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keenlycat-self-improving-agent敏锐猫自我提升剂

Agent Skill

keenlycat-self-improving-agent 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

8,299

周安装

339

GitHub Stars

公开资料未说明

下载量

2,685
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:keenlycat-self-improving-agent(敏锐猫自我提升剂)
来源仓库:https://github.com/keenlycat/keenlycat-self-improving-agent
安装命令:
openclaw skills install keenlycat-self-improving-agent
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 keenlycat-self-improving-agent

简介

持续捕获并应用从错误、用户更正、成功任务和定期审查中学到的知识,以提高代理绩效。

SKILL.md

name
self-improving-agent
description
Captures learnings, errors, and corrections to enable continuous improvement. Use when: (1) A command or operation fails unexpectedly, (2) User corrects the agent's behavior, (3) Agent completes a complex task successfully, (4) Periodic review of past learnings.

Self-Improving Agent

Overview

This skill enables OpenClaw to continuously improve by capturing learnings from:

  • Failed operations and errors
  • User corrections and feedback
  • Successful complex task completions
  • Periodic review and consolidation

When to Use

  1. Error Recovery: When a command or operation fails unexpectedly
  2. User Correction: When the user corrects the agent's behavior or output
  3. Success Capture: After completing a complex task successfully
  4. Learning Review: Periodic review of past learnings to consolidate knowledge

Core Workflow

1. Capture Learning

When something noteworthy happens:

Type: error | correction | success | insight
Context: What was being attempted
Issue: What went wrong (for errors)
Correction: What should be done differently
Lesson: Generalizable takeaway
Tags: Relevant topics/skills

2. Store Learning

Learnings are stored in memory/learnings.jsonl with:

  • Timestamp
  • Type and severity
  • Full context and details
  • Tags for searchability

3. Retrieve Relevant Learnings

Before starting a task, search past learnings:

  • Match by task type
  • Match by tags
  • Match by error patterns

4. Apply Learnings

Use retrieved learnings to:

  • Avoid past mistakes
  • Apply successful patterns
  • Adjust approach based on corrections

Memory Structure

Learnings are stored in JSONL format:

{
  "timestamp": "2026-03-06T10:30:00Z",
  "type": "error",
  "severity": "high",
  "context": "Installing npm package globally",
  "issue": "Permission denied without sudo",
  "correction": "Use sudo for global installs or configure npm prefix",
  "lesson": "Always check if operation requires elevated privileges",
  "tags": ["npm", "permissions", "installation"],
  "taskSlug": "npm-global-install"
}

Learning Types

TypeWhen to UseExample
errorOperation failedCommand returned non-zero exit code
correctionUser corrected behavior"Don't use rm, use trash instead"
successComplex task completedSuccessfully deployed to production
insightDiscovered optimization"This API is faster than alternatives"

Severity Levels

  • critical: System-breaking errors, data loss risk
  • high: Task-blocking errors, significant issues
  • medium: Minor issues, workarounds available
  • low: Optimization opportunities, nice-to-know

Commands

Capture a Learning

# Manual capture (for user corrections)
openclaw memory add-learning --type correction --context "..." --lesson "..."

Search Learnings

# Search by keyword
openclaw memory search-learnings "npm permissions"

# Search by tag
openclaw memory search-learnings --tag npm

# Search by type
openclaw memory search-learnings --type error

Review Learnings

# Review recent learnings
openclaw memory review-learnings --days 7

# Review by category
openclaw memory review-learnings --tag deployment

Best Practices

  1. Capture Immediately: Record learnings while context is fresh
  2. Be Specific: Include full error messages and exact commands
  3. Generalize Lessons: Extract principles that apply beyond this instance
  4. Tag Thoughtfully: Use consistent tags for easy retrieval
  5. Review Regularly: Weekly review helps consolidate knowledge
  6. Avoid Duplicates: Check existing learnings before adding new ones

Integration Points

  • Error Handlers: Automatically capture command failures
  • User Feedback: Listen for correction patterns in conversation
  • Task Completion: Prompt for learning capture after complex tasks
  • Heartbeat: Include learning review in periodic checks

Example Scenarios

Scenario 1: Command Failure

Context: Running `npm install -g package`
Issue: EACCES permission error
Correction: Run with sudo or configure npm prefix
Lesson: Check if global install requires elevated privileges
Tags: npm, permissions, installation

Scenario 2: User Correction

Context: Suggested using `rm -rf` for cleanup
Correction: User prefers `trash` for safety
Lesson: Default to safe, reversible operations
Tags: safety, file-operations, user-preference

Scenario 3: Success Pattern

Context: Deploying to VPS via SSH
Success: Used rsync with specific flags for reliability
Lesson: rsync -avz --delete is reliable for deployments
Tags: deployment, ssh, rsync, success

Safety Rules

  • Never store sensitive data (passwords, API keys, tokens)
  • Sanitize error messages that might contain secrets
  • Require user approval before storing corrections
  • Allow users to delete or edit learnings
  • Respect user privacy preferences

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

73.55%
按下载量换算1,975

安全审计

VirusTotal

通过

ClawScan

可疑

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。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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