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self-improvement-system自我提升系统

Agent Skill

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。

总安装

9,740

周安装

394

GitHub Stars

2

下载量

3,057
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: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

简介

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。

SKILL.md

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.

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

按任务关键词查找相关 Skills

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

94.01%
按下载量换算2,874

安全审计

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通过

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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。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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