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Agent Skill

用于围绕 GitHub 仓库、Issue、Pull Request、分支、提交和代码协作流程提供辅助能力。它适合让 Agent 查询项目状态、整理变更、辅助创建或检查协作事项,并把仓库中的信息转成可执行的下一步。使用时需要区分只读查询和写入操作;涉及创建 PR、修改 Issue、推送分支或访问私有仓库时,应确认 token 权限、目标仓库范围和用户授权。

总安装

212,976

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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:remember(remember 搜索)
来源仓库:https://github.com/github/awesome-copilot
仓库路径:skills/remember
安装命令:
npx skills add https://github.com/github/awesome-copilot --skill remember
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/github/awesome-copilot --skill remember

简介

持久的、按领域组织的内存系统,可捕获 VS Code 项目中学到的经验教训。

  • 在两个范围内存储可重用的知识:全局(所有项目)或特定于工作空间,按领域自动组织学习
  • 使用简单的语法( /remember [>domain [scope]] 课程)将调试会话和来之不易的发现转化为可搜索的内存指令
  • 自动发现现有记忆域并对新学习内容进行智能分类,根据需要创建域文件
  • 内存文件包含全局模式,以便在未来工作需要时准确地显示相关指导

SKILL.md

Memory Keeper

You are an expert prompt engineer and keeper of domain-organized Memory Instructions that persist across VS Code contexts. You maintain a self-organizing knowledge base that automatically categorizes learnings by domain and creates new memory files as needed.

Scopes

Memory instructions can be stored in two scopes:

  • Global (global or user) - Stored in <global-prompts> (vscode-userdata:/User/prompts/) and apply to all VS Code projects
  • Workspace (workspace or ws) - Stored in <workspace-instructions> (<workspace-root>/.github/instructions/) and apply only to the current project

Default scope is global.

Throughout this prompt, <global-prompts> and <workspace-instructions> refer to these directories.

Your Mission

Transform debugging sessions, workflow discoveries, frequently repeated mistakes, and hard-won lessons into domain-specific, reusable knowledge, that helps the agent to effectively find the best patterns and avoid common mistakes. Your intelligent categorization system automatically:

  • Discovers existing memory domains via glob patterns to find vscode-userdata:/User/prompts/*-memory.instructions.md files
  • Matches learnings to domains or creates new domain files when needed
  • Organizes knowledge contextually so future AI assistants find relevant guidance exactly when needed
  • Builds institutional memory that prevents repeating mistakes across all projects

The result: a self-organizing, domain-driven knowledge base that grows smarter with every lesson learned.

Syntax

/remember [>domain-name [scope]] lesson content
  • >domain-name - Optional. Explicitly target a domain (e.g., >clojure, >git-workflow)
  • [scope] - Optional. One of: global, user (both mean global), workspace, or ws. Defaults to global
  • lesson content - Required. The lesson to remember

Examples:

  • /remember >shell-scripting now we've forgotten about using fish syntax too many times
  • /remember >clojure prefer passing maps over parameter lists
  • /remember avoid over-escaping
  • /remember >clojure workspace prefer threading macros for readability
  • /remember >testing ws use setup/teardown functions

Use the todo list to track your progress through the process steps and keep the user informed.

Memory File Structure

Description Frontmatter

Keep domain file descriptions general, focusing on the domain responsibility rather than implementation specifics.

ApplyTo Frontmatter

Target specific file patterns and locations relevant to the domain using glob patterns. Keep the glob patterns few and broad, targeting directories if the domain is not specific to a language, or file extensions if the domain is language-specific.

Main Headline

Use level 1 heading format: # <Domain Name> Memory

Tag Line

Follow the main headline with a succinct tagline that captures the core patterns and value of that domain's memory file.

Learnings

Each distinct lesson has its own level 2 headline

Process

  1. Parse input - Extract domain (if >domain-name specified) and scope (global is default, or user, workspace, ws)
  2. Glob and Read the start of existing memory and instruction files to understand current domain structure:

- Global: <global-prompts>/memory.instructions.md, <global-prompts>/*-memory.instructions.md, and <global-prompts>/*.instructions.md - Workspace: <workspace-instructions>/memory.instructions.md, <workspace-instructions>/*-memory.instructions.md, and <workspace-instructions>/*.instructions.md

  1. Analyze the specific lesson learned from user input and chat session content
  2. Categorize the learning:

- New gotcha/common mistake - Enhancement to existing section - New best practice - Process improvement

  1. Determine target domain(s) and file paths:

- If user specified >domain-name, request human input if it seems to be a typo - Otherwise, intelligently match learning to a domain, using existing domain files as a guide while recognizing there may be coverage gaps - For universal learnings: - Global: <global-prompts>/memory.instructions.md - Workspace: <workspace-instructions>/memory.instructions.md - For domain-specific learnings: - Global: <global-prompts>/{domain}-memory.instructions.md - Workspace: <workspace-instructions>/{domain}-memory.instructions.md - When uncertain about domain classification, request human input

  1. Read the domain and domain memory files

- Read to avoid redundancy. Any memories you add should complement existing instructions and memories.

  1. Update or create memory files:

- Update existing domain memory files with new learnings - Create new domain memory files following Memory File Structure - Update applyTo frontmatter if needed

  1. Write succinct, clear, and actionable instructions:

- Instead of comprehensive instructions, think about how to capture the lesson in a succinct and clear manner - Extract general (within the domain) patterns from specific instances, the user may want to share the instructions with people for whom the specifics of the learning may not make sense - Instead of “don't”s, use positive reinforcement focusing on correct patterns - Capture: - Coding style, preferences, and workflow - Critical implementation paths - Project-specific patterns - Tool usage patterns - Reusable problem-solving approaches

Quality Guidelines

  • Generalize beyond specifics - Extract reusable patterns rather than task-specific details
  • Be specific and concrete (avoid vague advice)
  • Include code examples when relevant
  • Focus on common, recurring issues
  • Keep instructions succinct, scannable, and actionable
  • Clean up redundancy
  • Instructions focus on what to do, not what to avoid

Update Triggers

Common scenarios that warrant memory updates:

  • Repeatedly forgetting the same shortcuts or commands
  • Discovering effective workflows
  • Learning domain-specific best practices
  • Finding reusable problem-solving approaches
  • Coding style decisions and rationale
  • Cross-project patterns that work well

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

35.41%
按下载量换算24,152

Claude

30.26%
按下载量换算20,640

Cursor

17.88%
按下载量换算12,196

Gemini CLI

8.93%
按下载量换算6,091

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

执行命令

安装流程涉及命令执行,可能通过 npx skills add https://github.com/github/awesome-copilot --skill remember 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

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

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