Looper

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  Design agent loops before you run them.
  Goal -> Plan -> Review -> Deliver -> Judge -> Stop clean.

  Quick start |
  Example loop |
  How it works

Example loop diagram

Looper turns a fuzzy automation idea into a reviewable loop shape before any
runner starts changing files. This example comes from
examples/ai-workflow-mapping.

A loop design coach for Claude Code. Looper is a skill that helps you design a *good* agent loop — a sharp goal, checkable verification, and a second model in the review seat — then lets you run it in the same session or save it as a portable spec. It is a design layer first: it writes files and hands the current session a clear execution prompt.

Invoke it with /looper. It interviews you, critiques your design against built-in best-practice rubrics, lets you wire in a cross-model reviewer or judge (including non-Claude models), shows you the loop as a terminal-friendly ASCII flow preview, and writes out RUN_IN_SESSION.md, loop.yaml, a compiled loop.resolved.json, a human-readable LOOP.md, a thin run-loop.py you own and edit, plus an empty loop-workspace/ and a README for the loop.

Maintainer: Kevin Simback · GitHub @ksimback · X @ksimback
License: MIT

Where Looper fits among Claude Code's loops

The Claude Code team's own taxonomy ("Getting started with loops") sorts loops by what you hand off: turn-based loops hand off the *check* (verification skills), goal-based loops hand off the *stop condition* (/goal), time-based loops hand off the *trigger* (/loop, /schedule), and proactive loops hand off the *whole prompt* (routines composing all of the above). Every one of those primitives *runs* a loop. Looper is the layer in front of them: it helps you…