For a distribution of continuous type, we can neglect the possibility that two of the samples are equal, since this has probability $0$. Thus the probability density for the second smallest item being $y_2$ is the probability of getting one sample below $y_2$ times the probability density at $y_2$ times the probability of getting $n-2$ samples above $y_2$, times a factor accounting for the permutations. All $(n-2)!$ permutations of the $n-2$ samples are already being counted, so we just have to account for the $n(n-1)$ ordered choices of the smallest and second smallest item. Thus the probability density is
$$n(n-1)F(y_2)f(y_2)(1-F(y_2))^{n-2}\;.$$
We can integrate this to get rid of the derivative $f=F'$:
$$
\begin{eqnarray}
P(Y_2\gt y_2)
&=&
\int_{y_2}^\infty n(n-1)F(y)f(y)(1-F(y))^{n-2}\mathrm dy
\\
&=&
\int_{y_2}^\infty n(n-1)F(y)(1-F(y))^{n-2}\frac{\mathrm dF(y)}{\mathrm dy}\mathrm dy
\\
&=&
\int_{F(y_2)}^1n(n-1)F(1-F)^{n-2}\mathrm dF
\\
&=&
\int_0^{1-F(y_2)}n(n-1)(1-u)u^{n-2}\mathrm du
\\
&=&
n(1-F(y_2))^{n-1}-(n-1)(1-F(y_2))^n\;.
\end{eqnarray}
$$
With $W_n=nF(Y_2)$, this becomes
$$
\begin{eqnarray}
P(W_n\gt w)
&=&
n\left(1-\frac wn\right)^{n-1}-(n-1)\left(1-\frac wn\right)^n
\\
&=&
\left(1-\frac wn\right)^{n-1}\left(n-(n-1)\left(1-\frac wn\right)\right)
\\
&=&
\left(1-\frac wn\right)^{n-1}\left(1+w-\frac wn\right)\;.
\end{eqnarray}
$$
Thus the limit distribution is
$$
\lim_{n\to\infty}P(W_n\gt w)=(1+w)\mathrm e^{-w}\;,
$$
or
$$
\lim_{n\to\infty}P(W_n\lt w)=1-(1+w)\mathrm e^{-w}=\frac12w^2+O\left(w^3\right)\;.
$$
Here's a plot.