home/categories/machine-learning/yaleh-meta-cc-claude-skills-rapid-convergence-skill-md
machine-learningdata-ai

rapid-convergence

Achieve 3-4 iteration methodology convergence (vs standard 5-7) when clear baseline metrics exist, domain scope is focused, and direct validation is possible. Use when you have V_meta baseline ≥0.40, quantifiable success criteria, retrospective validation data, and generic agents are sufficient. Enables 40-60% time reduction (10-15 hours vs 20-30 hours) without sacrificing quality. Prediction model helps estimate iteration count during experiment planning. Validated in error recovery (3 iterations, 10 hours, V_instance=0.83, V_meta=0.85).

yaleh
maintainer
yaleh
Actualizado 12/13/2025
Estrellas
15
Forks
1
quick start

Installation and usage

Achieve 3-4 iteration methodology convergence (vs standard 5-7) when clear baseline metrics exist, domain scope is focused, and direct validation is possible. Use when you have V_meta baseline ≥0.40, quantifiable success criteria, retrospective validation data, and generic agents are sufficient. Enables 40-60% time reduction (10-15 hours vs 20-30 hours) without sacrificing quality. Prediction model helps estimate iteration count during experiment planning. Validated in error recovery (3 iterations, 10 hours, V_instance=0.83, V_meta=0.85).

Instalación
$ install --globalskills.sh
Uso

Después de instalarlo, puedes usar este skill ejecutando el siguiente comando en tu terminal:

skills use rapid-convergence