Reusable prompts and step-by-step workflows that let coding agents self-pace through a task until it's done.
Common questions about reusable Loops for AI coding agents
A loop is a reusable workflow for AI coding agents built around four parts: a goal, a check command that objectively verifies progress, an exit condition, and a max-iteration limit. Paste the kickoff prompt into your agent and it self-paces through the task, re-running the check command after each pass until the exit condition is met.
A normal prompt is one instruction for one turn. A loop is the system around repeated turns — it tells the agent how to verify its own work and when to stop, so it can keep going without you re-prompting it after every attempt. This practice is known as loop engineering.
Most loops on this page are agent-agnostic — the kickoff prompt is plain text you can paste into Claude Code, Cursor, Codex, or any other coding agent. Each loop's page lists which agents it was specifically built and tested for.
Use a loop when a task needs several revision passes and has an objective acceptance check, such as fixing tests, resolving CI failures, reviewing code, verifying a deployment, or completing a large refactor. One-off questions and tasks without a clear definition of done usually do not need a loop.
Choose a stable, repeatable check with a meaningful exit code, such as tests, type checking, linting, or a deployment health check. Define an observable completion state and set a maximum iteration count so the AI agent cannot retry forever.
A loop repeatedly executes prompts and commands, so restrict file, network, secret, and deployment access; run risky work in an isolated branch or sandbox; and keep iteration limits and human review. Never let an unreviewed loop run destructive commands.
