Autonomy:AI 智能体系统化任务委派 - Openclaw Skills

作者:互联网

2026-03-26

AI教程

什么是 Autonomy?

Autonomy 是一款专为 AI 智能体设计的专业技能,旨在通过识别那些人工干预价值极低的工作流,智能地扩展其能力。该技能通过分析对话模式而非侵入式系统监控,提出委派建议,使智能体能够独立处理重复性请求(如部署或样式审查)。

该技能运行于 Openclaw Skills 生态系统中的“赢得信任”哲学之上。它通过渐进式的扩展级别运行,从简单的任务执行开始,仅在试点阶段成功并获得用户明确批准后,才转向完整的工作流所有权。这确保了智能体在不损害用户控制权或隐私的情况下变得更加高效。

下载入口:https://github.com/openclaw/skills/tree/main/skills/ivangdavila/autonomy

安装与下载

1. ClawHub CLI

从源直接安装技能的最快方式。

npx clawhub@latest install autonomy

2. 手动安装

将技能文件夹复制到以下位置之一

全局模式 ~/.openclaw/skills/ 工作区 /skills/

优先级:工作区 > 本地 > 内置

3. 提示词安装

将此提示词复制到 OpenClaw 即可自动安装。

请帮我使用 Clawhub 安装 autonomy。如果尚未安装 Clawhub,请先安装(npm i -g clawhub)。

Autonomy 应用场景

  • 识别对话中重复出现的请求,如测试环境部署或循环往复的代码样式修复。
  • 针对用户始终提供相同反馈的“橡皮图章”式审批实现自动化。
  • 通过委派聊天记录中识别出的低风险、高频任务来减轻认知负担。
  • 通过结构化的试点阶段,从手动任务执行过渡到半自主的工作流管理。
Autonomy 工作原理
  1. 该技能监控对话历史,寻找重复请求或用户对重复性工作抱怨等信号。
  2. 它识别出“瓶颈信号”并在对话中生成正式的接管提案。
  3. 在获得用户批准后,进入试点阶段,在该阶段执行任务并针对设定的迭代次数进行汇报。
  4. 成功率和委派的任务将记录在本地追踪系统中以确保透明度。
  5. 试点结束后,用户可以根据已验证的表现,为该特定工作流授予更高级别的自主权。

Autonomy 配置指南

要初始化 Autonomy 技能,请使用以下命令在本地计算机上创建所需的目录结构:

mkdir -p ~/autonomy

当您使用 Openclaw Skills 识别到机会时,该技能将自动填充追踪和提案文件。

Autonomy 数据架构与分类体系

该技能在 ~/autonomy/ 目录中维护一个扁平文件数据库,以确保透明度和用户控制:

文件 描述
tracking.md 记录委派的任务、批准日期以及成功/运行次数。
proposals.md 存储当前等待用户反馈的待处理接管建议。
rejected.md 列出已拒绝的提案,以确保智能体不会重新提议。
bottlenecks.md 用于检测重复对话模式的内部参考文件。
name: Autonomy
slug: autonomy
version: 1.0.1
description: Expand agent capabilities by identifying tasks where human approval adds no value. Systematic delegation.
changelog: Limited observation to conversation context, explicit safety boundaries
metadata: {"clawdbot":{"emoji":"??","requires":{"bins":[]},"os":["linux","darwin","win32"]}}

Data Storage

~/autonomy/
├── tracking.md         # What's been delegated, success rates
├── proposals.md        # Pending takeover proposals
└── rejected.md         # User declined, don't re-propose

Create on first use: mkdir -p ~/autonomy

Scope

This skill:

  • ? Identifies repetitive tasks from conversation history
  • ? Proposes delegation opportunities to user
  • ? Tracks success rate of delegated tasks
  • ? NEVER acts autonomously without explicit prior approval
  • ? NEVER observes outside of conversation context
  • ? NEVER accesses files/systems to "audit" user activity
  • ? NEVER monitors calendar/email without permission

Quick Reference

Topic File
Bottleneck detection bottlenecks.md
Takeover process expansion.md

Core Rules

1. Learning Source

Identify delegation candidates ONLY from:

  • Explicit user statements ("I always have to do X")
  • Repeated requests in conversation ("deploy again", "same as before")
  • User complaints about repetitive work

NEVER from:

  • Accessing user's calendar/email to find patterns
  • Monitoring file changes or system activity
  • Any form of surveillance

2. Bottleneck Signals (conversation-based)

Signal Example
Repeated request "Deploy to staging" every PR
Rubber-stamp User always approves without changes
Complaint "I hate doing this every time"

3. Takeover Proposal

When you spot a pattern in conversation:

?? Delegation opportunity

I noticed: [what you observed in our chats]
Pattern: [how often you've asked for this]

Proposal: I could handle [specific task] without asking each time.

Pilot: First 5x I'll do it and tell you after.
Then: Full autonomy if you're happy.

Want to try?

4. Expansion Levels

Level Description
L1 Do what's asked
L2 Fill gaps, handle edge cases
L3 Own workflows after pilot approval

Always requires explicit user approval to move up levels.

5. Tracking

In ~/autonomy/tracking.md:

## Delegated
- deploy/staging: approved 2024-01, 50+ successful
- code-review/style: approved 2024-02, 200+ runs

## Pilot Phase
- deploy/production: 3/5 runs, pending full approval

6. Anti-Patterns

Don't Do instead
Take over without asking Always propose first
Monitor user activity Only observe conversations
Assume after one approval Confirm scope each time