TimesFM Forecasting
google-research/timesfm
Forecasts any univariate time series zero-shot with Google's TimesFM model, returning point forecasts and calibrated prediction intervals without training.
Train DeePMD-kit models with progressive disclosure. An agent skill from jinzhezenggroup/computational-chemistry-agent-skills.
Install Deepmd Train
$ npx skills add jinzhezenggroup/computational-chemistry-agent-skills --skill deepmd-train -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install jinzhezenggroup/computational-chemistry-agent-skills deepmd-train --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/jinzhezenggroup/computational-chemistry-agent-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/machine-learning-potentials/deepmd-train .claude/skills/deepmd-train && rm -rf skills-srcUse ~/.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/
Install the "deepmd-train" agent skill from https://github.com/jinzhezenggroup/computational-chemistry-agent-skills/tree/master/machine-learning-potentials/deepmd-train into .claude/skills/deepmd-train/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deepmd-train", 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 install https://github.com/jinzhezenggroup/computational-chemistry-agent-skills/tree/master/machine-learning-potentials/deepmd-trainType 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.
$ npx skills add jinzhezenggroup/computational-chemistry-agent-skills --skill deepmd-train -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install jinzhezenggroup/computational-chemistry-agent-skills deepmd-train --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/jinzhezenggroup/computational-chemistry-agent-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/machine-learning-potentials/deepmd-train .agents/skills/deepmd-train && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "deepmd-train" agent skill from https://github.com/jinzhezenggroup/computational-chemistry-agent-skills/tree/master/machine-learning-potentials/deepmd-train into .agents/skills/deepmd-train/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deepmd-train", 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.
$ npx skills add jinzhezenggroup/computational-chemistry-agent-skills --skill deepmd-train -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install jinzhezenggroup/computational-chemistry-agent-skills deepmd-train --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/jinzhezenggroup/computational-chemistry-agent-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/machine-learning-potentials/deepmd-train .cursor/skills/deepmd-train && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "deepmd-train" agent skill from https://github.com/jinzhezenggroup/computational-chemistry-agent-skills/tree/master/machine-learning-potentials/deepmd-train into .cursor/skills/deepmd-train/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deepmd-train", 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 https://github.com/jinzhezenggroup/computational-chemistry-agent-skills.git --path machine-learning-potentials/deepmd-train--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add jinzhezenggroup/computational-chemistry-agent-skills --skill deepmd-train -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install jinzhezenggroup/computational-chemistry-agent-skills deepmd-train --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/jinzhezenggroup/computational-chemistry-agent-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/machine-learning-potentials/deepmd-train .gemini/skills/deepmd-train && rm -rf skills-srcUse ~/.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/
Install the "deepmd-train" agent skill from https://github.com/jinzhezenggroup/computational-chemistry-agent-skills/tree/master/machine-learning-potentials/deepmd-train into .gemini/skills/deepmd-train/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deepmd-train", 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.
$ gh skill install jinzhezenggroup/computational-chemistry-agent-skills deepmd-trainInstalls 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).
$ npx skills add jinzhezenggroup/computational-chemistry-agent-skills --skill deepmd-train -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/jinzhezenggroup/computational-chemistry-agent-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/machine-learning-potentials/deepmd-train .github/skills/deepmd-train && rm -rf skills-srcUse ~/.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/
Install the "deepmd-train" agent skill from https://github.com/jinzhezenggroup/computational-chemistry-agent-skills/tree/master/machine-learning-potentials/deepmd-train into .github/skills/deepmd-train/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deepmd-train", 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.
$ npx skills add jinzhezenggroup/computational-chemistry-agent-skills --skill deepmd-train -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install jinzhezenggroup/computational-chemistry-agent-skills deepmd-train --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/jinzhezenggroup/computational-chemistry-agent-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/machine-learning-potentials/deepmd-train .opencode/skills/deepmd-train && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "deepmd-train" agent skill from https://github.com/jinzhezenggroup/computational-chemistry-agent-skills/tree/master/machine-learning-potentials/deepmd-train into .opencode/skills/deepmd-train/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deepmd-train", 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-train- GitHub stars
- 148
- Token cost
- ~1.5k tokens
- SKILL.md length
- 537 words
- Files
- 3
- Skills in repo
- 62
- Repo updated
- First seen
- Licence
- LGPL-3.0-or-later
At a glance
Train DeePMD-kit models with progressive disclosure. An agent skill from jinzhezenggroup/computational-chemistry-agent-skills.
- Works in 6 steps: Confirm environment → Confirm training data → Read the selected model reference → …
- The user wants to train a DeePMD-kit potential
- SKILL.md covers Progressive disclosure protocol, Model selection, Common workflow and Agent checklist, plus 1 more section
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Deepmd Train is an agent skill from jinzhezenggroup/computational-chemistry-agent-skills. Train DeePMD-kit models with progressive disclosure. Use when the user wants to train a DeePMD-kit potential, prepare an input.json, choose between model families such as see2a/DeepPot-SE and DPA3, run dp train, monitor learning curves, freeze checkpoints, or test trained models. Start with model selection and read only the selected model reference under models/ when model-specific configuration is needed.
Its SKILL.md is about 1.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files (for example `models/dpa3.md` and `models/se-e2-a.md`). Compatibility notes: Requires deepmd-kit installed. The selected backend and model may require PyTorch, TensorFlow, JAX, Paddle, GPU support, or custom OP libraries.
It sits in Data & Analytics. The repository describes itself as: Agent skills to run computational-chemistry tasks, used in OpenClaw. The licence is LGPL-3.0-or-later.
When your agent uses it
- The user wants to train a DeePMD-kit potential
- Prepare an input.json
- Choose between model families such as see2a/DeepPot-SE and DPA3
- Monitor learning curves
Example prompts
- “/deepmd-train”
Requirements
- Python 3
- Compatibility (from SKILL.md): Requires deepmd-kit installed. The selected backend and model may require PyTorch, TensorFlow, JAX, Paddle, GPU support, or custom OP libraries.
Workflow steps
6 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 5c19e75. 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 bash).
From the folder's file list and the shell code blocks in SKILL.md.
Network
Links to these hosts (documentation or services it may open):
docs.deepmodeling.com
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. The selected backend and model may require PyTorch, TensorFlow, JAX, Paddle, GPU support, or custom OP libraries.
From compatibility in the SKILL.md frontmatter.
Context cost
Deepmd Train loads about 1.5k tokens when it runs. Until then it costs about 107 tokens; SKILL.md has 537 words of instructions outside code blocks.
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 jinzhezenggroup/computational-chemistry-agent-skills at commit 5c19e75, republished under its LGPL-3.0-or-later licence (© jinzhezenggroup). 537 words, ~1,457 tokens.
Download SKILL.mdSave it as
.claude/skills/deepmd-train/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.- name
- deepmd-train
- description
- Train DeePMD-kit models with progressive disclosure. Use when the user wants to train a DeePMD-kit potential, prepare an input.json, choose between model families such as se_e2_a/DeepPot-SE and DPA3, run `dp train`, monitor learning curves, freeze checkpoints, or test trained models. Start with model selection and read only the selected model reference under `models/` when model-specific configuration is needed.
- compatibility
- Requires deepmd-kit installed. The selected backend and model may require PyTorch, TensorFlow, JAX, Paddle, GPU support, or custom OP libraries.
- license
- LGPL-3.0-or-later
- metadata.author
- iProzd
- metadata.version
- 1.1
- metadata.repository
- https://github.com/deepmodeling/deepmd-kit
DeePMD-kit Training
Use this skill to guide DeePMD-kit model training without loading every model-specific recipe up front. The workflow is intentionally progressive:
- Understand the user's data, target accuracy, compute budget, and deployment backend.
- Choose an appropriate model family.
- Read only the reference file for the selected model under
models/. - Generate or edit
input.json, run training, monitor, freeze, and test.
Progressive disclosure protocol
Do not start by reading every model document. First classify the request:
- If the user already named a model, read only that model reference.
- If the user asks for a recommendation, collect the decision inputs below, choose a model, then read only the selected reference.
- If model-specific parameters are not needed yet, stay in this top-level workflow.
Available model references:
| Model reference | Read when |
|---|---|
models/se-e2-a.md | The user wants a classical DeepPot-SE baseline, broad compatibility, or a smaller/established production model. |
models/dpa3.md | The user wants a high-accuracy DPA3/LAM workflow, large/diverse datasets, dynamic neighbor selection, or pretrained DPA3-style training. |
Model selection
Ask only for missing information that changes the choice. Prefer reasonable defaults when the answer is obvious from context.
Key inputs:
- Data format and size: deepmd/npy, deepmd/hdf5, mixed type, number of systems/frames/elements.
- Target: quick baseline, production accuracy, large atomic model, transfer/fine-tuning, or deployment in MD.
- Compute: CPU/GPU, available memory, single-node vs. distributed training.
- Backend/deployment: PyTorch/TensorFlow/JAX/Paddle training; LAMMPS, Python inference, or other downstream use.
- Labels: energy/force only or also virial/stress.
- System diversity: single chemistry/phase vs. diverse multi-domain datasets.
Recommended defaults:
- Choose se_e2_a for a robust baseline, small to medium systems, compatibility-focused workflows, or when compute is limited.
- Choose DPA3 for high accuracy on diverse datasets, LAM-style training, or when the user explicitly asks for DPA3, DPA-3, LiGS, dynamic neighbor selection, or pretrained DPA3 variants.
Common workflow
1. Confirm environment
dp --versionFor PyTorch training, use dp --pt ...; for TensorFlow, use dp ...; for other backends, confirm the installed backend first.
Show full SKILL.md (227 more words)Show less
2. Confirm training data
Training data should be in DeePMD format, typically deepmd/npy or deepmd/hdf5. If the user has raw electronic-structure outputs, convert them first with dpdata before writing the training input.
Minimum information needed to build input.json:
type_map- training system paths
- validation system paths
- whether virial labels are present and should be trained
- target number of steps or accuracy/time budget
- model choice
3. Read the selected model reference
After selecting a model, read the corresponding file under models/ and apply its model-specific configuration, hyperparameters, and caveats.
4. Train
dp --pt train input.jsonUse the backend-specific command if not using PyTorch.
Restart from a checkpoint when needed:
dp --pt train input.json --restart model.ckpt.pt5. Monitor
Training progress is usually written to lcurve.out. Check for:
- decreasing validation RMSE
- NaN or exploding losses
- train/validation divergence
- learning-rate schedule behaving as expected
6. Freeze and test
dp --pt freeze -o model.pth
dp --pt test -m model.pth -s /path/to/test_system -n 30Adjust the backend flags and output extension for non-PyTorch models.
Agent checklist
- Model was selected before reading model-specific details.
- Only the selected model reference was loaded.
- Training/validation data paths exist or are clearly marked as placeholders.
-
type_mapmatches the data and model/pretrained checkpoint. - Virial loss is enabled only when virial labels are available and desired.
- Backend command matches the selected model and installed DeePMD-kit environment.
- The generated
input.jsonis valid JSON. - Training was monitored via
lcurve.outor equivalent logs. - Final model was frozen and tested when requested.
References
© jinzhezenggroup, LGPL-3.0-or-later. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 2 other files in machine-learning-potentials/deepmd-train of jinzhezenggroup/computational-chemistry-agent-skills.
- SKILL.md
- models/dpa3.md
- models/se-e2-a.md
Open the folder on GitHubat commit 5c19e75
Compare with similar skills
Deepmd Train 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Deepmd Train this skilljinzhezenggroup/computational-chemistry-agent-skills | 148 | — | ~1.5k | Automated safety check: Pass | LGPL-3.0-or-later | |
| TimesFM Forecastinggoogle-research/timesfm | 34k | — | ~4.7k | Automated safety check: Pass | Apache-2.0 | |
| AI Daily DigestvigorX777/ai-daily-digest | 1.6k | — | ~1.3k | Automated safety check: Pass | None | |
| Anomalib Adding A Modelopen-edge-platform/anomalib | 6.2k | — | ~1.9k | Automated safety check: Pass | Apache-2.0 | |
| Anomalib Tiled Ensembleopen-edge-platform/anomalib | 6.2k | — | ~1.4k | Automated safety check: Pass | Apache-2.0 | |
| Sae Feature Annotationssoftnanolab/bagel | 148 | — | ~1.5k | Automated safety check: Pass | MIT |
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Anomalib Adding A Model
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Adds a new anomaly-detection model to anomalib under src/anomalib/models/.
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Related
Categories
Questions about Deepmd Train
What does Deepmd Train do?
Train DeePMD-kit models with progressive disclosure. An agent skill from jinzhezenggroup/computational-chemistry-agent-skills. Deepmd Train is an agent skill from jinzhezenggroup/computational-chemistry-agent-skills. Train DeePMD-kit models with progressive disclosure.
When should I use Deepmd Train?
Deepmd Train fits situations like: the user wants to train a DeePMD-kit potential; prepare an input.json; choose between model families such as see2a/DeepPot-SE and DPA3; monitor learning curves.
How do I install Deepmd Train in Claude Code?
Run `npx skills add jinzhezenggroup/computational-chemistry-agent-skills --skill deepmd-train -a claude-code`. Or copy the skill folder (machine-learning-potentials/deepmd-train in jinzhezenggroup/computational-chemistry-agent-skills) into .claude/skills/deepmd-train in your project. Claude Code loads it when a task matches its description.
How do I install Deepmd Train in Codex?
Run `npx skills add jinzhezenggroup/computational-chemistry-agent-skills --skill deepmd-train -a codex`. Or copy the skill folder (machine-learning-potentials/deepmd-train in jinzhezenggroup/computational-chemistry-agent-skills) into .agents/skills/deepmd-train in your project. Codex loads it when a task matches its description.
Can I use Deepmd Train 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 jinzhezenggroup/computational-chemistry-agent-skills --skill deepmd-train -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-train, .gemini/skills/deepmd-train, .github/skills/deepmd-train and .opencode/skills/deepmd-train in your project.
What does Deepmd Train need to run?
SKILL.md names no scripts, command-line tools or credentials: Deepmd Train is instructions for the agent only. Our summary lists: Python 3. Compatibility (from SKILL.md): Requires deepmd-kit installed. The selected backend and model may require PyTorch, TensorFlow, JAX, Paddle, GPU support, or custom OP libraries..
Does Deepmd Train access the network?
SKILL.md names 1 domain. As links in the text: docs.deepmodeling.com. This is read from the text; nothing was executed.
Is Deepmd Train 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 Train use?
Deepmd Train is published under the LGPL-3.0-or-later licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Deepmd Train use?
About 1.5k tokens (SKILL.md is roughly 5.8k 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 Train?
Skills that share tags, products or a category with Deepmd Train: TimesFM Forecasting (google-research/timesfm, 34k stars), AI Daily Digest (vigorX777/ai-daily-digest, 1.6k stars), Anomalib Adding A Model (open-edge-platform/anomalib, 6.2k stars) and Anomalib Tiled Ensemble (open-edge-platform/anomalib, 6.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Deepmd Train?
jinzhezenggroup (a GitHub organization) maintains it in jinzhezenggroup/computational-chemistry-agent-skills, which has 148 GitHub stars. The repository holds 62 skills in this directory. The repository was last updated on October 9, 2026.
Source: jinzhezenggroup/computational-chemistry-agent-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.