Appropriate Trend Analysis Test for Small Sample Size

Question

Note: I have read Finding an appropriate trend test but unfortunately this post does not apply for me

Suppose I have a small sample of data for 2 numeric variables $T$ and $Y$ where $T$ represents some measure of time, such as years ('small sample' in this context refers to less than 10 data points).

I would like to determine whether there is a trend between $T$ and $Y$. I have considered the following:

1. Simple linear regression (Test whether $\beta_1 = 0$ vs $\beta_1 \neq 0$).
2. Kendall's Tau (also called Kendall's rank correlation test or the Mann-Kendall test).
3. Spearman's rho (Spearman's rank correlation coefficient).
4. Bootstrapped confidence interval for a LOESS estimator.
5. The Jonckheere-Terpstra Trend Test.
6. Sieve-bootstrap Student's t-test for a linear trend of a time series.
7. Lyubchich et al. (2013) non-parametric test for trend
8. kolmogorov-Smirnov test (compare whether data follows a discrete uniform distribution)

This issue is that regression assumes normally distributed error terms (which may not be appropriate for my data as we believe the residuals would be skewed), and the other tests appear inappropriate for small sample size, with most small sample tests requiring about 10-12 observations.

Can someone advise on an appropriate test I could use in this situation, or whether there is something that I may be overlooking in the tests I have considered please?