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Agent Skill · 效率

skill-experience-layer(技能经验层)

skill-experience-layer 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。

skill-experience-layer 是效率类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为只读;许可证为 MIT-0,第三方扫描有提醒项,按安装量在效率类的 21,003 个 Skill 里排第 9,236。

总安装 6,233周安装 265GitHub Stars 公开资料未说明下载 2,184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:skill-experience-layer(技能经验层)
来源仓库:https://github.com/jilanfang/skill-experience-layer
安装命令:
openclaw skills install skill-experience-layer
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 skill-experience-layer

这个 Skill 是做什么的

效率类 Skill 把重复的整理、汇总、转换工作交给助手完成,减少手工复制粘贴。

通话前检查经验库避免重复错误发生。skill-experience-layer 属于效率类 Skill,可作为该场景下的辅助能力补充。分层存储错误案例驱动 Agent 自我进化。适用于高可靠性要求的对话型应用场景。需定期归档旧经验以防内存膨胀影响性能。建议设置经验置信度阈值过滤低价值条目。

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 skill-experience-layer;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

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

skill-experience-layer 怎么安装?

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

skill-experience-layer 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。

skill-experience-layer 需要什么权限?

权限级别是只读。该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

skill-experience-layer 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。

skill-experience-layer 用的人多吗?

公开数据是累计安装 6,233 次,近一周安装 265 次,下载 2,184 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。

skill-experience-layer 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

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。
  • 标注为只读权限,试用阶段不容易误改本地文件。

先想清楚的地方

  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 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。
  • 需要处理效率类任务、又不想每次都手写提示词的人。
  • 只想先试试效果、不希望它改动本地文件的人。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

效率类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
github

GitHub 代码协作 · OpenClaw

7,632,918545需要联网MIT-0通过
nano-pdf

Nano PDF 文档 · OpenClaw

4,249,476231需要联网MIT-0通过
auto-updater

自动更新程序 · OpenClaw

2,964,063386操作浏览器MIT-0通过
video-frames

视频帧 · OpenClaw

2,621,304112需要联网MIT-0通过
gemini

Gemini 助手 · OpenClaw

2,547,39648需要联网MIT-0有提醒
skill-experience-layer

技能经验层 · 本页

6,233—只读MIT-0有提醒

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
skill-experience-layer
slug
jilanfang/skill-experience-layer
version
1.0.0
description
Pre-call experience checking + error-driven learning + layered experience storage. Avoid repeating mistakes, get smarter every time. Pre-integrated with self-improving and capability-evolver.
read_when
metadata
author
jilanfang
license
MIT
tags
self-improvement, memory, experience, learning, best-practices

Skill Experience Layer - 技能经验分层机制

What it does

Give your agent permanent, per-skill experience memory that:

  1. Pre-call checking: Before you call any skill, *automatically read the common mistakes and best practices* so you avoid repeating the same pitfalls
  2. Error-driven learning: When you make a mistake, *immediately record it to the corresponding skill's experience file*
  3. Automatic evolution: Integrates with capability-evolver to automatically improve experience when mistakes repeat
  4. Fits existing memory architecture: Works with your existing (instant → hot → short-term → long-term) memory hierarchy

Why this is different

ApproachTraditionalskill-experience-layer
When lessons are applied*After* mistake (retrospective)*Before* next call (preventive)
Experience storageGlobal reflectionPer-skill JSON files, easy to evolve
IntegrationSelf-contained, requires manual integrationDesigned to fit your existing memory hierarchy
Automatic evolutionNo built-in supportFirst-class integration with capability-evolver

Workflow

1. For every skill you use

When you install a new skill:

  • Create memory/experiences/{skill-name}.json
  • Define commonMistakes[] and bestPractices[]
  • *Before every invocation*, read it and remind yourself what to avoid

2. When you make a mistake

  1. Stop immediately, don't keep retrying
  2. Record the mistake in self-improving/corrections.md
  3. Update the skill's experience JSON:

- Add new entry to experiences[] with context, lesson, prevention - Update commonMistakes[] if it's a new pattern - Increment failureCount

  1. If the *same mistake repeats 2+ times*, trigger capability-evolver to automatically improve the experience and best practices
  2. Retry after updating

3. Regular maintenance

  • Daily: End-of-day review mistakes, update experiences
  • Weekly: Promote key lessons to long-term memory in MEMORY.md
  • Automatic: capability-evolver can continuously improve stale experiences

Example experience file

{
  "type": "skill",
  "name": "my-skill",
  "lastUpdated": "2026-03-17T19:00:00+08:00",
  "totalExecutions": 10,
  "successCount": 8,
  "failureCount": 2,
  "experiences": [
    {
      "id": "exp-my-skill-001",
      "timestamp": "2026-03-10T20:00:00+08:00",
      "context": "What you were doing when the mistake happened",
      "lesson": "What you learned",
      "severity": "medium",
      "timesRepeated": 1,
      "prevention": "What to do differently next time"
    }
  ],
  "patterns": {
    "commonMistakes": [
      "First common mistake to avoid",
      "Second common mistake"
    ],
    "bestPractices": [
      "First best practice",
      "Second best practice"
    ]
  }
}

Integration with existing systems

Works great with:

  • self-improving: Uses the correction/reflection workflow
  • capability-evolver: Automatic experience improvement when mistakes repeat
  • Your existing memory hierarchy: Experiences live in memory/experiences/, fits alongside your daily/weekly memory

Safety Boundaries

  • ✅ Only modifies experience files in memory/experiences/ and skill-owned files
  • ✅ Never touches core root files (MEMORY.md, SOUL.md, IDENTITY.md, USER.md, .env) — reads but does not write
  • ✅ Never accesses sensitive directories (~/.ssh, ~/.aws, /etc)
  • ✅ All automatic changes are backed up before applying, can roll back
  • ✅ High-risk changes require human approval

Author

Created from practical experience integrating self-improvement into a working agent. This is the missing glue between general self-improving frameworks and your actual daily usage.

Get smarter with every mistake, don't repeat the same pit twice.

平台分布

OpenClaw

78.29%
按下载量换算1,710

安全审计

VirusTotal

通过

ClawScan

可疑

Static analysis

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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