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do-setup进行设置

Agent Skill

do-setup 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。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

318

周安装

13

GitHub Stars

7

下载量

102
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:do-setup(进行设置)
来源仓库:https://github.com/fabio-barboza/development-orchestrator
仓库路径:skills/do-setup
安装命令:
npx skills add https://github.com/fabio-barboza/development-orchestrator --skill do-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/fabio-barboza/development-orchestrator --skill do-setup

简介

do-setup 负责项目初始化与环境搭建,确保代理辅助开发流程顺畅启动。

  • 自主完成依赖安装、配置文件生成和基础脚手架创建,无需用户逐步确认。
  • 严格遵守项目约定,使用 .editorconfig、pre-commit 等标准工具链配置。
  • 若发现项目结构异常或缺失必要文件,应明确列出问题而非自行假设补全。
  • do-setup 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Project Setup

Role

You are a senior developer advocate responsible for project initialization, tooling configuration, and ensuring the agent-assisted development environment is correctly set up.

Autonomous Execution Policy

CRITICAL: NEVER pause, stop, or wait for user input during execution. Proceed through ALL steps autonomously without asking the user to "continue", "proceed", or confirm intermediate results. The ONLY acceptable reason to stop and ask the user is when there is a genuine doubt or ambiguity that cannot be resolved by reading the project files.

Execution Constraints

CRITICAL: This skill MUST NOT execute the application, run tests, start servers, compile code, or perform any runtime validation. Its sole purpose is to analyze the project structure and produce the configuration document. All analysis must be done by reading files and inspecting the directory structure — never by running the application.

Procedures

Step 0: Detect AI Tool Environment Before doing anything else, determine which AI tool is executing this skill:

  1. Check for .claude/ directory in the project root → Claude Code → config file: CLAUDE.md
  2. Check if .github/copilot-instructions.md already exists → GitHub Copilot → config file: .github/copilot-instructions.md
  3. Check if .github/ directory exists but copilot-instructions.md does not → likely GitHub Copilot → config file: .github/copilot-instructions.md
  4. If none of the above, infer from the current tool context. When in doubt, default to CLAUDE.md.

Store the resolved config file path internally and use it consistently throughout all remaining steps.

Step 1: Initialize Project Configuration

  1. If your AI tool provides a built-in project initialization command (e.g., /init in Claude Code), execute it to generate the initial project configuration file determined in Step 0.
  2. Wait for the initialization to complete before proceeding.
  3. If no built-in init command exists, locate or create the config file at the path determined in Step 0.

Step 2: Deep Project Analysis

  1. Read the project configuration file at the path determined in Step 0, and README.md if it exists.
  2. Read root config files if they exist: package.json, go.mod, pom.xml, build.gradle, build.gradle.kts, docker-compose.yml, tsconfig.json, settings.gradle, .nvmrc, Makefile, Dockerfile.
  3. Scan directory structure recursively, ignoring:

- Dependencies: node_modules/, .venv/, venv/, vendor/, .gradle/, .m2/ - Build: target/, build/, dist/, out/, .next/, __pycache__/ - Hidden: any path starting with . (except .claude/ and .github/) - Binaries/media: *.jar, *.class, *.png, *.jpg, *.pdf

  1. Read representative files from each layer (e.g., a controller, a use case, a repository) to understand adopted patterns.
  2. Build an internal summary with:

- Main stack and versions - Adopted architecture (Clean Arch, MVC, DDD, etc.) - Naming and organization patterns - System purpose - External integrations (queues, databases, APIs)

  1. Check test infrastructure:

- Look for a test script in package.json (or equivalent for the stack). - Scan for test files (*.test.*, *.spec.*, __tests__/, test/, tests/). - If neither is found, include in the project configuration file output: "⚠️ AVISO: Nenhuma infraestrutura de testes detectada. O DO Framework exige que testes passem antes de marcar tasks como concluídas. Configure um test runner antes de usar do-execute-task."

Step 3: Identify Relevant Skills

  1. List all available skills in the AI tool's skills directory (e.g., .claude/skills/ for Claude Code).
  2. **EXCLUDE all do-* skills entirely** — they are internal workflow skills and must NOT appear anywhere in the output artifact. Only evaluate technology/library skills (e.g., claude-api, find-skills).
  3. For each remaining (non-do-*) skill, read the SKILL.md header and description.
  4. Based on the Step 2 summary, evaluate if the skill is relevant to the project.
  5. A skill is relevant if it covers at least one of:

- The project's primary language or framework - The adopted architecture - An identified pattern or integration (queues, database, API, etc.)

Step 4: Update the project configuration file Merge the following sections into the project configuration file at the path determined in Step 0. Preserve all existing content and append or update only the sections below:

## Project Summary
- **Purpose:** [system description]
- **Stack:** [main technologies and versions]
- **Architecture:** [adopted pattern]
- **Integrations:** [external services]

## Available Skills
**NOTE: Only list non-`do-*` skills here. Never include workflow skills (do-setup, do-create-pbi, do-create-techspec, do-create-tasks, do-execute-task, do-execute-review, do-execute-qa, etc.) in this table.**
| Skill | Path | When to use |
|-------|------|-------------|
| [name] | [skills-dir]/[skill]/SKILL.md | [usage context] |

## Uncovered Skills
| Technology | Note |
|------------|------|
| [e.g., Java/Quarkus] | No skill available locally — add manually to the skills directory |

## Project Conventions
- **Naming:** [file and folder naming patterns]
- **Directory structure:** [relevant paths per layer]
- **Output patterns:** [where to generate files, templates used]

Step 5: Report Results & Sync Progress (Mandatory)

  1. SYNC INTERNAL PROGRESS: Once the project configuration file is updated, use the TaskUpdate tool to mark all corresponding items in your internal task tracking as completed.
  2. ARTIFACT PATH VERIFICATION: Before reporting, confirm the config file was written to the exact path resolved in Step 0. Read the file back to verify it exists and contains the expected content.
  3. Provide a summary of the setup performed.
  4. COMPLIANCE CHECK: Before responding to the user, verify:

- Is the project configuration file saved at the correct path (resolved in Step 0)? - Did you accurately identify the project stack and skills?

Output Language

Todos os artefatos gerados (seções do arquivo de configuração do projeto, resumos) devem ser escritos em Português do Brasil (PT-BR). Apenas exemplos de código, nomes de variáveis e caminhos de arquivos permanecem em inglês.

Error Handling

  • If no config files are found (no package.json, go.mod, etc.), warn the user that the project may not be initialized and ask for clarification about the stack.
  • If the project configuration file does not exist, create it from scratch.
  • If the project configuration file already exists, merge new sections without overwriting user-written content — append or update only the sections defined in Step 4.
  • If the skills directory is empty or missing, report that no skills are available and suggest the user install skills.
  • If the directory scan reveals an unrecognizable project structure, document what was found and ask the user for guidance.

References

  • Output: Project configuration file (e.g., CLAUDE.md for Claude Code, .github/copilot-instructions.md for GitHub Copilot)
  • Skills directory: AI tool's skills directory (e.g., .claude/skills/ for Claude Code)

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

32.81%
按下载量换算33

Claude

30.56%
按下载量换算31

Cursor

19.53%
按下载量换算20

Gemini CLI

8.24%
按下载量换算8

安全审计

Gen Agent Trust Hub

通过

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。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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