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
更新於 12/13/2025
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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).

安裝
$ install --globalskills.sh
使用

安裝後,您可以透過在終端機執行以下指令來使用此技能:

skills use rapid-convergence