这是一个特定项目的技能(Skill)示例。请将其用作你自定义项目的模板。
From everything-claude-codenpx claudepluginhub codelably/harmony-claude-codeThis skill uses the workspace's default tool permissions.
Designs and optimizes AI agent action spaces, tool definitions, observation formats, error recovery, and context for higher task completion rates.
Enables AI agents to execute x402 payments with per-task budgets, spending controls, and non-custodial wallets via MCP tools. Use when agents pay for APIs, services, or other agents.
Compares coding agents like Claude Code and Aider on custom YAML-defined codebase tasks using git worktrees, measuring pass rate, cost, time, and consistency.
这是一个特定项目的技能(Skill)示例。请将其用作你自定义项目的模板。
基于真实的生产应用:Zenith - AI 驱动的客户发现平台。
在处理该特定项目时参考此技能。项目技能包含:
技术栈 (Tech Stack):
服务 (Services):
┌─────────────────────────────────────────────────────────────┐
│ Frontend │
│ Next.js 15 + TypeScript + TailwindCSS │
│ Deployed: Vercel / Cloud Run │
└─────────────────────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────┐
│ Backend │
│ FastAPI + Python 3.11 + Pydantic │
│ Deployed: Cloud Run │
└─────────────────────────────────────────────────────────────┘
│
┌───────────────┼───────────────┐
▼ ▼ ▼
┌──────────┐ ┌──────────┐ ┌──────────┐
│ Supabase │ │ Claude │ │ Redis │
│ Database │ │ API │ │ Cache │
└──────────┘ └──────────┘ └──────────┘
project/
├── frontend/
│ └── src/
│ ├── app/ # Next.js App Router 页面
│ │ ├── api/ # API 路由
│ │ ├── (auth)/ # 身份验证保护的路由
│ │ └── workspace/ # 主应用工作区
│ ├── components/ # React 组件
│ │ ├── ui/ # 基础 UI 组件
│ │ ├── forms/ # 表单组件
│ │ └── layouts/ # 布局组件
│ ├── hooks/ # 自定义 React 钩子 (Hooks)
│ ├── lib/ # 工具库
│ ├── types/ # TypeScript 类型定义
│ └── config/ # 配置
│
├── backend/
│ ├── routers/ # FastAPI 路由处理器
│ ├── models.py # Pydantic 模型
│ ├── main.py # FastAPI 应用入口
│ ├── auth_system.py # 身份验证系统
│ ├── database.py # 数据库操作
│ ├── services/ # 业务逻辑
│ └── tests/ # pytest 测试
│
├── deploy/ # 部署配置
├── docs/ # 文档
└── scripts/ # 工具脚本
from pydantic import BaseModel
from typing import Generic, TypeVar, Optional
T = TypeVar('T')
class ApiResponse(BaseModel, Generic[T]):
success: bool
data: Optional[T] = None
error: Optional[str] = None
@classmethod
def ok(cls, data: T) -> "ApiResponse[T]":
return cls(success=True, data=data)
@classmethod
def fail(cls, error: str) -> "ApiResponse[T]":
return cls(success=False, error=error)
interface ApiResponse<T> {
success: boolean
data?: T
error?: string
}
async function fetchApi<T>(
endpoint: string,
options?: RequestInit
): Promise<ApiResponse<T>> {
try {
const response = await fetch(`/api${endpoint}`, {
...options,
headers: {
'Content-Type': 'application/json',
...options?.headers,
},
})
if (!response.ok) {
return { success: false, error: `HTTP ${response.status}` }
}
return await response.json()
} catch (error) {
return { success: false, error: String(error) }
}
}
from anthropic import Anthropic
from pydantic import BaseModel
class AnalysisResult(BaseModel):
summary: str
key_points: list[str]
confidence: float
async def analyze_with_claude(content: str) -> AnalysisResult:
client = Anthropic()
response = client.messages.create(
model="claude-sonnet-4-5-20250514",
max_tokens=1024,
messages=[{"role": "user", "content": content}],
tools=[{
"name": "provide_analysis",
"description": "Provide structured analysis",
"input_schema": AnalysisResult.model_json_schema()
}],
tool_choice={"type": "tool", "name": "provide_analysis"}
)
# 提取工具调用结果
tool_use = next(
block for block in response.content
if block.type == "tool_use"
)
return AnalysisResult(**tool_use.input)
import { useState, useCallback } from 'react'
interface UseApiState<T> {
data: T | null
loading: boolean
error: string | null
}
export function useApi<T>(
fetchFn: () => Promise<ApiResponse<T>>
) {
const [state, setState] = useState<UseApiState<T>>({
data: null,
loading: false,
error: null,
})
const execute = useCallback(async () => {
setState(prev => ({ ...prev, loading: true, error: null }))
const result = await fetchFn()
if (result.success) {
setState({ data: result.data!, loading: false, error: null })
} else {
setState({ data: null, loading: false, error: result.error! })
}
}, [fetchFn])
return { ...state, execute }
}
# 运行所有测试
poetry run pytest tests/
# 运行并生成覆盖率报告
poetry run pytest tests/ --cov=. --cov-report=html
# 运行特定测试文件
poetry run pytest tests/test_auth.py -v
测试结构:
import pytest
from httpx import AsyncClient
from main import app
@pytest.fixture
async def client():
async with AsyncClient(app=app, base_url="http://test") as ac:
yield ac
@pytest.mark.asyncio
async def test_health_check(client: AsyncClient):
response = await client.get("/health")
assert response.status_code == 200
assert response.json()["status"] == "healthy"
# 运行测试
npm run test
# 运行并生成覆盖率报告
npm run test -- --coverage
# 运行 E2E 测试
npm run test:e2e
测试结构:
import { render, screen, fireEvent } from '@testing-library/react'
import { WorkspacePanel } from './WorkspacePanel'
describe('WorkspacePanel', () => {
it('renders workspace correctly', () => {
render(<WorkspacePanel />)
expect(screen.getByRole('main')).toBeInTheDocument()
})
it('handles session creation', async () => {
render(<WorkspacePanel />)
fireEvent.click(screen.getByText('New Session'))
expect(await screen.findByText('Session created')).toBeInTheDocument()
})
})
npm run build 成功 (前端)poetry run pytest 通过 (后端)# 构建并部署前端
cd frontend && npm run build
gcloud run deploy frontend --source .
# 构建并部署后端
cd backend
gcloud run deploy backend --source .
# 前端 (.env.local)
NEXT_PUBLIC_API_URL=https://api.example.com
NEXT_PUBLIC_SUPABASE_URL=https://xxx.supabase.co
NEXT_PUBLIC_SUPABASE_ANON_KEY=eyJ...
# 后端 (.env)
DATABASE_URL=postgresql://...
ANTHROPIC_API_KEY=sk-ant-...
SUPABASE_URL=https://xxx.supabase.co
SUPABASE_KEY=eyJ...
coding-standards.md - 通用代码最佳实践backend-patterns.md - API 与数据库模式frontend-patterns.md - React 与 Next.js 模式tdd-workflow/ - 测试驱动开发 (TDD) 方法论