---

name: validate-inputs description: "Check function inputs for correctness and safety. Use when implementing defensive programming." mcp_fallback: none category: analysis tier: 2 user-invocable: false

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

Copy# 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