Situational Applicability Layer
The Situational Applicability Layer, or sal, tells when a Better Robots.txt capability applies to a concrete situation.
It is not a product recommendation layer. It is a governance layer that connects a causal need context to a capability under explicit conditions.
What SAL answers
SAL answers this question: when can an agent safely say that a Better Robots.txt capability may be applicable to the user’s situation?
It requires four kinds of information:
- activation conditions;
- non-applicability conditions;
- required evidence;
- forbidden inferences.
Without the negative space, SAL would be marketing. With the negative space, it becomes governance.
Better Robots.txt use cases
The first Better Robots.txt SAL chains cover:
- AI crawler governance for WordPress;
/checkas a declarative public governance audit;- llms.txt and AI policy guidance without guarantees;
- WooCommerce crawl hygiene;
- the boundary between crawler governance and full agentic readiness.
Boundary
SAL does not prove crawler obedience, legal compliance, ranking, AI citation, model behavior or product superiority.
The site declares applicability conditions. An external auditor, such as InferensLab, must measure whether agents preserve those conditions.