Token导航 LogoToken导航TokenDH.com
前端设计只读github未标认证来源可访问许可证需确认审计通过

self-improving-for-codexself improving FOR Codex 命令行

Agent Skill

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。

总安装

753

周安装

32

GitHub Stars

105

下载量

264
CodexClaudeCursorGemini CLI

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

unknown

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-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

简介

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。

SKILL.md

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

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

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

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

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。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

继续浏览同类 Skills