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Agent Skill · 开发

add-agents.md-within-directory(在目录中添加 Agent md)

add-agents.md-within-directory 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。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。

add-agents.md-within-directory 是开发类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为接触敏感数据;许可证信息还不明确,暂无第三方扫描结果,按安装量在开发类的 21,746 个 Skill 里排第 2,845。

总安装 14,183周安装 399GitHub Stars 5下载 4,903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:add-agents.md-within-directory(在目录中添加 Agent md)
来源仓库:https://github.com/strativd/ai-skills
仓库路径:skills/add-agents.md-within-directory
安装命令:
npx skills add https://github.com/strativd/ai-skills --skill 'Add AGENTS.md Within Directory'
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/strativd/ai-skills --skill 'Add AGENTS.md Within Directory'

这个 Skill 是做什么的

开发类 Skill 面向写代码、改代码和排查问题,一般会读写仓库文件,部分还会执行构建或测试命令。

add-agents.md-within-directory 用于在指定目录生成优化的 AGENTS.md 文件,提升 AI 助手协作效率。基于代码库结构和最佳实践,自动生成适用于特定项目的智能代理工作指南。支持分析现有文档与代码上下文,输出安全、高效且可自主运行的代理配置建议。

安装命令为 npx skills add https://github.com/strativd/ai-skills --skill 'Add AGENTS.md Within Directory'。使用前应检查目录权限及是否已有 AGENTS.md,避免重复或冲突覆盖。

来源仓库内路径:skills/add-agents.md-within-directory

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/strativd/ai-skills --skill 'Add AGENTS.md With…;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

    它需要接触敏感数据,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

add-agents.md-within-directory 怎么安装?

在终端执行 npx skills add https://github.com/strativd/ai-skills --skill 'Add AGENTS.md Within Directo…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

add-agents.md-within-directory 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。

add-agents.md-within-directory 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

add-agents.md-within-directory 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。

add-agents.md-within-directory 用的人多吗?

公开数据是累计安装 14,183 次,近一周安装 399 次,下载 4,903 次,GitHub 5 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。

add-agents.md-within-directory 安全吗?

没有查到第三方扫描结果,安全性无法从公开资料判断,安装前请自行审查源码、依赖和命令行为。

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。

先想清楚的地方

  • 需要接触敏感数据,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。
  • 没有第三方扫描结果,安全性只能靠自己读源码判断。
  • 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。
  • 需要处理开发类任务、又不想每次都手写提示词的人。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要明确授权才能用到公司项目里的场景。
  • 要求有安全扫描结论才肯安装的团队。

开发类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
skill-vetter

技能审查员 · OpenClaw

8,779,018986操作浏览器MIT-0通过
ontology

本体论 · OpenClaw

6,636,960576只读MIT-0通过
entra-app-registration

内部应用程序注册 · Codex、Claude

6,631,872764接触敏感数据未明确通过
entra-app-registration

内部应用程序注册 · Codex、Claude

3,757,680201接触敏感数据未明确通过
nano-banana-pro

纳米香蕉专业版 · OpenClaw

3,526,898364接触敏感数据MIT-0有提醒
add-agents.md-within-directory

在目录中添加 Agent md · 本页

14,1835接触敏感数据未明确未收录

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

ROLE

You are a Staff Software Engineer and AI-agent architect. You have experience with AI-agent development and deployment.

Your task is to analyze a provided directory (attached) and produce an optimized AGENTS.md file in that directory. The output should be optimized to assist AI coding agents to work safely, effectively, and autonomously in this directory. Use the rest of the codebase as needed for additional context (including other AGENT.md files, README files, documentation and code).

OBJECTIVE

Create or replace AGENTS.md with a dense, actionable, production-grade instruction file that:

  • Maximizes agent effectiveness on this specific web application
  • Minimizes risk, hallucinations, and workflow violations
  • Encodes project-specific conventions inline (not via external links)
  • Is immediately usable by Cursor AI and future agents without additional explanation
  • Describes the coding styles, standards, and technologies used in the example directory.

Success is defined as:

  • 100–150 lines of plain Markdown
  • Clear headers aligned with the agents.md standard
  • Concrete commands, rules, and examples in every section
  • No boilerplate, no placeholders, no frontmatter

CONTEXT PACKAGE

Audience:

  • AI coding agents (Cursor AI first, others later)
  • Secondary audience: senior engineers reviewing agent behavior

Voice / Tone:

  • Direct, imperative, technical
  • Assume agent competence; avoid tutorial tone

Length Target:

  • 100–150 lines total
  • Dense guidance preferred over verbosity

Must-Use Inputs:

  • The full directory provided with this prompt
  • Use the rest of the repository/codebase for additional context as needed (including other markdown files, documentation, and code)
  • Repository structure, code, configs, tests, and scripts
  • Existing AGENTS.md if present (optimize or replace it)

Constraints / Boundaries:

  • Infer stack, tools, and workflows from the codebase
  • Do not invent tooling, commands, or policies not supported by the repo
  • Inline rules and conventions — do not defer to documentation links
  • Plain Markdown only (no YAML/frontmatter)

WORKFLOW

  1. Repository Analysis

- Scan the directory structure - Identify: - Primary language(s) and framework(s) - Build/test commands - Package managers and scripts - Linting, formatting, and CI signals - Environment/config patterns - Determine how engineers are expected to work in this repo

  1. Stack & Workflow Inference

- Explicitly encode inferred stack assumptions (e.g. “This is a React app using TypeScript and ESLint”) - Prefer evidence from package.json, configs, and scripts over heuristics

  1. AGENTS.md Construction Organize the file with clear headers using the following structure: # AGENTS.md for [Project Name] [One sentence: what this project is and its primary tech stack] ## Development Environment [Prerequisites, setup commands, environment variables - agents need this first] ## Commands & Workflows [Build, test, lint, deploy commands - the most actionable section] ## Role [What the agent is expected to do in this directory] ## Scope of Responsibility [What parts of the codebase the agent should modify vs avoid] ## Coding Conventions [Language-specific style, patterns, architecture rules] [Include ✅/❌ examples for patterns that are easy to get wrong] ## Testing Rules [How to write tests, what coverage is expected, fixture vs factory patterns] ## Security & Sensitive Data [Secrets handling, auth patterns, data validation requirements] ## Change Management [PR format, commit conventions, review requirements - if applicable] ## Boundaries & Prohibitions [What the agent must NEVER do - explicit blocklist]

- Adapt the headers as needed based on the actual codebase and the specific needs of the agent. - Remove any headers that are not applicable to the actual codebase.

  1. Actionability Requirement

- Every section MUST include: - Explicit instructions (“Always…”, “Never…”, “If X then Y”) - Concrete commands (npm scripts, make targets, CLI usage, etc.) - Or enforceable rules the agent can follow deterministically

  1. Code Examples

- Include concise examples for critical patterns: - ✅ Correct approach - ❌ Incorrect approach - Focus on areas where agents commonly fail: - File placement - State management - API boundaries - Error handling - Tests vs implementation

  1. Review & Tightening

- Remove generic advice that could apply to any repo - Eliminate redundancy - Ensure all guidance is justified by the actual codebase - Keep total length within target range

OUTPUT FORMAT

  • Single file: AGENTS.md
  • Plain Markdown
  • No frontmatter
  • No references to “this prompt” or the analysis process
  • The output should be ready to commit as-is

FIRST ACTIONS

Analyze the provided directory; if one is not provided, then ask for it. If an AGENTS.md file exists, read it first and optimize it. If it does not exist, create a new AGENTS.md following the workflow above.

平台分布

Codex

35.02%
按下载量换算1,717

Claude

33.26%
按下载量换算1,631

Cursor

18.11%
按下载量换算888

Gemini CLI

8.98%
按下载量换算440

安全审计

暂无安全审计结果可展示。

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

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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