Agent skill

Deepmd Inference

Run DeePMD-kit inference to predict energies, forces, and stresses using a trained DP model.

AGPL-3.0Auto-check passedData & Analytics

Install Deepmd Inference

skills CLI

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

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

GitHub CLI

$ gh skill install Hello-QM/catgo-LRG deepmd-inference --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/deepmd-inference .claude/skills/deepmd-inference && 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/

Paste into Claude Code

Install the "deepmd-inference" agent skill from https://github.com/Hello-QM/catgo-LRG/tree/main/.claude/skills/deepmd-inference into .claude/skills/deepmd-inference/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deepmd-inference", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/Hello-QM/catgo-LRG/tree/main/.claude/skills/deepmd-inference

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add Hello-QM/catgo-LRG --skill deepmd-inference -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install Hello-QM/catgo-LRG deepmd-inference --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

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

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "deepmd-inference" agent skill from https://github.com/Hello-QM/catgo-LRG/tree/main/.claude/skills/deepmd-inference into .agents/skills/deepmd-inference/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deepmd-inference", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add Hello-QM/catgo-LRG --skill deepmd-inference -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install Hello-QM/catgo-LRG deepmd-inference --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

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

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "deepmd-inference" agent skill from https://github.com/Hello-QM/catgo-LRG/tree/main/.claude/skills/deepmd-inference into .cursor/skills/deepmd-inference/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deepmd-inference", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/Hello-QM/catgo-LRG.git --path .claude/skills/deepmd-inference

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add Hello-QM/catgo-LRG --skill deepmd-inference -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install Hello-QM/catgo-LRG deepmd-inference --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

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

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "deepmd-inference" agent skill from https://github.com/Hello-QM/catgo-LRG/tree/main/.claude/skills/deepmd-inference into .gemini/skills/deepmd-inference/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deepmd-inference", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install Hello-QM/catgo-LRG deepmd-inference

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add Hello-QM/catgo-LRG --skill deepmd-inference -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

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

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "deepmd-inference" agent skill from https://github.com/Hello-QM/catgo-LRG/tree/main/.claude/skills/deepmd-inference into .github/skills/deepmd-inference/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deepmd-inference", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add Hello-QM/catgo-LRG --skill deepmd-inference -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install Hello-QM/catgo-LRG deepmd-inference --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

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

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "deepmd-inference" agent skill from https://github.com/Hello-QM/catgo-LRG/tree/main/.claude/skills/deepmd-inference into .opencode/skills/deepmd-inference/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deepmd-inference", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: deepmd-inference
GitHub stars: 205
Token cost: ~945 tokens
SKILL.md length: 261 words
Files: 1
Skills in repo: 75
Repo updated: Sep 22, 2026
First seen: Oct 7, 2026
Licence: AGPL-3.0

At a glance

Run DeePMD-kit inference to predict energies, forces, and stresses using a trained DP model.

Works in 2 steps: Model Testing (against reference data) → Single Structure Prediction (Python)
Tasks that involve Machine learning
SKILL.md covers When to Use, Prerequisites, Workflow Steps and Python Script — Single…, plus 5 more sections
Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Deepmd Inference is an agent skill from Hello-QM/catgo-LRG. Run DeePMD-kit inference to predict energies, forces, and stresses using a trained DP model. Also covers model evaluation and testing.

Its SKILL.md is about 950 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 deepmd-kit installed. A frozen model (.pb) file is needed.

It sits in Data & Analytics, covering Machine learning. It works with Python. 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

Tasks that involve Machine learning

Example prompts

“/deepmd-inference”

Requirements

Python 3
Compatibility (from SKILL.md): Requires deepmd-kit installed. A frozen model (.pb) file is needed.

Workflow steps

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

Model Testing (against reference data)
Single Structure Prediction (Python)

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 and bash).
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 deepmd-kit installed. A frozen model (.pb) file is needed.
From compatibility in the SKILL.md frontmatter.

Context cost

Deepmd Inference loads about 945 tokens when it runs. Until then it costs about 38 tokens; SKILL.md has 261 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~38
When it runs · the whole SKILL.md, loaded when a task matches: ~945

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). 261 words, ~945 tokens.

Download SKILL.mdSave it as .claude/skills/deepmd-inference/SKILL.md (or your agent's skills folder).

name: deepmd-inference
description: Run DeePMD-kit inference to predict energies, forces, and stresses using a trained DP model. Also covers model evaluation and testing.
compatibility: Requires deepmd-kit installed. A frozen model (.pb) file is needed.
catalog-hidden: true

DeePMD Inference

When to Use

User wants to predict energy/forces for a structure using a trained DP model
User wants to evaluate model accuracy against DFT reference data
User wants to use a DP model as an ASE calculator for optimization or NEB

Prerequisites

A frozen DeePMD model file (.pb or .savedmodel)
deepmd-kit installed (dp --version)
Structure to predict on, or test data in dpdata format

Workflow Steps

1. Model Testing (against reference data)

catgo_workflow_engine(action="add_task", params={
  "workflow_id": "wf_xxx",
  "task_type": "shell",
  "name": "dp_test",
  "command": "dp test -m frozen_model.pb -s ./data/test -n 100 -d test_results 2>&1 | tee test.log",
  "system_name": "dp_eval"
})

This outputs RMSE for energy, forces, and virial.

2. Single Structure Prediction (Python)

catgo_workflow_engine(action="add_task", params={
  "workflow_id": "wf_xxx",
  "task_type": "shell",
  "name": "dp_predict",
  "command": "python predict.py",
  "input_files": {
    "predict.py": "<script content>"
  },
  "system_name": "dp_predict"
})

Python Script — Single Prediction

python

from deepmd.infer import DeepPot
from ase.io import read
import numpy as np

dp = DeepPot("frozen_model.pb")
atoms = read("structure.vasp")

coord = atoms.get_positions().reshape(1, -1)
cell = atoms.get_cell().array.reshape(1, -1)
atype = [dp.get_type_map().index(s) for s in atoms.get_chemical_symbols()]

energy, force, virial = dp.eval(coord, cell, atype)

print(f"Energy: {energy[0][0]:.6f} eV")
print(f"Max force: {np.max(np.abs(force)):.6f} eV/Ang")

Python Script — ASE Calculator

python

from deepmd.calculator import DP
from ase.io import read, write
from ase.optimize import BFGS

atoms = read("structure.vasp")
atoms.calc = DP(model="frozen_model.pb")

# Single point
energy = atoms.get_potential_energy()
forces = atoms.get_forces()
print(f"Energy: {energy:.6f} eV")

# Optimization
opt = BFGS(atoms, trajectory="opt.traj")
opt.run(fmax=0.01)
write("optimized.vasp", atoms)

dp test Output Format

Energy RMSE        : 1.234e-03 eV/atom
Force  RMSE        : 2.345e-02 eV/Ang
Virial RMSE        : 3.456e-01 eV/cell

Acceptable thresholds:

Energy: < 5 meV/atom
Force: < 100 meV/Ang (< 50 meV/Ang for high accuracy)
Virial: < 1 kbar

Model Compression (for faster inference)

bash

dp compress -i frozen_model.pb -o compressed_model.pb

Compressed models are 3-10x faster with minimal accuracy loss. Always compress before production MD.

Parameter Guidance

Parameter	Notes
`-m`	Path to frozen model (.pb)
`-s`	Path to test data directory (dpdata format)
`-n`	Number of test frames (default: all)
`-d`	Output directory for detailed results
`--atomic`	Output per-atom energy decomposition

Common Pitfalls

type_map mismatch — the element order in inference must match training. Check with dp show-type-map frozen_model.pb.
Unfrozen model — dp test and ASE calculator need a frozen .pb file, not the training checkpoint directory.
Extrapolation — DP models are unreliable outside the training data distribution. Check the model deviation.
Model deviation — for production use, train 4 models with different seeds and compute max_devi_f to detect extrapolation.
Memory for large systems — DP inference on 10K+ atoms can exceed GPU memory. Use --batch-size or CPU inference.

Files

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

Open the folder on GitHubat commit fd6291b

Compare with similar skills

Deepmd Inference 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.

Deepmd Inference compared with similar skills
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Deepmd Inference this skillHello-QM/catgo-LRG	205	—	~945	Automated safety check: Pass	AGPL-3.0	16 days ago
Scikit LearnzLanqing/codex-claude-academic-skills	4.6k	17 repos	~3.9k	Automated safety check: Pass	BSD-3-Clause	4 mo ago
Senior Data ScientistRaidriar7170/hermes-skilleval	125	6 repos	~1.4k	Automated safety check: Pass	MIT	12 days ago
Geomlitalo-goncalves/geoML	109	—	~4.2k	Automated safety check: Pass	GPL-3.0	6 days ago
QuantMind Training Config Generatorqusong0627/QuantMind	1.7k	—	~1.5k	Automated safety check: Pass	AGPL-3.0	today
Time Series Analytics Useropen-edge-platform/edge-ai-libraries	169	—	~3.1k	Automated safety check: Pass	Apache-2.0	today

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Questions about Deepmd Inference

What does Deepmd Inference do?

Run DeePMD-kit inference to predict energies, forces, and stresses using a trained DP model. Deepmd Inference is an agent skill from Hello-QM/catgo-LRG. Run DeePMD-kit inference to predict energies, forces, and stresses using a trained DP model.

When should I use Deepmd Inference?

Deepmd Inference fits situations like: tasks that involve Machine learning.

How do I install Deepmd Inference in Claude Code?

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

How do I install Deepmd Inference in Codex?

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

Can I use Deepmd Inference 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 deepmd-inference -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/deepmd-inference, .gemini/skills/deepmd-inference, .github/skills/deepmd-inference and .opencode/skills/deepmd-inference in your project.

What does Deepmd Inference need to run?

SKILL.md names no scripts, command-line tools or credentials: Deepmd Inference is instructions for the agent only. Our summary lists: Python 3. Compatibility (from SKILL.md): Requires deepmd-kit installed. A frozen model (.pb) file is needed. .

Does Deepmd Inference 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 Deepmd Inference 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 Deepmd Inference use?

Deepmd Inference 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 Deepmd Inference use?

About 945 tokens (SKILL.md is roughly 3.8k 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 Deepmd Inference?

Skills that share tags, products or a category with Deepmd Inference: Scikit Learn (zLanqing/codex-claude-academic-skills, 4.6k stars), Senior Data Scientist (Raidriar7170/hermes-skilleval, 125 stars), Geoml (italo-goncalves/geoML, 109 stars) and QuantMind Training Config Generator (qusong0627/QuantMind, 1.7k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Deepmd Inference?

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.

Install Deepmd Inference

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Compatibility

Context cost

Safety

SKILL.md

DeePMD Inference

When to Use

Prerequisites

Workflow Steps

1. Model Testing (against reference data)

2. Single Structure Prediction (Python)

Python Script — Single Prediction

Python Script — ASE Calculator

dp test Output Format

Model Compression (for faster inference)

Parameter Guidance

Common Pitfalls

Files

Compare with similar skills

Similar skills

More from Hello-QM/catgo-LRG

Related

Questions about Deepmd Inference