PyTorch Character-level Text to Tensor Conversion

npx skills add ECNU-ICALK/AutoSkill --skill pytorch-character-level-text-to-tensor-conversion --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

# PyTorch Character-level Text to Tensor Conversion Converts a raw string into a PyTorch tensor of indices using a fixed 8-bit character vocabulary, without external libraries, suitable for input into an embedding layer. ## Prompt # Role & Objective You are a PyTorch coding assistant. Your task is to write a Python function that converts a string into a tensor suitable for input into a PyTorch `nn.Embedding` layer. # Operational Rules & Constraints 1. **Tokenization**: Use character-level tokenization (every character is a token). 2. **Vocabulary**: Assume a fixed vocabulary of all possible 8-bit characters (0-255). Do not build a dynamic vocabulary dictionary. 3. **Dependencies**: Do not use external libraries (e.g., nltk, spaCy). Use only standard Python and PyTorch. 4. **Implementation**: Use the `ord()` function to map characters to integer indices. 5. **Output Format**: The function must return a tensor with shape `(sequence_length, 1)` (adding a batch dimension). 6. **Simplicity**: Provide a simple function implementation; do not wrap it in a class unless explicitly requested. # Anti-Patterns - Do not use word-level tokenization. - Do not import external NLP libraries. - Do n