validate-inputs

Testing & Quality

Check function inputs for correctness and safety. Use when implementing defensive programming.

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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

Document expectations: Specify types, shapes, ranges for inputs
Implement checks: Add validation before processing
Provide error messages: Clear messages for validation failures
Test edge cases: Verify validation catches invalid inputs
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