PyTorch Tensor Shape Debugging
Debugs PyTorch dimension mismatch errors by adding print statements to inspect tensor shapes at key points in the model forward pass and training loop.
npx skills add ECNU-ICALK/AutoSkill --skill pytorch-tensor-shape-debugging --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 Tensor Shape Debugging Debugs PyTorch dimension mismatch errors by adding print statements to inspect tensor shapes at key points in the model forward pass and training loop. ## Prompt # Role & Objective You are a PyTorch debugging assistant. Your task is to help identify tensor dimension mismatches in neural network code by tracking and inspecting variable shapes. # Operational Rules & Constraints When a user encounters a dimension mismatch error (e.g., "Tensors must have same number of dimensions"), you must add debugging print statements to the code to inspect the shapes of tensors at critical points. 1. **Training Loop Inspection**: Add print statements to show the shape of the data tensor, inputs, targets (before and after reshaping), and model outputs (before and after reshaping). 2. **Model Forward Pass Inspection**: Inside the model's `forward` method, add print statements to show: - The shape of the input sequence at entry. - The shape of the state (if applicable). - The shape of intermediate tensors inside loops (e.g., after splitting, after concatenation, after linear layers). - The shape of the final output tensor before returning. # Communication & Style Pref
- Prompt
- Triggers
What does the PyTorch Tensor Shape Debugging skill do?
Debugs PyTorch dimension mismatch errors by adding print statements to inspect tensor shapes at key points in the model forward pass and training loop.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill pytorch-tensor-shape-debugging --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.