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).
Packaged view
This page reorganizes the original catalog entry around fit, installability, and workflow context first. The original raw source lives below.
Install command
npx @skill-hub/cli install yaleh-meta-cc-rapid-convergence
Repository
Skill path: .claude/skills/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).
Open repositoryBest for
Primary workflow: Research & Ops.
Technical facets: Full Stack, Data / AI.
Target audience: Development teams looking for install-ready agent workflows..
License: Unknown.
Original source
Catalog source: SkillHub Club.
Repository owner: yaleh.
This is still a mirrored public skill entry. Review the repository before installing into production workflows.
What it helps with
- Install Rapid Convergence into Claude Code, Codex CLI, Gemini CLI, or OpenCode workflows
- Review https://github.com/yaleh/meta-cc before adding Rapid Convergence to shared team environments
- Use Rapid Convergence for development workflows
Works across
Favorites: 0.
Sub-skills: 0.
Aggregator: No.