Does rapid convergence mean rushing the work?

No. It is about recognizing structural factors and using early high-impact automation to reach the same high-quality benchmarks (V ≥ 0.80) in less time.

How does the iteration prediction model work?

It uses a base of 4 iterations and applies penalties for factors like fuzzy domain scope, lack of baseline metrics, or the need for multi-context validation to estimate the upper bound of effort.

What are the requirements to use this skill effectively?

It works best when you have clear baseline metrics (V_meta ≥ 0.40), a focused domain scope that can be described in under three sentences, and the ability to perform direct validation.

Can I use this for exploratory research?

It is generally not recommended for exploratory research where success criteria are undefined; in those cases, standard convergence or baseline-establishing skills are preferred.

What is the primary goal of the Rapid Convergence skill?

The primary goal is to reach methodology stability and quality thresholds in 3-4 iterations instead of 5-7, effectively halving the time required for complex workflow setup.

Rapid Convergence Optimizer

Name: Rapid Convergence Optimizer
Author: yaleh

byyaleh

•

生産性とワークフロー

Accelerates methodology convergence in AI workflows by reducing iteration cycles through structural optimization and metric-based planning.

概要

The Rapid Convergence skill is a meta-cognition tool designed to help Claude achieve production-ready methodology status in just 3-4 iterations, compared to the standard 5-7. By utilizing a specific prediction model and focusing on high-impact automation early in the cycle, it enables a 40-60% reduction in development time. This skill is ideal for projects with established baselines (V_meta ≥ 0.40), focused domain scopes, and direct validation requirements, ensuring that speed increases do not result in a loss of quality or architectural integrity.

主な機能

Metric-based baseline assessment (V_meta) for objective progress tracking
Retrospective validation techniques to minimize deployment overhead
Early identification of Pareto-principle automation opportunities
Structural optimization strategies to reach 0.80 quality thresholds faster
15 GitHub stars
Iteration count prediction model for accurate project planning

ユースケース

Estimating timelines and iteration counts for new experimental workflows
Optimizing error recovery and debugging methodologies using historical data
Standardizing repetitive development tasks into high-efficiency playbooks

概要

主な機能

Metric-based baseline assessment (V_meta) for objective progress tracking
Retrospective validation techniques to minimize deployment overhead
Early identification of Pareto-principle automation opportunities
Structural optimization strategies to reach 0.80 quality thresholds faster
15 GitHub stars
Iteration count prediction model for accurate project planning

ユースケース

Estimating timelines and iteration counts for new experimental workflows
Optimizing error recovery and debugging methodologies using historical data
Standardizing repetitive development tasks into high-efficiency playbooks