This paper presents a weighted epistemic logic tailored for skill assessment, incorporating fuzzy skill sets to represent agent capabilities. By employing implicit weights for belief operators and explicit proficiency formulas, the logic enables reasoning about the interplay between belief and capability. An extension with quantified updates supports analysis of skill set ranges under specified conditions, addressing attribute selection in rough set contexts. The logic establishes direct correspondences with classical Pawlak rough sets and close alignments with fuzzy rough sets. The computational complexity of model checking is analyzed, with the basic logic in P and the extended logic co-NP-complete.