Multi-robot Control Barrier Function (CBF) safety filters can become infeasible, but a failed quadratic program (QP) does not indicate why the conflict occurred or how to resolve it. To address this, we develop an exact feasibility certificate for multi-agent CBF filters with heterogeneous control-affine dynamics and convex input sets. The certificate quantifies a feasibility reserve by separating the demand imposed by safety constraints from the available actuator supply. This decomposition shows when CBF gain tuning or increased actuation can and cannot resolve infeasibility, and identifies the agents and interactions responsible for the conflict. We further propose an algorithm to optimally allocate shared safety constraints by maximizing the worst local feasibility margin, yielding a linear program for polyhedral input sets. In
320 paired closed-loop simulations, the proposed allocation reduces infeasible control steps from roughly
50% to
6.2%, and reduces safety-violating runs from
118/160 to
24/160. In addition, across
52 infeasibility events, the certificate identifies an interaction whose relaxation restores feasibility in
94% of cases.