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Agent Skill · 待分类

project-setup(项目设置)

project-setup 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。

project-setup 是待分类类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为权限未标注;许可证信息还不明确,暂无第三方扫描结果,按安装量在待分类类的 11,209 个 Skill 里排第 6,822。

总安装 419周安装 18GitHub Stars 公开资料未说明下载 147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:project-setup(项目设置)
来源仓库:https://github.com/fearovex/claude-config
仓库路径:skills/project-setup
安装命令:
npx skills add https://github.com/fearovex/claude-config --skill project-setup
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/fearovex/claude-config --skill project-setup

这个 Skill 是做什么的

这个 Skill 的分类还没最终确定,用法以来源仓库的说明为准。

project-setup 初始化项目上下文并加载持久化记忆层,适配不同技术栈约定。适用于新项目启动时的环境检测和基础配置搭建。若 Engram 可用则自动保存项目快照,否则仅做本地记录。建议首次使用时确认项目结构和权限设置,防止后续协作冲突。

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。

来源仓库内路径:skills/project-setup

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/fearovex/claude-config --skill project-setup;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

    权限没有明确标注,第一次使用时只给一个无关紧要的任务,观察它到底读了哪些文件和网络。

常见问题

6 条

project-setup 怎么安装?

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

project-setup 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。

project-setup 需要什么权限?

权限级别没有明确标注。第一次使用时建议只给无关紧要的任务,观察它实际读写了哪些内容。

project-setup 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。

project-setup 用的人多吗?

公开数据是累计安装 419 次,近一周安装 18 次,下载 147 次。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。

project-setup 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

  • 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。
  • 需要处理待分类类任务、又不想每次都手写提示词的人。

不太适合

  • 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。
  • 需要厂商级技术支持和 SLA 的生产系统。

待分类类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
infsh-cli

infsh CLI · Codex、Claude

4,374,576382权限未标注未明确未收录
ai-video-generation

AI 视频生成 · Codex、Claude

4,313,376382调用外部服务未明确有提醒
lark-openapi-explorer

云雀 OpenAPI 浏览器 · Codex、Claude

2,064,1209,011需要联网未明确有提醒
lark-workflow-standup-report

Lark 工作流程站立报告 · Codex、Claude

2,041,6328,978需要联网未明确通过
use-my-browser

使用我的浏览器 · Codex、Claude

1,848,49140操作浏览器未明确未通过
project-setup

项目设置 · 本页

419—权限未标注未明确未收录

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

project-setup

Deploys the complete SDD architecture with engram persistence and ai-context/ memory layer in the current project.

Triggers: /project-setup, initialize project, setup sdd, configure claude project, new sdd project


What this skill does

When the user runs /project-setup, I analyze the current project and generate:

  1. CLAUDE.md at the project root with real detected context
  2. ai-context/ with the 5 memory files initialized
  3. Engram project context for the SDD cycle
  4. Registry of relevant skills based on the detected stack

Process

Step 1 — Project detection

I read and analyze:

  • package.json / pyproject.toml / go.mod / Cargo.toml / pom.xml
  • Folder structure (src/, app/, lib/, tests/, etc.)
  • Configuration files (tsconfig, eslint, prettier, etc.)
  • README.md if it exists
  • Existing docs folders
  • .git/ to confirm it is a repository

I infer:

  • Main language and version
  • Framework(s) in use
  • Database / ORM
  • Testing tools
  • Build / bundler tools
  • Detected naming conventions (camelCase, snake_case, etc.)
  • Folder structure (feature-based, layer-based, monorepo, etc.)

Step 2 — Generate project CLAUDE.md

I create CLAUDE.md at the root with these sections:

# [Project Name]

## Stack

[Detected stack with versions]

## Architecture

[Explained folder structure]
[Detected architectural pattern]

## Conventions

[Detected naming conventions]
[Observed code patterns]

## Important Commands

[Scripts from package.json / Makefile / etc.]

## Project Memory

At the start of each session, read the relevant files in ai-context/:

- ai-context/stack.md — Detailed technical stack
- ai-context/architecture.md — Architecture decisions
- ai-context/conventions.md — Team conventions
- ai-context/known-issues.md — Known bugs and gotchas
- ai-context/changelog-ai.md — AI change history

After completing significant work: update the relevant files or
run /memory-update so the AI updates them.

## Active Skills

[List of relevant skills for this project]

## SDD — Spec-Driven Development

This project uses SDD.
Entry points: `/sdd-explore <topic>` (investigate first) or `/sdd-propose <change-name>` (start from proposal).
Multi-phase flows are handled by the orchestrator as meta-commands — type them directly in conversation.
Phase skills: `/sdd-explore`, `/sdd-propose`, `/sdd-spec`, `/sdd-design`, `/sdd-tasks`, `/sdd-apply`, `/sdd-verify`, `/sdd-archive`.

Step 3 — Initialize ai-context/

I create the 5 files with real content based on what was detected:

ai-context/stack.md
# Technical Stack

Last updated: [date]

## Language

- [Language]: [version]

## Main Framework

- [Framework]: [version]
- [Relevant configuration details]

## Database / ORM

- [If applicable]

## Testing

- [Testing framework]
- [Commands to run tests]

## Build / Bundler

- [Tool]: [version]
- [Build command]
- [Dev command]

## Key Dependencies

| Package | Version   | Purpose        |
| ------- | --------- | -------------- |
| [name]  | [version] | [what it does] |
ai-context/architecture.md
# Project Architecture

Last updated: [date]

## Architectural Pattern

[Detected: feature-based / layer-based / clean architecture / etc.]

## Folder Structure

[Explained tree with the purpose of each folder]

## Architecture Decisions

| Decision | Choice | Alternatives | Reason |
| -------- | ------ | ------------ | ------ |

[Inferred from existing code]

## Data Flow

[Description of the main flow]

## Entry Points

[Main entry points of the system]
ai-context/conventions.md
# Project Conventions

Last updated: [date]

## Naming

- Files: [detected]
- Variables/Functions: [detected]
- Classes/Types: [detected]
- Constants: [detected]

## File Structure

[How files of each type are organized]

## Code Patterns

[Patterns detected in existing code]

## Git

[Commit conventions if detected]
[Branch strategy if detected]

## Testing

[Where tests live]
[Test naming conventions]
ai-context/known-issues.md
# Known Issues

Last updated: [date]

## Active Bugs

[Empty at start — filled during development]

## Gotchas and Limitations

[Anything unusual detected in existing code]

## Identified Technical Debt

[Problematic patterns detected]

## Workarounds in Use

[If there are workarounds in the code, document them here]
ai-context/changelog-ai.md
# AI Changelog

This file records significant changes made by Claude.

## Entry Format

### [YYYY-MM-DD] — [Change name]

**What was done**: [description]
**Modified files**: [list]
**Decisions made**: [relevant decisions]
**Notes**: [anything important]

---

[Entries are added here chronologically]

Step 4 — Persist project context to engram

Check if Engram MCP is reachable (call mem_context):

  • If reachable: save project context to engram via mem_save with topic_key: sdd-init/{project-name}. Log INFO: Engram detected — project context persisted to engram.
  • If not reachable: log WARNING: Engram not available — project context not persisted. SDD artifacts will be ephemeral.

Step 5 — Final report

I present to the user:

✅ Project configured: [name]

Detected stack:
  - [language + version]
  - [framework + version]
  - [testing framework]

Files created:
  - CLAUDE.md
  - ai-context/stack.md
  - ai-context/architecture.md
  - ai-context/conventions.md
  - ai-context/known-issues.md
  - ai-context/changelog-ai.md

Next steps:
  1. Review and adjust CLAUDE.md with details I could not detect
  2. To start a change: /sdd-explore <topic> or /sdd-propose <change-name>
  3. To create project-specific skills: /skill-create <name>

Rules

  • NEVER overwrite existing files without warning and asking for confirmation
  • If CLAUDE.md already exists, I offer an intelligent merge or creating a backup
  • If ai-context/ already exists, I offer to update only what is missing
  • I always read real code — I never invent the stack
  • If I cannot determine something with certainty, I mark it as [To confirm]
  • NEVER create a .claude/commands/ directory — commands/ is a legacy mechanism; .claude/skills/ is the only supported extensibility path for new projects.

平台分布

Codex

35.93%
按下载量换算53

Claude

30.74%
按下载量换算45

Cursor

18.83%
按下载量换算28

Gemini CLI

10.05%
按下载量换算15

安全审计

暂无安全审计结果可展示。

权限和风险

权限未标注

当前来源未能明确判断权限范围,默认进入异常复核队列。

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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