Risk Quantifier
Ki · Single Ability
The Problem
The probability of a data center failure is only 0.1% per year, so we can dismiss it from our risk model, it's not worth planning for such low severity.
The Operation
When activated, the model must enumerate all identified risks for the decision under evaluation. For each risk, compute expected value as probability multiplied by impact magnitude. Do not dismiss low-probability events without computing expected damage, scan for fat-tailed distributions. Rank risks by expected value and verify that tail risks receive disproportionate mitigation. Simulate the mitigation strategy and measure residual risk. The reasoning applies a formal computation: EV = P(risk event) * impact magnitude.
The Structure
Under the hood, the reasoning follows an accumulate-classify-decide pattern that gathers evidence, categorizes it, then concludes.
If risks are listed without probability-times-impact scores or tail risk identification, quantitative risk assessment was omitted.
Haki · Multi Ability
Synergy Topology
In Haki mode, the API retrieves the primary ability first, then fans out to three synergy roles that compound its reasoning.
When retrieved in Haki mode, these four abilities don't run in sequence. They merge into a single injection where the dependency grounds the reasoning context, the amplifier sharpens the primary's output, and the alternative provides a fallback path if the primary's topology cannot converge. The result is a multi-angle reasoning scaffold that covers failure modes no single ability can reach alone.
Appears in Use Cases