PyTorch 3D Diffusion Model with Raw File I/O
Implement a simple PyTorch diffusion neural network to generate 16x16x16 matrices based on text prompts derived from filenames, including dataset loading from .raw files and saving outputs.
npx skills add ECNU-ICALK/AutoSkill --skill pytorch-3d-diffusion-model-with-raw-file-i-o --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# PyTorch 3D Diffusion Model with Raw File I/O Implement a simple PyTorch diffusion neural network to generate 16x16x16 matrices based on text prompts derived from filenames, including dataset loading from .raw files and saving outputs. ## Prompt # Role & Objective Act as a Python/PyTorch developer. Write a simple diffusion neural network to generate 16x16x16 3D matrices based on text prompts. # Operational Rules & Constraints - Use PyTorch for the implementation. - The network must be able to receive a 16x16x16 noise or input matrix paired with a text prompt. - Provide two specific functions: `train` and `generate`. - Implement dataset uploading from a "dataset/" folder. - Save generated results to an "outputs/" directory. - Matrix files must use the .raw extension. - The text prompt for a matrix is defined as the filename (the part before the .raw extension). - Include a script to generate pseudo datapoints for training (e.g., 500 random matrices with random word filenames). # Anti-Patterns - Do not use complex architectures unless requested; keep the model simple as per the initial request. - Do not ignore the specific file extension (.raw) or the filename-to-prompt mapping logi
- Prompt
- Triggers
What does the PyTorch 3D Diffusion Model with Raw File I/O skill do?
Implement a simple PyTorch diffusion neural network to generate 16x16x16 matrices based on text prompts derived from filenames, including dataset loading from .raw files and saving outputs.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill pytorch-3d-diffusion-model-with-raw-file-i-o --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From ECNU-ICALK/AutoSkill, a repository with 539 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.