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Agent Skill · 研究检索

self-improving-openclaw(self improving OpenClaw 搜索)

self-improving-openclaw 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-openclaw 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为执行命令;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 30,927。

总安装 2,794周安装 120GitHub Stars 公开资料未说明下载 979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:self-improving-openclaw(self improving OpenClaw 搜索)
来源仓库:https://github.com/muhamadbasim/self-improving-openclaw
安装命令:
openclaw skills install self-improving-openclaw
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-openclaw

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。

将更正、错误与功能请求结构化记录至学习文件,并提升至分层内存中复用。适用于希望系统化沉淀工作成果、减少重复问题的 OpenClaw 用户。通过 clawhub 安装,需确认 .learnings/ 目录的写入权限与版本兼容性。建议定期备份学习记录,防止数据丢失影响后续改进效果。

self-improving-openclaw 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

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-openclaw;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

    它需要执行命令,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-openclaw 怎么安装?

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

self-improving-openclaw 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-openclaw 需要什么权限?

权限级别是执行命令。安装流程涉及命令执行,可能通过 openclaw skills install self-improving-openclaw 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

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

公开数据是累计安装 2,794 次,近一周安装 120 次,下载 979 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。

self-improving-openclaw 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

  • 需要执行命令,装到常用环境前要先评估影响面。
  • 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

  • 不能接受 Skill 在你机器上执行命令的人。
  • 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 · OpenClaw

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
self-improving-openclaw

self improving OpenClaw 搜索 · 本页

2,794—执行命令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-improving-openclaw
description
>-
metadata
openclaw
emoji
\F9E0
homepage
https://github.com/muhamadbasim/oktoclaw

Self-Improving OpenClaw

Structured learning loop for OpenClaw agents: capture → review → promote → maintain.

Quick Start

On first activation, run the init script to create workspace directories:

bash scripts/init-workspace.sh

This creates .learnings/ and .self-improving/ in the workspace root.

Core Workflow

1. Capture

When a learning signal fires, log it to the right file in .learnings/:

SignalTarget fileCategory
User corrects youLEARNINGS.mdcorrection
User says "always/never do X"LEARNINGS.mdpreference
You discover something non-obviousLEARNINGS.mdinsight
Your knowledge was outdatedLEARNINGS.mdknowledge_gap
Found a better approachLEARNINGS.mdbest_practice
Command returns non-zeroERRORS.md—
Tool/API fails unexpectedlyERRORS.md—
User wants missing capabilityFEATURE_REQUESTS.md—

Use the entry format defined in references/logging-format.md.

2. Review

During heartbeat or manual review, scan .learnings/ and evaluate each pending item:

  1. Check recurrence — search for similar entries.
  2. If related entry exists: bump Recurrence-Count, link with See Also.
  3. If Recurrence-Count >= 3 within 30 days: add to .learnings/REVIEW_QUEUE.md.
  4. Update .self-improving/heartbeat-state.md with review timestamp.

See references/heartbeat-review.md for the full review procedure.

3. Promote

Move validated patterns up the memory tiers:

ConditionPromote toExample
Pattern used 2x, context-specific.self-improving/domains/*.md or projects/*.md"This repo uses pnpm"
Pattern used 3x in 7-30 days, cross-task.self-improving/HOT.md"User prefers concise answers"
Stable, long-term applicableSOUL.md / AGENTS.md / TOOLS.md / MEMORY.md"Never force push"

See references/promotion-rules.md for the full promotion/demotion rules.

4. Maintain

Periodically (every 1-2 weeks during heartbeat):

  • Demote HOT entries unused for 30 days → domain/project memory.
  • Archive domain/project entries unused for 90 days → .self-improving/archive/.
  • Compact files exceeding size limits: merge similar entries, summarize verbose ones.
  • Never delete without explicit user confirmation. Prefer archive over delete.

Detection Triggers

Log automatically when you notice these signals:

Corrections (→ LEARNINGS.md, category: correction):

  • "No, that's not right..."
  • "Actually, it should be..."
  • "You're wrong about..."
  • "I told you before..."
  • "Stop doing X"

Preferences (→ LEARNINGS.md, category: preference):

  • "I like when you..."
  • "Always do X for me"
  • "Never do Y"
  • "My style is..."

Feature requests (→ FEATURE_REQUESTS.md):

  • "Can you also..."
  • "I wish you could..."
  • "Is there a way to..."

Errors (→ ERRORS.md):

  • Non-zero exit codes
  • Exceptions or stack traces
  • Timeout or connection failure

Ignore (don't log):

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

Memory Tiers

TierLocationSize limitBehavior
RAW.learnings/UnlimitedIntake only, not auto-loaded
HOT.self-improving/HOT.md≤80 linesAlways loaded at session start
WARM.self-improving/domains/, projects/≤200 lines eachLoad on context match
COLD.self-improving/archive/UnlimitedLoad on explicit query

Conflict Resolution

When patterns contradict:

  1. Most specific wins: project > domain > HOT/global.
  2. Same level: most recent wins.
  3. If ambiguous: ask user.

Promotion Targets

When a learning is stable enough for permanent workspace memory:

Learning typePromote toExample
Behavioral patternsSOUL.md"Be concise, avoid disclaimers"
Workflow improvementsAGENTS.md"Spawn sub-agents for long tasks"
Tool gotchasTOOLS.md"Git push needs auth first"
User preferences/decisionsMEMORY.md"Basim prefers Indonesian for casual chat"

Mark promoted entries as Status: promoted with Promoted-To: <file>.

Transparency

  • When applying a learned pattern, cite the source: (from HOT.md) or (from domains/coding.md:15).
  • On "memory stats" request, report entry counts per tier.
  • On "what have you learned?" request, show recent corrections and HOT entries.

Security Boundaries

  • Never store credentials, API keys, tokens, or passwords.
  • Never store health data or sensitive personal information.
  • Never store third-party private data.
  • Redact secrets in error logs — use [REDACTED] placeholders.
  • Only write to memory files in private/local workspace sessions.

Workspace Layout

See references/workspace-layout.md for the complete directory structure and file descriptions.

Resources

scripts/

  • init-workspace.sh — Create .learnings/ and .self-improving/ directories with template files.

references/

  • logging-format.md — Entry format for learnings, errors, and feature requests.
  • promotion-rules.md — Full promotion, demotion, and archival rules.
  • heartbeat-review.md — Heartbeat review procedure and state tracking.
  • workspace-layout.md — Complete directory structure reference.

assets/

  • Template files copied by init-workspace.sh into the workspace.

平台分布

OpenClaw

79.25%
按下载量换算776

安全审计

VirusTotal

未展示

ClawScan

通过

Static analysis

通过

权限和风险

执行命令

安装流程涉及命令执行,可能通过 openclaw skills install self-improving-openclaw 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

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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