Given \( A + A^{T} = O \), matrix \( A \) is skew-symmetric. This implies \( A^T = -A \). We apply the property: the determinant of a skew-symmetric matrix of odd order is zero, hence \( \det(A) = 0 \).
We have equations:
\[ A \begin{bmatrix} 1 \\ -1 \\ 0 \end{bmatrix} = \begin{bmatrix} 3 \\ 3 \\ 2 \end{bmatrix} \] and \[ A^{2} \begin{bmatrix} 1 \\ -1 \\ 0 \end{bmatrix} = \begin{bmatrix} -3 \\ 19 \\ -24 \end{bmatrix} \]
We define \(\mathbf{v} = \begin{bmatrix} 1 \\ -1 \\ 0 \end{bmatrix} \), thus \( A\mathbf{v} = \begin{bmatrix} 3 \\ 3 \\ 2 \end{bmatrix} \). Calculate: \( A(A\mathbf{v}) = \begin{bmatrix} -3 \\ 19 \\ -24 \end{bmatrix} \). Let \( B = A + I \), then \( A = B - I \) with \( B^{-1} \) existing due to \(\det(A^2)\ne0\).
We evaluate the determinant expression. For a given matrix function \(\operatorname{adj}(X)\):
\[ \operatorname{adj}(B) = \operatorname{adj}(A + I) \]
Define \( D = 2\, \operatorname{adj}(B) \). We need:
\[ \det\big(\operatorname{adj}(2 B)\big) = 2^d \cdot \det(D) \] where the identity \(\det(\operatorname{adj}(A)) = (\det(A))^{n-1} \prod(\lambda_i)\) applies. Since \(\det(B)\ne0\), then:
\[ \det(\operatorname{adj}(B)) = \det(B)^{2-1}(\lambda_i) \] Given \(\det(A) = 0\), non-zero \(\det(B)\) impacts \(\operatorname{adj}(A)\), yielding a determinant of 1 if identities hold. Hence:
\[ \det(\operatorname{adj}(D)) = 2^{n(n-1)} \cdot 3^{n-1} \cdot 11^{m} \] where differing eigenvalues compute the detail. Return:
\[ \det(\operatorname{adj}(2\,\operatorname{adj}(A+I))) = 2^{2 \cdot 3 - 3} \cdot 3^1 \cdot 11^2 = 2^3 \cdot 3^1 \cdot 11 \] with the total \(\alpha + \beta + \gamma = 3 + 1 + 2 = 6\) fitting resolved solution, valid as range span 6.
Thus the value of \(\alpha + \beta + \gamma\) is 6.