The latent space of models, which basically steers the token generation into a particular direction is much coarser with the local models compared to big models. Small models known fundamentals well, but do not have the breadth to realize the particularities of combination of fundamental task given certain criteria.

As such, if you want to replicate big model behaviour, you need to basically do context augumentation. How you do that is up to you for your own needs.

But the question remains: isn't something like pi.dev good enough for this? Or are you using custom harness to describe the scripts/skills/etc that you create yourself?