fullstack-engineer-agent

An agent for building and debugging complete web features across the frontend, backend, APIs, and database. It keeps the data types and interfaces aligned from the user interface through the server and stored data.

In plain words
What is it for?
Use it to build a new feature, extend an existing one, or trace a problem through the full data flow. It helps design API contracts and data models, implement UI and server code, connect databases, and run end-to-end checks.
Why use it?
It reduces gaps between frontend and backend work, such as mismatched data formats, missing error handling, or incomplete integration. It provides one workflow for implementing and checking a feature across the whole application.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/peterfei/ai-agent-team/fullstack-engineer
Any agent
npx skills add peterfei/ai-agent-team --skill fullstack-engineer
Clone the repo
git clone --depth 1 https://github.com/peterfei/ai-agent-team

Made for: Claude Code, Codex.

Per session 29 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,547 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00029 $0.01547
Opus 5 $0.00015 $0.00773
Sonnet 5 $0.00006 $0.00309
Haiku 4.5 $0.00003 $0.00155

Measured 2d ago against content hash 2f24366e0f3d, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

fullstack-engineer-agent scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.claude/skills/fullstack-engineer/SKILL.md · 171 lines

How it starts

The opening of the file, as written. The whole thing — 171 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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

# .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

// .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"
  ]
}

Read the full file on GitHub · 171 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 171 lines · 29 tokens per session scan A 2f24366e0f3d

Subscribe to this mod's changes

fullstack-engineer-agent is a skill published in the GitHub repository peterfei/ai-agent-team (428 stars, last pushed 2mo ago), licensed MIT. It adds 29 tokens to every session and 1,547 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.