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Looper 将模糊自动化目标转成带验证、评审、预算与停止条件的可复用 Agent 循环规范。
Feeling a bit loopy? Introducing Looper - your loop design coach It’s been made clear that we shouldn’t be promoting agents, we should be designing loops You can use /loop or /goal but if your loop is poorly designed then it won’t produce good output Even worse, a poorly designed loop will burn a ton of tokens So I created Looper - a tool to help me design better loops, and decided to open source it Here’s what it does: > /looper invokes the loop design wizard > critiques your goal/loop and helps you sharpen it based on a library of best practices > builds a verification rubric so it knows what "done" should be > allows you to add an LLM as judge, including non-Claude models like Codex > adds guardrails and helps you set a budget with tight/generous options to avoid token waste > generates portable artifacts to make it easy to repeat / edit the loop > gives you a visual diagram for your loop (see replies for examples) Looper is intentionally a design layer, not a heavy orchestration framework - it helps you create a loop worth running, then hands it to Claude Code or your own runner/orchestrator Please share your feedback, this was v0.1 so I expect it to have room for improvements, and feel free to submit issues or PRs Note: Looper was inspired by @mvanhorn's "WTF is a loop" article and then fine-tuned with some of the tips in the great loop architecture article by @djfarrelly https://t.co/smAXbBrWnW
是什么
Looper 是 GitHub 项目 ksimback/looper,使用 Python 实现的 Claude Code skill 与循环设计工具。它定位为轻量设计层:在执行器真正修改文件前,通过向导把模糊目标整理成目标、计划、评审、交付、裁判和停止条件,并生成 loop.yaml、解析后的 JSON、LOOP.md、会话执行提示及可选 Python runner。
核心设计 它把验证拆成程序化检查、评审模型量规和人工签核,并设置计划门、交付门、修订次数、最大迭代、无进展检测与预算上限;同时允许接入包括 Codex 在内的其他模型作为 judge。这样,循环的成功标准、反馈路径、状态日志和执行边界都能被提前检查和版本化。
关键洞察
内容的核心论点是:Agent 的效果首先取决于循环设计,而不只是模型能力。/goal、/loop 或 /schedule 负责持续执行或调度,Looper 则负责把它们要运行的对象设计清楚,并提供代码审查、Bug Hunt、文档同步和研究综合等模板。
边界与风险 README 明确说明它不负责持久化重试、并发控制、子 Agent 生命周期、生产运行历史或真正的调度;Token/USD 上限在加入额外计量前主要是操作员可见的建议。因此它适合作为执行前的设计与质量门,不应被误当成完整编排平台。