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richardan01

self_improving_agent

作者 richieriri · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ✓ 安全检测通过
264
总下载
0
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0
当前安装
1
版本数
在 OpenClaw 中安装
/install selfimproving
功能描述
Captures learnings, errors, and corrections to enable continuous improvement. Use when: (1) A command or operation fails unexpectedly, (2) User corrects Clau...
安全使用建议
This skill appears to do what it says: add lightweight reminders and local logging of errors/learnings and offer a helper to scaffold new skills from those learnings. Before installing or enabling hooks: (1) verify the repository/source you clone matches the registry entry (the included _meta.json shows different metadata—confirm origin), (2) review the scripts (they only emit text and manipulate files in your workspace, but inspect extract-skill.sh before running to confirm it meets your expectations), (3) prefer project-level hook configuration rather than user-global if you want to avoid affecting all sessions, and (4) be aware that promoted files (CLAUDE.md, SOUL.md, etc.) are injected into agent context and will change agent behavior—treat that as a deliberate prompt-injection mechanism and review promoted content before making it global.
功能分析
Type: OpenClaw Skill Name: selfimproving Version: 1.0.0 The self-improving skill bundle is designed to help an AI agent log errors, user corrections, and best practices into markdown files (e.g., .learnings/LEARNINGS.md) to improve future performance. It includes shell scripts (activator.sh, error-detector.sh, extract-skill.sh) for automated reminders and skill scaffolding, as well as OpenClaw hooks (handler.js) that inject reminders into the agent's bootstrap context. The code and instructions are transparent, lack obfuscation, and contain no evidence of data exfiltration or malicious intent; the behavior is entirely consistent with the stated goal of creating a feedback loop for agent self-improvement.
能力评估
Purpose & Capability
The skill is designed to capture learnings/errors and promote them into workspace files or new skills. The included scripts (activator, error-detector, extract-skill) and OpenClaw/handler hooks all relate directly to that purpose. Minor inconsistency: packaged _meta.json and the registry metadata show different ownerId/version timestamps, which could indicate the bundled files came from a different upstream snapshot than the registry entry—worth verifying the source before install.
Instruction Scope
SKILL.md and hooks instruct the agent to create and update .learnings/ files, inject reminder content into sessions, and optionally promote entries into workspace-level files (CLAUDE.md, AGENTS.md, SOUL.md). The scripts only read platform-provided environment (CLAUDE_TOOL_OUTPUT in error-detector) and operate on local workspace paths. This is within the skill's stated scope, but the promotion/injection behavior means the skill can change the agent's context (prompt injection effect) if enabled globally—this is expected for this class of tool but important to understand.
Install Mechanism
There is no automated install spec; installation is manual (clawdhub install or git clone from GitHub). All included code is in the package (no remote downloads or network installs during runtime). The git URL referenced is a normal GitHub repository. No high-risk download URLs or archive extraction steps are present.
Credentials
The skill does not request credentials or environment variables. The error-detector script reads a platform-provided variable (CLAUDE_TOOL_OUTPUT) to detect failures—this is proportional to detecting tool errors. Scripts write to project/user workspace (e.g., ~/.openclaw/workspace/.learnings or ./ .learnings) which is consistent with logging behavior; extract-skill.sh validates output paths to avoid writing outside the workspace.
Persistence & Privilege
always:false and no primary credential are good. However, the documentation encourages copying hooks to user-level locations (~/.openclaw/hooks) and enabling them in user/global settings; if a user enables those hooks globally, the reminder injection will affect every session for that user. This is intentional for the skill but increases its reach—enable at project scope if you want to limit impact.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install selfimproving
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /selfimproving 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.0
self improving agent
元数据
Slug selfimproving
版本 1.0.0
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 1
常见问题

self_improving_agent 是什么?

Captures learnings, errors, and corrections to enable continuous improvement. Use when: (1) A command or operation fails unexpectedly, (2) User corrects Clau... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 264 次。

如何安装 self_improving_agent?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install selfimproving」即可一键安装,无需额外配置。

self_improving_agent 是免费的吗?

是的,self_improving_agent 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

self_improving_agent 支持哪些平台?

self_improving_agent 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 self_improving_agent?

由 richieriri(@richardan01)开发并维护,当前版本 v1.0.0。

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