SigRank Two-Axis Operator Taxonomy: Finalized Datasets and Analytics Dashboards

When 1,627 AI operators are classified along two axes, structure emerges that is invisible in aggregate. Axis 1 — Build archetypes: 10 deterministic composition types based on leverage (cache_read/input), velocity (output/input), and construction (cache_write/cache_read). No clustering. When the HCM cohort (1,586 operators) is classified this way, yield spreads 9,387x, leverage spreads 133x, and fresh input ranges from 0.27% to 26.62%. Axis 2 — Experience ladder: 24 stages (8 tiers x 3 sub-stages) by descending first-match over fixed total-token thresholds. The canonical classifier thresholds are in experience_ladder.json. The observed distribution (1,626 operators) is in class-distribution-reference.json. Observed token range: 123,324 to 7.07T tokens. The two axes are designed as separate dimensions (orthogonal by design, not empirically tested for independence). An ARCH+ operator can be INPUT-BOUND. An IGNITER can be an AMPLIFIER. Contents: canonical classifier (experience_ladder.json), observed distribution (class-distribution-reference.json), archetype statistics (archetypes.json), 2 interactive dashboards (Chart.js CDN), METHODS.md (all metric formulas, quantile method, boundary conventions), PROVENANCE.md (source, exclusion protocol, reproducibility), generator script (gen-build-archetypes.mjs), and reference docs. Aggregate statistics only — no individual operator records. CC-BY-4.0.