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Agent Skill · 开发

self-improvement-system(自我提升系统)

self-improvement-system 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。

self-improvement-system 是开发类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为需要联网;许可证为 MIT-0,第三方扫描已通过,按安装量在开发类的 21,746 个 Skill 里排第 3,887。

总安装 9,740周安装 394GitHub Stars 2下载 3,057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
1.2.0

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:self-improvement-system(自我提升系统)
来源仓库:https://github.com/assafster/self-improvement-system
安装命令:
openclaw skills install self-improvement-system
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 self-improvement-system

这个 Skill 是做什么的

开发类 Skill 面向写代码、改代码和排查问题,一般会读写仓库文件,部分还会执行构建或测试命令。

运行自动化自我改进循环,帮助代理从历史错误中学习。适用于长期运行任务且需渐进式提升准确性的场景。周期性评估行为表现并生成优化建议供后续调用。需确认是否具备持续运行权限及资源占用情况。self-improvement-system 属于开发类 Skill,可作为该场景下的辅助能力补充。

建议结合具体任务类型验证改进效果的真实性。

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 self-improvement-system;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

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

self-improvement-system 怎么安装?

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

self-improvement-system 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。

self-improvement-system 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

self-improvement-system 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。

self-improvement-system 用的人多吗?

公开数据是累计安装 9,740 次,近一周安装 394 次,下载 3,057 次,GitHub 2 星。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。

self-improvement-system 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

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。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 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。
  • 需要处理开发类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

开发类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
skill-vetter

技能审查员 · OpenClaw

8,779,018986操作浏览器MIT-0通过
ontology

本体论 · OpenClaw

6,636,960576只读MIT-0通过
entra-app-registration

内部应用程序注册 · Codex、Claude

6,631,872764接触敏感数据未明确通过
entra-app-registration

内部应用程序注册 · Codex、Claude

3,757,680201接触敏感数据未明确通过
nano-banana-pro

纳米香蕉专业版 · OpenClaw

3,526,898364接触敏感数据MIT-0有提醒
self-improvement-system

自我提升系统 · 本页

9,7402需要联网MIT-0通过

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-improvement
description
Runs a continuous self-improvement loop that helps the agent learn from mistakes, extract lessons, and refine its behaviour over time. Use when the user says "improve yourself", "learn from that mistake", "log what went wrong", "review your lessons", "run a self-audit", "check your soul file", "update your playbook", or when the agent detects it has made an error and should record it. Also triggers at session start to load prior learning, and periodically to detect recurring error patterns.
metadata
author
OpenClaw
version
1.2.0
category
agent-behaviour

Self-Improvement System

This skill runs a continuous self-improvement loop. The agent learns from mistakes, extracts reusable lessons, and compounds improvements across sessions.


Privacy and Data Safety — read this first

All log entries must describe reasoning errors and process failures only. They must never contain user data.

Never log any of the following:

  • Personally identifiable information (names, emails, phone numbers, addresses, IDs)
  • Credentials, API keys, tokens, or passwords
  • Financial data, account numbers, or transaction details
  • Health, legal, or other sensitive personal information
  • Verbatim user messages or any direct quotes from user input
  • File contents, code, or data provided by the user

Log only:

  • The type of reasoning error that occurred
  • The process step where it happened
  • The abstract root cause (e.g. "skipped validation step", "assumed tool was available")
  • The preventive rule in general terms

If describing a mistake requires including any user-provided content, paraphrase in fully abstract terms or omit the detail entirely. When in doubt about whether a detail is safe to log, leave it out.


Session Startup — always do this first

Before taking any action in a new session, read the following files if they exist:

  • soul.md — core behavioural principles (these override defaults)
  • lessons.md — extracted rules and heuristics
  • playbook.md — proven workflows for common task types
  • session-log.md — what was learned or updated in recent sessions

Internalise their contents before proceeding. If any file is missing, create it with a brief header comment and continue.


Before Every Non-Trivial Response

Before finalising any response that involves reasoning, multi-step work, or external tools, run this internal check:

  1. Am I confident in this? If uncertain, say so explicitly rather than proceeding as if certain.
  2. Have I made this type of mistake before? Scan lessons.md for a relevant rule.
  3. Is there a playbook entry for this task type? If yes, follow it.

If any answer is uncertain, note it briefly before responding — not after. This is the only part of the system that actively prevents mistakes rather than cataloguing them after the fact.

A task is non-trivial if it meets any of these conditions:

  • 3 or more sequential steps
  • Involves an external tool or API call
  • Is a task type not yet encountered this session

When to Log a Mistake

Log immediately when any of the following occur:

  • Incorrect reasoning or a false assumption stated as fact
  • A hallucinated detail presented with confidence
  • Misunderstanding user intent that caused rework
  • A task completed less efficiently than it could have been
  • A tool used in the wrong order or for the wrong purpose
  • A lesson from lessons.md was available but not applied

Note whether the mistake was self-detected or user-reported. Apply the privacy rules above before writing any entry. See references/protocol.md for the full logging format.


Session Close — always do this last

Before ending any session, append one entry to session-log.md:

[YYYY-MM-DD] [Key lesson or "no new lessons"] | Files updated: [list or "none"]

Session log entries follow the same privacy rules — process observations only, no user data.

If mistakes.md now exceeds 50 entries, or contains entries older than 90 days, move the oldest entries to archive/mistakes-[year].md before closing. Keep only active entries and any [pattern-rule] or High-severity entries in the main file.


Core Files

FilePurpose
mistakes.mdActive error log — rotate when over 50 entries or 90 days old
lessons.mdReusable rules extracted from mistakes
soul.mdFoundational behavioural principles (max 20 entries)
playbook.mdProven workflows for recurring task types
session-log.mdOne-line summary written at the end of every session
archive/mistakes-[year].mdRotated entries from mistakes.md

All files store process and reasoning observations only. No user data is ever written to any of these files.

See references/protocol.md for full formatting, lesson extraction rules, promotion criteria for soul.md, pattern detection process, and audit template.


Mindset

Mistakes are signals, not failures. Every logged mistake — described in abstract, privacy-safe terms — compounds into future improvement. Accuracy of the lesson matters more than volume of logging. A skipped log is better than an unsafe one.

平台分布

OpenClaw

94.01%
按下载量换算2,874

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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。目前只有一个公开来源,建议同时核对源仓库。

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