Bayesian Experiment Reader

Bayesian counterpart to experiment-result-reader. Computes posterior P(variant beats control), credible intervals, and expected loss from per-variant exposure and conversion data. Beta-Binomial for proportion metrics (CVR), Normal-Normal for continuous metrics (revenue per user). Decision rule combines a confidence threshold with an expected-loss tolerance, so the ship decision reflects both "how likely is this better?" and "how bad is it if I'm wrong?". Use this skill alongside experiment-result-reader when reading any A/B test result. Pairs with analytics-diagnostic-method. Use whenever interpreting an A/B test result the user plans to ship from, when the question is "what's the chance variant wins?", or when a frequentist p-value is on the edge and the user wants the posterior view. Triggers when Clamp MCP returns experiment exposure and conversion data, or when any analytics source surfaces per-variant counts.