Iterative Loop

Overview

The Iterative Loop skill implements continuous AI-driven development loops that persist until completion criteria are met. Inspired by the Ralph Wiggum technique, this approach enables autonomous, self-correcting development cycles where the AI sees its previous work in files and git history, iteratively improving until success.

Core Philosophy

1. Iteration > Perfection - Don't aim for perfect on first try; let the loop refine the work
2. Failures Are Data - Each failure provides information to improve the next attempt
3. Clear Criteria - Success must be objectively measurable (tests, metrics, validations)
4. Persistence Wins - Keep trying until success; the loop handles retry logic automatically

Prerequisites

• Claude Code with session management
• Clear completion criteria (tests, linting, metrics)
• Version control (git) for tracking iterations