Autonomous Loop Orchestrator

Stop Prompting, Start Iterating

The Autonomous Loop Orchestrator transforms AI agent interactions from one-off prompts into structured, self-correcting development cycles. Built for developers who want to move beyond "chat-and-fix" workflows, this skill implements the Plan → Build → Run → Learn methodology to solve complex coding goals autonomously.

What it does

Unlike standard coding assistants that forget context between messages, this skill manages a dedicated workspace for every goal. It generates a detailed plan, monitors the build process, executes verification commands (tests, benchmarks, or linters), and captures failures as "learnings" that automatically inform the next iteration. It effectively creates a closed-loop system where the agent learns from its own mistakes until the goal is achieved.

Supported Workflows

• Multi-Iteration Development: Set a goal and a max iteration count for fully autonomous optimization.
• Safe Exploration: Use dry-run modes to validate agent plans before any code is modified.
• Verification-Driven Loops: Integrate with pytest, npm test, or custom CLI check commands.
• Skill Export: Once a loop is stabilized, export the entire workflow as a reusable skill definition.

Why it's better than manual prompting

Manual prompting requires you to act as the "glue"—running tests, copying errors, and reminding the AI what it tried before. This skill automates that overhead. It provides persistent state via plan.md and learnings.md, ensuring the agent never repeats the same mistake twice and has a clear, verifiable definition of success.