The Six SigRank Metrics
Every metric is a different lens on the token cascade. Yield is the headline; the other five explain why.
Yield (Υ) — Token Cascade Efficiency
Υ = (cache_read × output) / input² — the headline SigRank metric. What cascade yield measures, how to improve it, and class tier mapping.
Cache Hit Rate — Context Reuse Efficiency
cache_read / (cache_read + cache_write) — how well you reuse prompt-cached context. The highest-leverage AI coding metric.
Compression Ratio — Output per Input
output / input — how much you get back per token sent to an AI model. What it means and how to improve it.
Leverage — Cached Context Amplification
cache_read / input — how much cached context amplifies your fresh input. What token leverage means and how to increase it.
Velocity — Token Production Rate
output / input — output efficiency ratio in AI coding. Why it's a secondary metric and how it interacts with yield.
Signal-to-Noise Ratio (SNR) — Signal Density
signal_tokens / total_tokens — signal density in AI coding. What signal vs noise means and its link to the Conservation Law.