Pytorch Tensor Shape Debugging

What the source does

• FilesystemNone observedNo reads or writes in the source
• NetworkNone observedNo outbound calls in the source
• ShellNone observedNo commands run in the source
• DependenciesPinnedNothing floats
• SecretsNone foundNo keys in source

Five audit layers

Gates applied: no behavioural pass.

Audit 0.4.1 · source sha 04e912cff54b · 14 Oct 2026Report an issue or request a re-scan

• PublisherECNU-ICALK GitHub organizationOwner of the repository this skill is read from
• Source folderECNU-ICALK/AutoSkill/SkillBank/ConvSkill/english_gpt4_8_GLM4.7/pytorch-tensor-shape-debuggingThe exact folder we audited
• LicenceNot declaredAs the repository states it
• Commit pinned for this verdict04e912cff54bA new audit runs when the source changes
• Repository statusNot archivedLast push 157 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its description: Debugs PyTorch dimension mismatch errors by adding print statements to inspect tensor shapes at key points in the model forward pass and training loop.

---
id: "8b93986d-15e0-4a54-8a17-c966b075c738"
name: "PyTorch Tensor Shape Debugging"
description: "Debugs PyTorch dimension mismatch errors by adding print statements to inspect tensor shapes at key points in the model forward pass and training loop."
version: "0.1.0"
tags:
  - "pytorch"
  - "debugging"
  - "tensor shapes"
  - "dimension mismatch"
  - "neural networks"
triggers:
  - "track and inspect all the variables in the code"
  - "debug tensor shapes"
  - "figure out the source of the dimension problem"
  - "add print statements to check shapes"
---

# 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 Preferences
- Present the modified code with the added print statements clearly.
- Explain that these prints will help trace where the shape divergence occurs.

# Anti-Patterns
- Do not attempt to fix the code without first inspecting the shapes if the user specifically requests to "track and inspect all the variables".
- Do not remove existing logic unless it is clearly the cause of the error.

## Triggers

- track and inspect all the variables in the code
- debug tensor shapes
- figure out the source of the dimension problem
- add print statements to check shapes

Excerpt, first 2,349 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

git clone https://github.com/ECNU-ICALK/AutoSkill
git -C AutoSkill checkout 04e912cff54b

Works in

1. 2026-10-14
   ✓SAFE B 89 / 100

   first audit · source 04e912cff54b

Questions about Pytorch Tensor Shape Debugging

• github.com/ECNU-ICALK/AutoSkill/tree/main/SkillBank/ConvSkill/en — the audited source at commit 04e912cff54b and every finding aboveObserved · 14 Oct 2026

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