Token导航 LogoToken导航

Agent Skill · 效率

xiaobai-self-improve(小白自我提升)

xiaobai-self-improve 用于记录任务执行中的错误、用户纠正、经验和能力缺口,适合在 OpenClaw 中希望让 Agent 持续沉淀问题、修正和最佳实践时使用。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。

xiaobai-self-improve 是效率类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为只读;许可证为 MIT-0,第三方扫描有提醒项,按安装量在效率类的 21,003 个 Skill 里排第 12,584。

总安装 4,074周安装 175GitHub Stars 公开资料未说明下载 1,428核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.0.0

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:xiaobai-self-improve(小白自我提升)
来源仓库:https://github.com/aptratcn/xiaobai-self-improve
安装命令:
openclaw skills install xiaobai-self-improve
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 xiaobai-self-improve

这个 Skill 是做什么的

效率类 Skill 把重复的整理、汇总、转换工作交给助手完成,减少手工复制粘贴。

自我改进引擎,从错误中学习并持续优化 Agent 行为模式。适合希望 Agent 长期积累经验、修正偏差的效率类任务。自动记录失败案例、用户反馈与能力短板,生成改进建议。需确认日志写入权限与存储路径,防止敏感信息泄露。xiaobai-self-improve 属于效率类 Skill,可作为该场景下的辅助能力补充。

建议定期审查学习样本质量,避免错误模式被固化传播。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install xiaobai-self-improve;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

xiaobai-self-improve 怎么安装?

在终端执行 openclaw skills install xiaobai-self-improve。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

xiaobai-self-improve 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

xiaobai-self-improve 需要什么权限?

权限级别是只读。该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

xiaobai-self-improve 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

xiaobai-self-improve 用的人多吗?

公开数据是累计安装 4,074 次,近一周安装 175 次,下载 1,428 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

xiaobai-self-improve 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。
  • 标注为只读权限,试用阶段不容易误改本地文件。

先想清楚的地方

  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理效率类任务、又不想每次都手写提示词的人。
  • 只想先试试效果、不希望它改动本地文件的人。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

效率类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
github

GitHub 代码协作 · OpenClaw

7,632,918545需要联网MIT-0通过
nano-pdf

Nano PDF 文档 · OpenClaw

4,249,476231需要联网MIT-0通过
auto-updater

自动更新程序 · OpenClaw

2,964,063386操作浏览器MIT-0通过
video-frames

视频帧 · OpenClaw

2,621,304112需要联网MIT-0通过
gemini

Gemini 助手 · OpenClaw

2,547,39648需要联网MIT-0有提醒
xiaobai-self-improve

小白自我提升 · 本页

4,074—只读MIT-0有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
self-improve
version
1.0.0
description
Self-Improvement Engine - AI Agent that learns from mistakes and continuously improves. No more repeating the same errors.
emoji
🔄
tags
[self-improvement, learning, reliability, productivity, ai-agent]

Self-Improvement Engine 🔄

AI Agent that learns from mistakes and continuously improves. No more repeating the same errors.

The Problem

AI Agents make the same mistakes repeatedly:

  • Forgot to verify before saying "done"
  • Made incorrect time calculations
  • Promised tasks but didn't execute
  • Gave vague responses instead of concrete action

Each session starts fresh, learning nothing from past failures.

The Solution

Mistake Log

Track every mistake in memory/mistakes.json:

{
  "mistakes": [
    {
      "date": "2026-04-21",
      "type": "fake_completion",
      "description": "Said 'cron task created' but only wrote to file",
      "impact": "User didn't receive scheduled messages",
      "lesson": "Always verify task exists via cron list before claiming done",
      "rule": "MUST run verification command after any creation action"
    },
    {
      "date": "2026-04-21",
      "type": "time_calculation",
      "description": "Said '1 minute later' when it was actually 1 hour",
      "impact": "User got wrong expectation",
      "lesson": "Never calculate time mentally - always use tools",
      "rule": "Use session_status or date command for time, never mental math"
    }
  ]
}

Improvement Rules

Based on mistakes, auto-generate rules in WORK_RULES.md:

## Learned Rules (from mistakes)

1. After creating any cron task → MUST run `openclaw cron list` to verify
2. Time calculations → MUST use `session_status` or Python, never mental
3. Multi-step task → MUST checkpoint after each step
4. Tool error → MUST report immediately, don't silently retry

Self-Audit Protocol

Every heartbeat or session start:

1. Read mistakes.json
2. Read WORK_RULES.md
3. Check: Am I about to violate any learned rule?
4. If yes → adjust behavior
5. If I make a mistake → log it immediately

Mistake Categories

TypeDescriptionPrevention
fake_completionSaid done but didn't doEVR: Execute-Verify-Report
time_calculationWrong time mathUse tools, never mental
silent_failureTool failed, didn't tell userReport immediately
vague_promise"I'll do it" without actionExecute first, then report
forget_contextLost important infoUse memory tools
wrong_assumptionGuessed instead of checkingVerify before claiming

Usage Examples

When you make a mistake:
User: "You said 1 minute but it's actually 1 hour"

I acknowledge this mistake. Let me log it:
- Type: time_calculation
- Lesson: Always use tools for time, never mental math
- New rule added to WORK_RULES.md

I will not make this mistake again.
When starting a task:
I need to create a cron task.
Checking WORK_RULES.md:
- Rule #1: After creating cron task → MUST verify with cron list

[Creates task]
[Runs openclaw cron list]
[Confirms task exists]
Task verified and confirmed. ✅
When user corrects you:
User: "You should have checked before saying it was done"

Logging mistake:
- Type: fake_completion
- Lesson: Verify before claiming done
- This becomes a permanent rule in WORK_RULES.md

This mistake is now in my permanent memory and won't happen again.

Integration with Other Skills

  • EVR - Self-improve reinforces Execute-Verify-Report
  • Memory Guard - Mistakes persist across sessions
  • Daily Reflection - Review mistakes at day end
  • Error Recovery - Log errors that led to mistakes

Key Principle

Every mistake is a learning opportunity. Log it, learn from it, never repeat it.

License

MIT

平台分布

OpenClaw

98.92%
按下载量换算1,413

安全审计

VirusTotal

通过

ClawScan

可疑

Static analysis

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

继续浏览同类 Skills