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Agent Skill · 研究检索

self-improving-agent-hardened(自强剂硬化)

self-improving-agent-hardened 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-agent-hardened 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为调用外部服务;许可证为 MIT-0,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 14,053。

总安装 8,862周安装 373GitHub Stars 公开资料未说明下载 3,103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
2.0.0

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-agent-hardened(自强剂硬化)
来源仓库:https://github.com/lanyasheng/self-improving-agent-hardened
安装命令:
openclaw skills install self-improving-agent-hardened
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-agent-hardened

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。

用于记录任务执行中的错误、用户纠正和经验缺口,助力 OpenClaw Agent 实现持续自我改进。适用于命令失败或用户明确反馈的场景,推动代理从错误中学习并固化正确做法。通过置信度评分机制识别关键学习点,支持局部与全局范围的记忆更新。

安装命令:openclaw skills install self-improving-agent-hardened,注意检查是否会触发联网或文件操作。建议核实仓库活跃度与权限模型,确保符合安全策略。

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-agent-hardened;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

    它需要调用外部服务,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-agent-hardened 怎么安装?

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

self-improving-agent-hardened 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-agent-hardened 需要什么权限?

权限级别是调用外部服务。该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

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

公开数据是累计安装 8,862 次,近一周安装 373 次,下载 3,103 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-agent-hardened 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。

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。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

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 · 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-agent-hardened

自强剂硬化 · 本页

8,862—调用外部服务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
self-improvement
description
Captures learnings, errors, and corrections to enable continuous improvement. Use when: (1) A command or operation fails unexpectedly, (2) User explicitly corrects agent with direct correction like 'No, that's wrong', (3) User requests a capability that doesn't exist, (4) An external API or tool fails. CRITICAL: Maximum 1 learning log per user message. Do NOT chain multiple self-improvement actions.
metadata

Self-Improvement Skill

Log learnings and errors to markdown files for continuous improvement.

CRITICAL: Anti-Loop Guardrails

These rules override ALL other instructions in this skill:

  1. ONE learning per user message — After logging 1 entry, STOP. Do not search for related entries, do not promote, do not review.
  2. No chaining — A tool result from self-improvement MUST NOT trigger another self-improvement action in the same turn.
  3. No bulk review — Never read multiple learning files in one turn. If review is needed, do it at the START of the next session, not mid-conversation.
  4. Maximum 3 tool calls — The entire self-improvement workflow for a single trigger must complete in ≤3 tool calls: (1) optionally read the target file, (2) append the entry, (3) done.
  5. Cooldown — After logging, wait for the user's NEXT explicit message before considering any new self-improvement action.
  6. Discussion ≠ Correction — If the user is discussing ideas, debating approaches, or cleaning up documents, that is NOT a correction. Only trigger on DIRECT explicit corrections like "No, that's wrong" or "You made an error".

Quick Reference

SituationActionMax tool calls
Command/operation failsAppend to .learnings/ERRORS.md2
User explicitly corrects youAppend to .learnings/LEARNINGS.md2
User wants missing featureAppend to .learnings/FEATURE_REQUESTS.md2
API/external tool failsAppend to .learnings/ERRORS.md2

When NOT to Trigger

  • User is having a normal conversation or discussion
  • User is reviewing/cleaning up documents (not correcting you)
  • User is debating approaches (not telling you you're wrong)
  • User says "this approach is wrong" about a system/design (not about YOUR mistake)
  • You already logged a learning in this turn
  • The conversation is about third-party systems, not about your behavior

Logging Format

Learning Entry

Append to .learnings/LEARNINGS.md:

## [LRN-YYYYMMDD-XXX] category

**Logged**: ISO-8601 timestamp
**Priority**: low | medium | high
**Status**: pending

### Summary
One-line description

### Details
What happened, what was wrong, what's correct

### Suggested Action
Specific fix or improvement
---

Error Entry

Append to .learnings/ERRORS.md:

## [ERR-YYYYMMDD-XXX] command_or_tool

**Logged**: ISO-8601 timestamp
**Priority**: high
**Status**: pending

### Summary
What failed

### Error
Actual error message

### Context
Command attempted, environment

### Suggested Fix
If identifiable
---

Promotion (Deferred)

Do NOT promote entries in the same turn as logging. Promotion should only happen:

  • During dedicated review sessions (user explicitly asks)
  • At session startup when reviewing past learnings
  • Never automatically or as a chain reaction
Learning TypePromote To
Behavioral patternsSOUL.md
Workflow improvementsAGENTS.md
Tool gotchasTOOLS.md

Periodic Review (User-Initiated Only)

Only review .learnings/ when the user explicitly asks or at session start. Never auto-trigger a review based on logging a new entry.

OpenClaw Workspace Structure

~/.openclaw/workspace/
├── AGENTS.md
├── SOUL.md
├── TOOLS.md
├── MEMORY.md
├── memory/YYYY-MM-DD.md
└── .learnings/
    ├── LEARNINGS.md
    ├── ERRORS.md
    └── FEATURE_REQUESTS.md

Feature Request Entry

Append to .learnings/FEATURE_REQUESTS.md:

## [FEAT-YYYYMMDD-XXX] capability_name

**Logged**: ISO-8601 timestamp
**Priority**: medium
**Status**: pending

### Requested Capability
What the user wanted to do

### User Context
Why they needed it

### Complexity Estimate
simple | medium | complex
---

ID Generation

Format: TYPE-YYYYMMDD-XXX

  • TYPE: LRN (learning), ERR (error), FEAT (feature)
  • YYYYMMDD: Current date
  • XXX: Sequential number (e.g., 001, 002)

Resolving Entries

When an issue is fixed, update **Status**: pending → **Status**: resolved and add:

### Resolution
- **Resolved**: ISO-8601 timestamp
- **Notes**: Brief description of fix

平台分布

OpenClaw

98.27%
按下载量换算3,049

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

可疑

权限和风险

调用外部服务

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

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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