SigRank — Build Archetype Analytics

When the field is filtered by composition type, the yield spreads 9,000x and leverage spreads 133x · 1,586 operators (HCM) · Generated 2026-08-10

Yield spread
9,000x
0.05 (INPUT-BOUND) → 469 (KINETIC)
Leverage spread
133x
2.7x (INPUT-BOUND) → 361x (KINETIC)
Fresh input range
0.4% → 26.6%
CONVERGENT vs INPUT-BOUND
Field separates into
10 types
4 families · deterministic filter

Archetype Distribution

Operators per Archetype
Median Yield (Υ) per Archetype (log scale)

Leverage & Velocity

Median Leverage per Archetype (log scale)
Median Velocity per Archetype (log scale)

Token Composition (4-Pillar Breakdown)

Median token composition: Input / Output / Cache Read / Cache Write (%)

Activity Level

Median Total Tokens (log scale)
Median Tokens Per Day (log scale)

Signal Quality

Median SNR (signal-to-noise ratio) per Archetype

Top Platform per Archetype

Dominant platform for each archetype

Full Archetype Data Table

# Name Family N % Yield Υ Leverage Velocity SNR Tokens (med) Tok/Day Platform Composition

Methodology

Method: deterministic_build_archetypes — no clustering, no randomness

Classification: Each operator is classified into one of 10 build archetypes based on three derived dimensions of their token cascade:
  Leverage = cache_read / input (how much you reuse vs fresh input)
  Velocity = output / input (how much you generate vs take in)
  Construction = cache_write / cache_read (how much new context you build per read)

Precedence (first match wins):
  1. CONVERGENT — P80 on all 3 axes (leverage ≥ 74.6, velocity ≥ 0.34, construction ≥ 0.043)
  2. KINETIC — velocity ≥ 0.80
  3. Construction branch — construction ≥ 0.02 (BUILDER / RECURSIVE / AMPLIFIER by leverage)
  4. Reuse depth branch — else (INPUT-BOUND / PRIMING / CONTEXTUAL / DEEP READER / ARCHIVIST by leverage)

Source: public/data/archetypes.json generated by scripts/gen-build-archetypes.mjs from public/data/field-analysis.json
Population: human_center_of_mass (1,627 total, 1,586 HCM, outliers excluded)
Replaces: Previous 8 K-Means archetypes (silhouette 0.544/0.377) — now deterministic with clear definitions

Citation
McHenry, D. J. (2026). SigRank Two-Axis Operator Taxonomy: Finalized Datasets and Analytics Dashboards (v3.0) [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.21876660
Concept DOI (resolves to latest version)
https://doi.org/10.5281/zenodo.21875675
Author
Deric J. McHenry · ORCID 0009-0002-9904-5390
SigRank · signalaf.com · MO§ES™ · @burnmydays
License
Creative Commons Attribution 4.0 International (CC-BY 4.0)
BibTeX
@dataset{mchenry2026sigrank, author = {McHenry, Deric J.}, title = {{SigRank Two-Axis Operator Taxonomy: Finalized Datasets and Analytics Dashboards (v3.0)}}, year = 2026, publisher = {Zenodo}, version = {3.0}, doi = {10.5281/zenodo.21876660}, url = {https://doi.org/10.5281/zenodo.21876660} }
SigRank Build Archetype Analytics · 10 deterministic archetypes · 1,586 operators · Generated 2026-08-10