vc-marbles

Orchestrates adaptive denoising loops to converge AI-generated code into a coherent product.

1|Updated Mar 8, 2026
One-click install
npx skills add https://github.com/VetCoders/vibecrafted --skill vc-marbles
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: vc-marbles
Source: https://github.com/VetCoders/vibecrafted/tree/main/skills/vc-marbles
Command: npx skills add https://github.com/VetCoders/vibecrafted --skill vc-marbles

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Iterative convergence of AI-generated code by running adaptive denoising loops that transform chaotic outputs into a coherent product, stopping only when entropy reaches zero.

Core Features & Use Cases

  • Iterative denoising loops: measure residual entropy, target gaps, implement fixes, and re-denoise to converge to a complete solution with bounded iteration scopes.
  • Convergence governance: supports supervisor/watchdog mode and specialized agent-based execution patterns (vc-delegate and vc-agents) with structured loop reports and convergence metrics.
  • Robust state and safety: frontmatter-driven iteration limits, optional completion promises, and per-session state tracking to ensure repeatable progress across runs.

Use cases include iterative refactoring, incremental feature implementation, and systematic bug fixes in AI-assisted software projects.

Quick Start

Start a Marbles loop in your codebase and let it iteratively refine the output until convergence is achieved.

Frequently Asked Questions about vc-marbles

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I iteratively refine AI-generated code to reduce errors?▼

To iteratively refine AI-generated code, adaptive denoising loops measure residual entropy, target gaps, implement fixes, and re-denoise until convergence is achieved with bounded iteration scopes.

What is code convergence through adaptive denoising loops?▼

Code convergence through adaptive denoising loops orchestrates incremental transformations of chaotic AI outputs into a coherent product, stopping only when entropy reaches zero and a complete solution is formed.

How do I track iteration progress for incremental refactoring across sessions?▼

Track iteration progress for incremental refactoring using frontmatter-driven iteration limits and per-session state tracking, ensuring repeatable progress and structured loop reporting across runs.

Can I use a supervisor mode for systematic bug fixes in AI-assisted projects?▼

Yes, you can use supervisor or watchdog mode for systematic bug fixes, supporting specialized agent-based execution patterns with structured loop reports and convergence metrics.

What's the best way to manage bounded iteration scopes during feature implementation?▼

The best way to manage bounded iteration scopes during feature implementation is utilizing frontmatter-driven state to enforce iteration limits and optional completion promises for controlled convergence.

Why does my AI code generation loop fail to converge?▼

AI code generation loops fail to converge when residual entropy remains high, requiring adaptive denoising to target gaps, implement fixes, and re-denoise until reaching a coherent product state.