autonomous-loop-patterns

What problem does it solve?

This Skill helps you stop autonomous AI agents from running unbounded, losing progress between runs, or failing silently by turning “keep going” into an explicit, checkable loop control design.

Core Features & Use Cases

• Loop control primitives: trigger, worker, progress signal, stop condition, durable state, and safety cap.
• Pattern selection for common loop types: bounded single-run, sentinel continuation, checkpoint-resume, and supervisor respawn.
• Safety and reliability guidance: explicit completion/blocked states, stall detection, and caps for iterations, errors, time, and budget.
• Contracts for durable progress: what must persist across sessions and what must be outside model memory.

Quick Start

Use the autonomous-loop-patterns skill to define a checkpoint-resume loop contract for your multi-session agent, including the stop condition, the durable state schema, and the safety caps you will enforce outside the worker.