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Agent Skill · 研究检索

self-improving(自我完善)

self-improving 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-improving 是研究检索类 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,按安装量在研究检索类的 73,675 个 Skill 里排第 4。

总安装 6,829,284周安装 278,974GitHub Stars 1,046下载 2,209,474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
1.2.16

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(自我完善)
来源仓库:https://github.com/ivangdavila/self-improving
安装命令:
openclaw skills install self-improving
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

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

记录任务执行中的错误、用户纠正与能力短板,推动 Agent 持续学习与改进。适用于希望长期优化 Agent 行为模式与知识库完整性的高阶用户场景。自动沉淀经验教训,形成可复用的最佳实践模板供后续任务参考。需定期 review 修正记录,防止错误经验被固化传播。

不建议在公共共享 Agent 中开启此功能,以免泄露敏感操作历史。

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-improving;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-improving 怎么安装?

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

self-improving 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-improving 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-improving 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-improving 用的人多吗?

公开数据是累计安装 6,829,284 次,近一周安装 278,974 次,下载 2,209,474 次,GitHub 1,046 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-improving 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。
  • 来源仓库有 1,046 星,维护活跃度相对可查。

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。
  • 需要处理研究检索类任务、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。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · 本页

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
appinsights-instrumentation

应用洞察仪器 · Codex、Claude

6,440,568764执行命令未明确通过

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-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..."

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..."

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: Building Flutter UI
REFLECTION: Spacing looked off, had to redo
LESSON: Check visual spacing before showing user

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

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

平台分布

OpenClaw

93.58%
按下载量换算2,067,626

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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