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Agent Skill · 研究检索

remember(remember 搜索)

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。

remember 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为执行命令;许可证信息还不明确,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 621。

总安装 212,976周安装 8,654GitHub Stars 31,731下载 68,208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: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

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

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

来源仓库内路径:skills/remember

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/github/awesome-copilot --skill remember;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

    它需要执行命令,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

remember 怎么安装?

在终端执行 npx skills add https://github.com/github/awesome-copilot --skill remember。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

remember 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。

remember 需要什么权限?

权限级别是执行命令。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。

remember 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。

remember 用的人多吗?

公开数据是累计安装 212,976 次,近一周安装 8,654 次,下载 68,208 次,GitHub 31,731 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。

remember 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。
  • 来源仓库有 31,731 星,维护活跃度相对可查。

先想清楚的地方

  • 需要执行命令,装到常用环境前要先评估影响面。
  • 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。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 不能接受 Skill 在你机器上执行命令的人。
  • 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。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
remember

remember 搜索 · 本页

212,97631,731执行命令未明确通过

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

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

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。目前只有一个公开来源,建议同时核对源仓库。

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