A useful Frame predicts something
If a Frame is worth learning, it tells the learner what remains stable when content changes. That may include word order, a fixed connector, a required form, a semantic relationship or a register boundary. The Frame reduces uncertainty about how to build the next sentence.
A bad pattern merely points at holes. It tells you where substitutions happened in one example but not what substitutions are licensed or why the relationship matters.
Natural variation is a stress test
Change the topic, subject or polarity. Try a second realistic context. If the Frame repeatedly produces awkward or impossible language, the abstraction is too broad or the wrong elements were declared variable. If it works only with one memorised lexical combination, perhaps it should remain an example or phrase rather than a Frame.
This is why editorial review matters more than raw catalogue size. A smaller collection of constrained objects can support better decisions than a giant library of decorative templates.
Uncertainty should be visible rather than hidden
Some candidates are useful but not yet sufficiently reviewed. Metkagram’s broader architecture already distinguishes canonical objects from inferred or lower-confidence relationships. The same principle should apply to content: do not publish confidence you do not possess merely to make the catalogue look complete.
For learners and AI clients alike, a stable reviewed Frame is valuable because its meaning does not depend on guessing what an editor intended.