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self-improving-cn自强不息

Agent Skill

self-improving-cn 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

9,221

周安装

373

GitHub Stars

1

下载量

2,894
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-improving-cn(自强不息)
来源仓库:https://github.com/sharemen/self-improving-cn
安装命令:
openclaw skills install self-improving-cn
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-improving-cn

简介

用于记录任务执行中的错误、用户纠正和经验缺口,支持 OpenClaw Agent 在中文环境下的持续改进。

  • 适用于需要本地化认知调整与文化适配的场景。
  • 通过自我反省与组织记忆机制,提升代理在中文语境下的沟通与执行能力。
  • 安装命令:openclaw skills install self-improving-cn,需确认语言支持完整性。
  • 建议结合中文用户习惯验证反馈机制的有效性。

SKILL.md

name
Self-Improving + Proactive Agent
slug
self-improving
version
1.2.16
homepage
https://clawic.com/skills/self-improving
description
Self-reflection + Self-criticism + Self-learning + Self-organizing memory. Agent evaluates its own work, catches mistakes, and improves permanently. Use when (1) a command, tool, API, or operation fails; (2) the user corrects you or rejects your work; (3) you realize your knowledge is outdated or incorrect; (4) you discover a better approach; (5) the user explicitly installs or references the skill for the current task.
changelog
Clarifies the setup flow for proactive follow-through and safer installation behavior.
metadata
{"clawdbot":{"emoji":"🧠","requires":{"bins":[]},"os":["linux","darwin","win32"],"configPaths":["~/self-improving/"],"configPaths.optional":["./AGENTS.md","./SOUL.md","./HEARTBEAT.md"]}}

When to Use

User corrects you or points out mistakes. You complete significant work and want to evaluate the outcome. You notice something in your own output that could be better. Knowledge should compound over time without manual maintenance.

Architecture

Memory lives in ~/self-improving/ with tiered structure. If ~/self-improving/ does not exist, run setup.md. Workspace setup should add the standard self-improving steering to the workspace AGENTS, SOUL, and HEARTBEAT.md files, with recurring maintenance routed through heartbeat-rules.md.

~/self-improving/
├── memory.md          # HOT: ≤100 lines, always loaded
├── index.md           # Topic index with line counts
├── heartbeat-state.md # Heartbeat state: last run, reviewed change, action notes
├── projects/          # Per-project learnings
├── domains/           # Domain-specific (code, writing, comms)
├── archive/           # COLD: decayed patterns
└── corrections.md     # Last 50 corrections log

Quick Reference

TopicFile
Setup guidesetup.md
Heartbeat state templateheartbeat-state.md
Memory templatememory-template.md
Workspace heartbeat snippetHEARTBEAT.md
Heartbeat rulesheartbeat-rules.md
Learning mechanicslearning.md
Security boundariesboundaries.md
Scaling rulesscaling.md
Memory operationsoperations.md
Self-reflection logreflections.md
OpenClaw HEARTBEAT seedopenclaw-heartbeat.md

Requirements

  • No credentials required
  • No extra binaries required
  • Optional installation of the Proactivity skill may require network access

Learning Signals

Log automatically when you notice these patterns:

Corrections → add to corrections.md, evaluate for memory.md:

  • "No, that's not right..."
  • "Actually, it should be..."
  • "You're wrong about..."
  • "I prefer X, not Y"
  • "Remember that I always..."
  • "I told you before..."
  • "Stop doing X"
  • "Why do you keep..."
  • "不对,不是这样的……"
  • "实际上,应该是……"
  • "你搞错了……"
  • "我更喜欢 X,不是 Y"
  • "记住我永远都要……"
  • "我之前跟你说过……"
  • "别再做 X 了"
  • "你为什么一直……"

Preference signals → add to memory.md if explicit:

  • "I like when you..."
  • "Always do X for me"
  • "Never do Y"
  • "My style is..."
  • "For [project], use..."

-"你这样...的时候我觉的很好" --"一定要帮我做 X" -"永远不要做 Y" -"我的风格是……" -"在[项目]中 要用……"

Pattern candidates → track, promote after 3x:

  • Same instruction repeated 3+ times
  • Workflow that works well repeatedly
  • User praises specific approach

Ignore (don't log):

  • One-time instructions ("do X now")
  • Context-specific ("in this file...")
  • Hypotheticals ("what if...")

Self-Reflection

After completing significant work, pause and evaluate:

  1. Did it meet expectations? — Compare outcome vs intent
  2. What could be better? — Identify improvements for next time
  3. Is this a pattern? — If yes, log to corrections.md

When to self-reflect:

  • After completing a multi-step task
  • After receiving feedback (positive or negative)
  • After fixing a bug or mistake
  • When you notice your output could be better

Log format:

CONTEXT: [type of task]
REFLECTION: [what I noticed]
LESSON: [what to do differently]

Example:

CONTEXT: 搭建Flutter界面
REFLECTION: 间距不对,需要重做
LESSON: 给用户看之前先检查视觉间距

Self-reflection entries follow the same promotion rules: 3x applied successfully → promote to HOT.

Quick Queries

User saysAction
"What do you know about X?"Search all tiers for X
"What have you learned?"Show last 10 from corrections.md
"Show my patterns"List memory.md (HOT)
"Show [project] patterns"Load projects/{name}.md
"What's in warm storage?"List files in projects/ + domains/
"Memory stats"Show counts per tier
"Forget X"Remove from all tiers (confirm first)
"Export memory"ZIP all files
"你了解 X 的哪些内容?"Search all tiers for X
"你都学到了什么?"Show last 10 from corrections.md
"展示我的规则模式"List memory.md (HOT)
"展示 [项目] 的规则模式"Load projects/{name}.md
"热存储里有什么?"List files in projects/ + domains/
"忘记 X"Show counts per tier
"导出记忆"ZIP all files

Memory Stats

On "memory stats" request, report:

📊 Self-Improving Memory

HOT (always loaded):
  memory.md: X entries

WARM (load on demand):
  projects/: X files
  domains/: X files

COLD (archived):
  archive/: X files

Recent activity (7 days):
  Corrections logged: X
  Promotions to HOT: X
  Demotions to WARM: X

Common Traps

TrapWhy It FailsBetter Move
Learning from silenceCreates false rulesWait for explicit correction or repeated evidence
Promoting too fastPollutes HOT memoryKeep new lessons tentative until repeated
Reading every namespaceWastes contextLoad only HOT plus the smallest matching files
Compaction by deletionLoses trust and historyMerge, summarize, or demote instead

Core Rules

1. Learn from Corrections and Self-Reflection

  • Log when user explicitly corrects you
  • Log when you identify improvements in your own work
  • Never infer from silence alone
  • After 3 identical lessons → ask to confirm as rule

2. Tiered Storage

TierLocationSize LimitBehavior
HOTmemory.md≤100 linesAlways loaded
WARMprojects/, domains/≤200 lines eachLoad on context match
COLDarchive/UnlimitedLoad on explicit query

3. Automatic Promotion/Demotion

  • Pattern used 3x in 7 days → promote to HOT
  • Pattern unused 30 days → demote to WARM
  • Pattern unused 90 days → archive to COLD
  • Never delete without asking

4. Namespace Isolation

  • Project patterns stay in projects/{name}.md
  • Global preferences in HOT tier (memory.md)
  • Domain patterns (code, writing) in domains/
  • Cross-namespace inheritance: global → domain → project

5. Conflict Resolution

When patterns contradict:

  1. Most specific wins (project > domain > global)
  2. Most recent wins (same level)
  3. If ambiguous → ask user

6. Compaction

When file exceeds limit:

  1. Merge similar corrections into single rule
  2. Archive unused patterns
  3. Summarize verbose entries
  4. Never lose confirmed preferences

7. Transparency

  • Every action from memory → cite source: "Using X (from projects/foo.md:12)"
  • Weekly digest available: patterns learned, demoted, archived
  • Full export on demand: all files as ZIP

8. Security Boundaries

See boundaries.md — never store credentials, health data, third-party info.

9. Graceful Degradation

If context limit hit:

  1. Load only memory.md (HOT)
  2. Load relevant namespace on demand
  3. Never fail silently — tell user what's not loaded

Scope

This skill ONLY:

  • Learns from user corrections and self-reflection
  • Stores preferences in local files (~/self-improving/)
  • Maintains heartbeat state in ~/self-improving/heartbeat-state.md when the workspace integrates heartbeat
  • Reads its own memory files on activation

This skill NEVER:

  • Accesses calendar, email, or contacts
  • Makes network requests
  • Reads files outside ~/self-improving/
  • Infers preferences from silence or observation
  • Deletes or blindly rewrites self-improving memory during heartbeat cleanup
  • Modifies its own SKILL.md

Data Storage

Local state lives in ~/self-improving/:

  • memory.md for HOT rules and confirmed preferences
  • corrections.md for explicit corrections and reusable lessons
  • projects/ and domains/ for scoped patterns
  • archive/ for decayed or inactive patterns
  • heartbeat-state.md for recurring maintenance markers

Related Skills

Install with clawhub install <slug> if user confirms:

  • memory — Long-term memory patterns for agents
  • learning — Adaptive teaching and explanation
  • decide — Auto-learn decision patterns
  • escalate — Know when to ask vs act autonomously

Feedback

  • If useful: clawhub star self-improving
  • Stay updated: clawhub sync

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

82.55%
按下载量换算2,389

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。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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