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codify-learning编纂学习

Agent Skill

codify-learning 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

563

周安装

23

GitHub Stars

8

下载量

182
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:codify-learning(编纂学习)
来源仓库:https://github.com/phrazzld/claude-config
仓库路径:skills/codify-learning
安装命令:
npx skills add https://github.com/phrazzld/claude-config --skill codify-learning
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/phrazzld/claude-config --skill codify-learning

简介

codify-learning 沉淀任务执行中的错误修正与经验缺口。

  • 将每次纠正、反馈或调试洞察转化为系统级改进点。
  • 默认 codify 所有学习成果,不设 occurrence 阈值限制。
  • 输出 markdown 格式知识库,支持跨会话检索与应用。
  • 适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

SKILL.md

/codify-learning

Transform ephemeral learnings into durable system improvements.

Philosophy

Default codify, justify exceptions. Every correction, feedback, or "I should have known" moment represents a gap in the system. Codification closes that gap.

The "3+ occurrences" threshold is a myth - we have no cross-session memory. If you learned something, codify it.

Process

1. Identify Learnings

Scan the session for:

  • Errors encountered and how they were fixed
  • PR feedback received
  • Debugging insights ("the real problem was...")
  • Workflow improvements discovered
  • Patterns that should be enforced

2. Brainstorm Codification Targets

For each learning, consider:

  • Hook - Should this be guaranteed/blocked? (most deterministic)
  • Lint rule - Can a lint rule catch this at edit time? → invoke /guardrail
  • Agent - Should a reviewer catch this pattern?
  • Skill - Is this a reusable workflow?
  • CLAUDE.md - Is this philosophy/convention?

Choose the target that provides the most leverage. Hooks > Lint rules > Agents > Skills > CLAUDE.md for enforcement. Skills > CLAUDE.md for workflows.

Lint rules are ideal for: import boundaries, naming conventions, deprecated API usage, auth enforcement, architectural layering violations. If the pattern can be expressed as "this code shape should never/always appear," it's a lint rule.

3. Implement

For each codification:

  1. Read the target file
  2. Add the learning in appropriate format
  3. Wire up if needed (hooks need settings.json entry)
  4. Verify no duplication

4. Report

CODIFIED:
- [learning] → [file]: [summary of change]

NOT CODIFIED:
- [learning]: [justification - must be specific]

Anti-Patterns

❌ "No patterns detected" - One occurrence is enough ❌ "First time seeing this" - No cross-session memory exists ❌ "Seems too minor" - Minor issues compound into major friction ❌ "Not sure where to put it" - Brainstorm, ask, don't skip ❌ "Already obvious" - If it wasn't codified, the system didn't know it

See CLAUDE.md "Continuous Learning Philosophy" for valid exceptions and the full codification philosophy.

See Also

/done — Full session retrospective (subsumes codification as one step in a broader process: went-well, friction, bugs, codify, report).

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

34.51%
按下载量换算63

Claude

33.81%
按下载量换算62

Cursor

18.41%
按下载量换算34

Gemini CLI

10.29%
按下载量换算19

安全审计

Gen Agent Trust Hub

可疑

Socket

通过

Snyk

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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