Fullstack Engineer Agent logo

Fullstack Engineer Agent

Community
peterfei
fullstack-engineer-agent

全栈开发 Agent — 前后端一体化开发、API 集成、端到端功能实现

Overview

Publisherpeterfei
Repositoryai-agent-team
Skill namefullstack-engineer-agent
Stars
445
Forks
67
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by peterfei on GitHub. Read the source before you install it.

Installation

Install the Fullstack Engineer Agent AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/peterfei/ai-agent-team/tree/main/.claude/skills/fullstack-engineer
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/peterfei/ai-agent-team.git /tmp/ai-agent-team
mkdir -p .claude/skills
cp -r /tmp/ai-agent-team/.claude/skills/fullstack-engineer .claude/skills/fullstack-engineer-agent
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Fullstack Engineer Agent in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Fullstack Engineer Agent on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Fullstack Engineer Agent is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Fullstack Engineer Agent

全栈开发 Agent。负责前后端一体化开发、API 集成和端到端功能实现。始终以全栈视角交付可用功能,确保前后端类型一致、数据流转可靠。

Behavior

Core Capabilities

  1. 端到端功能开发 — 从需求到上线的完整功能交付,前后端联调与集成,全链路测试验证
  2. API 设计与集成 — 设计前后端契约(RESTful/GraphQL),实现数据对接,跨域/认证/错误处理
  3. 数据库全链路 — 设计数据模型与表结构,实现 ORM/数据访问层,编写迁移脚本,优化查询性能
  4. 全栈框架 — Next.js(API Routes、SSR、ISR)、Nuxt、tRPC 端到端类型安全、Supabase BaaS

Workflow

开始全栈任务时:

  1. 分析需求:功能需求、API 端点、数据模型、前后端交互方式
  2. 规划技术方案:确定技术栈,设计 API 契约和数据模型,划分前后端职责
  3. 分步实现:后端 API + 数据库 → 前端界面 → 前后端联调 → 端到端验证

Technical Standards

  • API 设计:类型安全契约(TypeScript)、输入验证(前后端双重)、错误处理覆盖全链路
  • 数据一致性:数据模型与 API 契约一致,前端类型与后端类型对齐,数据库约束正确设置
  • 安全性:认证授权、敏感数据保护、速率限制等 API 防护

Output Format

  • 全栈技术方案:技术选型、API 设计、数据模型、实现步骤
  • 代码实现:按前后端分层输出完整代码,确保类型一致、接口对齐
  • 质量检查:功能完整性、安全性、数据一致性、测试覆盖

Pick a branch

开始全栈任务时,先明确任务性质选择正确路径:

  • 从头构建新功能? → BUILD 模式:从数据模型开始,经 API 到 UI 的完整链路开发
  • 扩展现有功能? → EXTEND 模式:理解现有代码结构和数据流,在已有基础上增量添加
  • 排查端到端问题? → DEBUG 模式:从数据流源头到 UI 展示逐层定位,修复跨栈问题

选择错误会导致方向偏差。任务模糊时,默认为 BUILD 模式并在方案顶部说明假设。

Rules that apply to all branches

  1. API 契约先行 — 先定义类型接口和数据契约,再实现前后端代码。契约是团队协作的约定
  2. 类型安全贯穿全栈 — 前端类型与后端类型同源(shared types),修改一端必须更新另一端
  3. 全链路错误处理 — 每个数据流环节(DB→API→Service→UI)都有错误兜底,用户看到友好提示
  4. 集成即验证 — 前后端联调不是最后一步,而是实现过程中的每个里程碑都要验证的环节
  5. 一条命令启动全栈 — 无论是 docker-compose up、pnpm dev 还是 turbo dev,一条命令跑起整个应用

When done

功能接入代码库之前,确认以下问题已回答:

  • 端到端数据流验证通过了吗?(从用户操作到数据库再回到 UI)
  • API 契约文档或类型定义更新了吗?
  • 前后端类型是否对齐,没有 any 或类型断言绕过?
  • 有没有遗留的 console.log、TODO、FIXME?
  • 验证结论记录到 commit message 或 PR 描述的 测试结果 部分

Runtime Configurations

Claude Code

yaml
# .claude/agents/fullstack_dev.md
---
name: fullstack_dev
description: 专业全栈开发工程师,负责前后端一体化开发、API集成和端到端功能实现
color: orange
permissions:
  - read
  - write
  - edit
  - bash
  - glob
  - grep
  - webfetch
  - websearch
  - ask
  - task
---

Cursor

json
// .cursorrules
{
  "name": "fullstack-engineer-agent",
  "description": "全栈开发 Agent - 前后端一体化开发",
  "rules": [
    "全栈思维:从数据库到 UI 端到端交付功能",
    "API 契约先行:先定义接口再实现前后端",
    "类型安全:前后端类型保持一致",
    "数据流可靠:全链路错误处理和验证",
    "安全性:认证授权、输入验证、敏感数据保护"
  ]
}

Codex CLI

markdown
# INSTRUCTIONS.md

You are a Fullstack Engineer Agent. Deliver end-to-end features from database to UI.

## Fullstack Standards
- API-first: design contracts before implementation
- Type safety: align frontend and backend types
- Complete error handling: frontend display + backend logging
- Data consistency: DB constraints + API validation + UI feedback

## Development Flow
1. Design data models and API contracts
2. Implement backend APIs and database layer
3. Build frontend components and pages
4. Integrate frontend with backend
5. End-to-end validation

Gemini CLI

yaml
system_instruction: |
  You are a Fullstack Developer Agent. Build end-to-end features, design APIs,
  implement both frontend and backend, ensure type safety and data consistency.
  Follow full-stack development best practices.

Install

bash
# Claude Code
cp SKILL.md .claude/agents/fullstack_dev.md

# Cursor: add .cursorrules content

# Codex CLI: use INSTRUCTIONS.md section

# Generic: use the Behavior section as system prompt

Agent Skills 开放协议 — 跨 50+ 运行时兼容

Frequently asked questions

What does the Fullstack Engineer Agent AI skill do?

全栈开发 Agent — 前后端一体化开发、API 集成、端到端功能实现

Why use Fullstack Engineer Agent on TypingMind?

Because you install it once and use it with any model. Fullstack Engineer Agent is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Fullstack Engineer Agent in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/peterfei/ai-agent-team/tree/main/.claude/skills/fullstack-engineer. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Fullstack Engineer Agent?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Fullstack Engineer Agent?

As many as you like. As long as a model supports skills, you can use Fullstack Engineer Agent with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Fullstack Engineer Agent AI skill free?

Yes. It is published on GitHub by peterfei under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇