Validate Inputs

Implement input validation to ensure functions receive correct data types, shapes, ranges, and formats.

When to Use

• •Adding defensive checks to functions
• •Improving error messages for bad inputs
• •Ensuring tensor shape/dtype correctness
• •Validating configuration parameters

Quick Reference

# Input validation pattern
def validate_tensor(tensor):
    assert tensor is not None, "Tensor cannot be None"
    assert isinstance(tensor, ExTensor), "Must be ExTensor type"
    assert len(tensor.shape) > 0, "Tensor shape cannot be empty"
    assert tensor.dtype() in [DType.float32, DType.float64], "Invalid dtype"
    return True

# Usage with context
try:
    validate_tensor(input_data)
except AssertionError as e:
    raise ValueError(f"Invalid input: {e}")

Workflow

1. •Document expectations: Specify types, shapes, ranges for inputs
2. •Implement checks: Add validation before processing
3. •Provide error messages: Clear messages for validation failures
4. •Test edge cases: Verify validation catches invalid inputs
5. •Document behavior: Note what validation is performed

Output Format

Input validation specification:

• •Parameter name and type
• •Constraints (shape, range, valid values)
• •Error handling strategy
• •Error messages returned
• •Test cases for invalid inputs

References

• •See generate-tests skill for validation test cases
• •See CLAUDE.md > Defensive Programming for best practices
• •See scan-vulnerabilities skill for security validation