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autonomous-loops自主循环

Agent Skill

autonomous-loops 用于补充开发相关能力,适合在 OpenClaw 中需要让 Agent 承接开发相关任务时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

4,176

周安装

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下载量

1,392
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:autonomous-loops(自主循环)
来源仓库:https://github.com/djc00p/autonomous-loops
安装命令:
openclaw skills install autonomous-loops
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install autonomous-loops

简介

补充开发相关能力,支持代码循环与自动化流程。autonomous-loops 属于开发类 Skill,可作为该场景下的辅助能力补充。

  • 适用于顺序管道、持久 REPL 会话及并行规范驱动生成。
  • 可结合 PR 自动化与清理通道提升开发效率。
  • 安装命令:openclaw skills install autonomous-loops。
  • 使用前请核实是否会触发命令执行或文件修改操作。

SKILL.md

name
autonomous-loops
description
Autonomous Claude Code loop patterns: sequential pipelines, persistent REPL sessions, parallel spec-driven generation, PR automation, cleanup passes, and RFC-driven DAG orchestration. Choose pattern by complexity: simple (sequential) → medium (PR loop, infinite agents) → advanced (DAG with merge queue). Trigger phrases: autonomous loop, agent loop, continuous development, parallel agents, multi-pass refinement.
metadata
{"clawdbot":{"emoji":"🔄","requires":{"bins":["gh","git","node"],"env":["CLAW_SESSION","CLAW_SKILLS"]},"os":["linux","darwin","win32"]}}

Autonomous Loops — Patterns for Claude Code Automation

Running Claude in loops enables spec-driven development, CI/CD-style pipelines, and iterative refinement without human intervention between steps.

Quick Start

Choose your pattern by complexity:

  1. Sequential Pipeline (simple) — Chain claude -p calls for linear workflows
  2. Persistent REPL (simple) — Interactive sessions with history
  3. Spec-Driven Parallel (medium) — Deploy N agents from spec, manage waves
  4. PR Automation Loop (medium) — PR creation, CI fix, auto-merge
  5. De-Sloppify Pass (add-on) — Cleanup step after any implementation
  6. RFC-Driven DAG (advanced) — Multi-unit parallel work with dependency graph

Pattern Spectrum

PatternSetupComplexityBest For
Sequential PipelineBash scriptLowDaily tasks, scripted workflows
REPLNode/CLILowInteractive development
Parallel AgentsClaude Code loopMediumContent generation, spec variations
PR LoopShell scriptMediumIterative multi-day projects
De-SloppifyAdd-on to anyOptionalQuality cleanup after implementation
DAG OrchestrationPython/NodeHighLarge features, parallel units, merge coordination

References

  • references/sequential-pipeline.md — Basic claude -p loops with examples
  • references/persistent-repl.md — NanoClaw-style session persistence
  • references/parallel-agents.md — Spec-driven deployment with wave management
  • references/pr-automation.md — Continuous Claude PR loop with CI gates
  • references/de-sloppify.md — Quality cleanup pattern
  • references/dag-orchestration.md — RFC-driven multi-unit coordination

Key Principles

  1. Isolation — Each loop iteration gets fresh context (no bleed-through)
  2. Context Persistence — Use files (SHARED_TASK_NOTES.md) to bridge iterations
  3. Exit Conditions — Always set max-runs, max-cost, max-duration, or completion signal
  4. No Blind Retries — Capture error context for next iteration
  5. Separate Concerns — Different loop patterns for different problem sizes

Decision Matrix

Is this a single focused change?
├─ Yes → Sequential Pipeline
└─ No → Do you have a spec/RFC?
         ├─ Yes → Do you need parallel work?
         │        ├─ Yes → DAG Orchestration
         │        └─ No → PR Automation Loop
         └─ No → Do you need many variations?
                  ├─ Yes → Parallel Agents + Spec
                  └─ No → Sequential Pipeline + De-Sloppify

Anti-Patterns

❌ Infinite loops without exit conditions ❌ No context bridge between iterations ❌ Retrying the same failure without capturing error context ❌ Negative instructions instead of cleanup passes ❌ All agents in one context window (reviewer should never be the author) ❌ Ignoring file overlap in parallel work


Adapted from everything-claude-code by @affaan-m (MIT)

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

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能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

94.59%
按下载量换算1,317

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权限和风险

执行命令

安装流程涉及命令执行,可能通过 openclaw skills install autonomous-loops 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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