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self-improving-agent-hardened自强剂硬化

Agent Skill

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。

总安装

8,862

周安装

373

GitHub Stars

公开资料未说明

下载量

3,103
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-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

简介

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。

SKILL.md

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

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

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

external-service

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 warning/failed 结果,不能写成本站确认安全。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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