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Agent Skill · 前端设计

self-improving-for-codex(self improving FOR Codex 命令行)

self-improving-for-codex 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 Codex、Claude、Cursor、Gemini CLI 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-for-codex 是前端设计类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为只读;许可证信息还不明确,第三方扫描已通过,按安装量在前端设计类的 10,216 个 Skill 里排第 5,013。

总安装 753周安装 32GitHub Stars 105下载 264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
unknown

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-for-codex(self improving FOR Codex 命令行)
来源仓库:https://github.com/godgod126/self-improving-for-codex
仓库路径:skills/self-improving-for-codex
安装命令:
npx skills add https://github.com/godgod126/self-improving-for-codex --skill self-improving-for-codex
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 npx skills 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。

skills.shnpx skills
npx skills add https://github.com/godgod126/self-improving-for-codex --skill self-improving-for-codex

这个 Skill 是做什么的

前端设计类 Skill 关注页面结构、视觉规范和组件写法,输出通常是可运行的界面代码。

self-improving-for-codex 用于记录任务执行中的错误、纠正和经验沉淀。适合希望 Agent 持续改进问题处理和最佳实践的场景。通过 npx skills add 命令安装,建议结合原始 README 了解具体用法。安装前应确认权限范围和维护状态,避免触发文件读写或网络请求。

涉及经验存储时应注意数据边界和隐私保护。

来源仓库内路径:skills/self-improving-for-codex

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 Codex、Claude、Cursor、Gemini CLI;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 npx skills add https://github.com/godgod126/self-improving-for-codex --skill sel…;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

self-improving-for-codex 怎么安装?

在终端执行 npx skills add https://github.com/godgod126/self-improving-for-codex --skill self-improvin…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

self-improving-for-codex 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。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-for-codex 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。

self-improving-for-codex 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

self-improving-for-codex 用的人多吗?

公开数据是累计安装 753 次,近一周安装 32 次,下载 264 次,GitHub 105 星。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-for-codex 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。
  • 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

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 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 Codex,想直接扩展它能力的人。
  • 需要处理前端设计类任务、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

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

前端设计类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
frontend-design

前端设计 · Codex、Claude

8,823,360126,072只读MIT通过
vercel-composition-patterns

Vercel 组合模式 · Codex、Claude

3,858,81625,893需要联网MIT通过
vercel-react-native-skills

Vercel React native skills 前端 · Codex、Claude

2,468,66425,878只读MIT通过
adapt

adapt 搜索 · Codex、Claude

2,027,04023,125操作浏览器未明确通过
bolder

bolder 分析 · Codex、Claude

2,004,28823,130只读未明确通过
self-improving-for-codex

self improving FOR Codex 命令行 · 本页

753105只读未明确通过

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

Self-improving for Codex

Overview

Use this skill to give Codex a durable, Codex-native self-improving loop without depending on OpenClaw-only primitives such as SOUL or HEARTBEAT.md.

This skill assumes one stable rule-entry file and one stable memory directory:

  • Global rule entry: ~/.codex/AGENTS.md
  • Global memory directory: prefer ~/.codex/memories/

Workflow

1. Audit the current state

Inspect these locations first:

  • global AGENTS.md
  • the candidate memory directory
  • any existing PROFILE.md, ACTIVE.md, LEARNINGS.md, ERRORS.md, FEATURE_REQUESTS.md
  • any existing automation related to nightly review or memory maintenance

If the environment already contains a partial setup, preserve it and extend it instead of replacing it blindly.

2. Establish the memory layout

Create or normalize these files in the global memory directory:

  • PROFILE.md
  • ACTIVE.md
  • LEARNINGS.md
  • ERRORS.md
  • FEATURE_REQUESTS.md

Read references/memory-files.md when creating or repairing these files.

Use this separation consistently:

  • PROFILE.md: long-term stable user profile and communication preferences
  • ACTIVE.md: compact high-priority rules worth reading at the start of future tasks
  • LEARNINGS.md: reusable learnings and corrections not yet promoted to top-level rules
  • ERRORS.md: reusable debugging and environment failure knowledge
  • FEATURE_REQUESTS.md: missing capabilities worth tracking across sessions

3. Wire the loop through AGENTS.md

Use AGENTS.md as the single Codex-native entry point.

Its job is to tell Codex:

  • which memory files to read before starting work
  • when to log new entries
  • how to classify entries by file
  • when to promote content from raw logs into ACTIVE.md
  • that AGENTS.md itself must not be edited automatically unless the user explicitly asks

Read references/agents-snippet.md before proposing or updating the AGENTS.md text.

Unless the user explicitly asks for direct edits, propose the exact AGENTS.md snippet in chat and let the user apply it manually.

4. Add an optional nightly review loop

When the user wants recurring maintenance, create a nightly automation that:

  • reviews the current memory files
  • primarily refines LEARNINGS.md, ERRORS.md, and FEATURE_REQUESTS.md
  • proposes or applies safe updates to the memory files
  • never edits AGENTS.md automatically

Read references/nightly-review.md before designing the automation.

5. Validate the loop

Before finishing, confirm the setup actually forms a loop:

  1. AGENTS.md points Codex to PROFILE.md and ACTIVE.md
  2. the five memory files exist and have sane content
  3. promotion rules are explicit
  4. if automation was requested, the automation prompt clearly explains the refinement-only role and promotion rules

Promotion Rules

Apply these promotion rules consistently:

  • Promote to ACTIVE.md only when the content is stable, cross-task useful, and likely to improve future execution or communication
  • Keep PROFILE.md limited to durable user identity, style, and preference facts
  • Keep temporary context out of PROFILE.md
  • Keep one-off noise out of all memory files
  • If a candidate item is ambiguous, keep it in a raw log or leave it as a proposal instead of promoting it

Safety Rules

  • Do not assume Codex automatically reads arbitrary memory files; route the loop through AGENTS.md
  • Do not describe OpenClaw-only mechanisms as if they exist natively in Codex
  • Do not edit AGENTS.md automatically unless the user explicitly asks
  • Prefer updating ACTIVE.md over bloating AGENTS.md
  • Prefer compact, maintainable rules over long narrative summaries

Deliverables

When using this skill, aim to produce some or all of these:

  • a memory directory with the five core files
  • a proposed AGENTS.md snippet
  • an optional nightly automation prompt
  • a short explanation of what was created, what was not changed, and how the loop works

平台分布

Codex

35.66%
按下载量换算94

Claude

30.4%
按下载量换算80

Cursor

16.93%
按下载量换算45

Gemini CLI

8.88%
按下载量换算23

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

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。目前只有一个公开来源,建议同时核对源仓库。

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