I prefer to completely invert this problem and provoke the model into surfacing whatever desired behavior & capability by having the environment push back on it over time.
You get way more interesting behavior from agents when you allow them to probe their environment for a few turns and feed them errors about how their actions are inappropriate. It doesn't take very long for the model to "lock on" to the expected behavior if you are detailed in your tool feedback. I can get high quality outcomes using blank system prompts with good tool feedback.
Or knowledge that is in their training data, but the majority of its training data isn't following the best practices? (e.g. Web Content Accessibility Guidelines)
I think there is a fair point in those cases of having a bunch of markdown docs files detailing them