownregistry.com/api/v1/blob/1257e5a6777e8488fc5dca8b6f7a605e2c7a068a42e870e94a04685ef13fb424
Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.
Guides: the SKILL.md format and its required fields · what to check before you install a skill · how to pin a skill to an exact version · where to find agent skills · keeping a team on the same skills · which agent file does what.
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