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- Project-specific skill template covering architecture, patterns, testing, and deployment guidance.
affaan-m/ECC
Project-specific skill template covering architecture, patterns, testing, and deployment guidance.
npx skills add https://github.com/affaan-m/ECC --skill "docs/zh-TW/skills/project-guidelines-example"Source checked Jul 28, 2026·Refresh due Oct 26, 2026
Reorganized from the pinned upstream SKILL.md
According to the pinned SKILL.md from affaan-m/ECC: 這是專案特定技能的範例。使用此作為你自己專案的範本。
npx skills add https://github.com/affaan-m/ECC --skill "docs/zh-TW/skills/project-guidelines-example"Best fit
Bring this context
Expected outputs
Key source sections
Sections are extracted automatically from the pinned SKILL.md and link back to the source.
在處理專案特定設計時參考此技能。專案技能包含: - 架構概覽 - 檔案結構 - 程式碼模式 - 測試要求 - 部署工作流程
技術堆疊: - 前端:Next.js 15(App Router)、TypeScript、React - 後端:FastAPI(Python)、Pydantic 模型 - 資料庫:Supabase(PostgreSQL) - AI:Claude API 帶工具呼叫和結構化輸出 - 部署:Google Cloud Run - 測試:Playwright(E2E)、pytest(後端)、React Testing Library
Review the “檔案結構” section in the pinned source before continuing.
Review the “程式碼模式” section in the pinned source before continuing.
Review the “API 回應格式(FastAPI)” section in the pinned source before continuing.
SkillSignal prompt templates
These prompts were written by SkillSignal from the source structure; they are not upstream text.
Task-start prompt
Confirm source fit, inputs, and outputs before acting.
Use project-guidelines-example to help me with: [specific task]. Context: [files, data, or background]. Constraints: [environment, scope, and prohibited actions]. Before acting, check the pinned SKILL.md and explain which sections apply, what inputs are still missing, and what you will deliver.
Source-guided execution
Make the Agent explicitly follow the key extracted sections.
Apply the pinned project-guidelines-example source to [task]. Pay particular attention to these source sections: “何時使用”, “架構概覽”, “檔案結構”, “程式碼模式”, “API 回應格式(FastAPI)”. Preserve the important decision at each step. Mark facts not covered by the source as “needs confirmation” instead of inventing them. Then verify the result against my acceptance criteria: [criteria].
Result-review prompt
Check omissions, permissions, and source drift before delivery.
Review the current project-guidelines-example result: (1) does it satisfy the original task; (2) were any applicable steps or limits in the pinned SKILL.md missed; (3) did it perform any unauthorized file, command, network, or data action; and (4) which conclusions remain unverified? List issues first, then fix only what the source or user authorization supports.
Output checklist
The task matches the purpose documented in the SKILL.md.
The source section “何時使用” has been checked.
The source section “架構概覽” has been checked.
The source section “檔案結構” has been checked.
The source section “程式碼模式” has been checked.
Inputs, constraints, and acceptance criteria are explicit.
Unverified facts, compatibility, and outcome claims are clearly marked.
Any file, command, network, or data action has been reviewed.
Choose a different workflow
Project-specific skill template covering architecture, patterns, testing, and deployment guidance.
A separate implementation from affaan-m/ECC; compare its source, maintenance signals, and permission requirements.
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A separate implementation from K-Dense-AI/scientific-agent-skills; compare its source, maintenance signals, and permission requirements.
Open source detailBuild, scaffold, and deploy Power Automate cloud flows using the FlowStudio MCP server. Your agent constructs flow definitions, wires connections, deploys, and tests — all via MCP without opening the portal. Load this skill when asked to: create a flow, build a new flow, deploy a flow definition, scaffold a Power Automate workflow, construct a flow JSON, update an existing flow's actions, patch a flow definition, add actions to a flow, wire up connections, or generate a workflow definition from
A separate implementation from github/awesome-copilot; compare its source, maintenance signals, and permission requirements.
Open source detailFAQ
這是專案特定技能的範例。使用此作為你自己專案的範本。
The catalog detected this source-specific install command: npx skills add https://github.com/affaan-m/ECC --skill "docs/zh-TW/skills/project-guidelines-example". Inspect the command and pinned source before running it.
No dedicated Agent platform is declared in the pinned source record.
Quality breakdown
Based on traceable docs and repository signals; stars are not treated as quality.
Compare before choosing
These links are selected from shared tasks, functions, stacks, platforms, and same-name variants. Compare the source owner, documentation, permissions, and maintenance signals.
Project-specific skill template covering architecture, patterns, testing, and deployment guidance.
Build, inspect, test, and analyze bounded process-based discrete-event simulations with SimPy, including events, resources, interrupts, monitoring, replications, warm-up, and reproducible output analysis.
Build, scaffold, and deploy Power Automate cloud flows using the FlowStudio MCP server. Your agent constructs flow definitions, wires connections, deploys, and tests — all via MCP without opening the portal. Load this skill when asked to: create a flow, build a new flow, deploy a flow definition, scaffold a Power Automate workflow, construct a flow JSON, update an existing flow's actions, patch a flow definition, add actions to a flow, wire up connections, or generate a workflow definition from
End-to-end skill for building, testing, linting, versioning, and publishing a production-grade Python library to PyPI. Covers all four build backends (setuptools+setuptools_scm, hatchling, flit, poetry), PEP 440 versioning, semantic versioning, dynamic git-tag versioning, OOP/SOLID design, type hints (PEP 484/526/544/561), Trusted Publishing (OIDC), and the full PyPA packaging flow. Use for: creating Python packages, pip-installable SDKs, CLI tools, framework plugins, pyproject.toml setup, py.ty
Verification loop for Django projects: migrations, linting, tests with coverage, security scans, and deployment readiness checks before release or PR.
這是專案特定技能的範例。使用此作為你自己專案的範本。
基於真實生產應用程式:Zenith - AI 驅動的客戶探索平台。
在處理專案特定設計時參考此技能。專案技能包含:
技術堆疊:
服務:
┌─────────────────────────────────────────────────────────────┐
│ 前端 │
│ Next.js 15 + TypeScript + TailwindCSS │
│ 部署:Vercel / Cloud Run │
└─────────────────────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────┐
│ 後端 │
│ FastAPI + Python 3.11 + Pydantic │
│ 部署:Cloud Run │
└─────────────────────────────────────────────────────────────┘
│
┌───────────────┼───────────────┐
▼ ▼ ▼
┌──────────┐ ┌──────────┐ ┌──────────┐
│ Supabase │ │ Claude │ │ Redis │
│ Database │ │ API │ │ Cache │
└──────────┘ └──────────┘ └──────────┘
project/
├── frontend/
│ └── src/
│ ├── app/ # Next.js app router 頁面
│ │ ├── api/ # API 路由
│ │ ├── (auth)/ # 需認證路由
│ │ └── workspace/ # 主應用程式工作區
│ ├── components/ # React 元件
│ │ ├── ui/ # 基礎 UI 元件
│ │ ├── forms/ # 表單元件
│ │ └── layouts/ # 版面配置元件
│ ├── hooks/ # 自訂 React hooks
│ ├── lib/ # 工具
│ ├── types/ # TypeScript 定義
│ └── config/ # 設定
│
├── backend/
│ ├── routers/ # FastAPI 路由處理器
│ ├── models.py # Pydantic 模型
│ ├── main.py # FastAPI app 進入點
│ ├── auth_system.py # 認證
│ ├── database.py # 資料庫操作
│ ├── services/ # 業務邏輯
│ └── tests/ # pytest 測試
│
├── deploy/ # 部署設定
├── docs/ # 文件
└── scripts/ # 工具腳本
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"}
)
# 提取工具使用結果
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 }
}
# 執行所有測試
poetry run pytest tests/
# 執行帶覆蓋率的測試
poetry run pytest tests/ --cov=. --cov-report=html
# 執行特定測試檔案
poetry run pytest tests/test_auth.py -v
測試結構:
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"
# 執行測試
npm run test
# 執行帶覆蓋率的測試
npm run test -- --coverage
# 執行 E2E 測試
npm run test:e2e
測試結構:
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 通過(後端)# 建置和部署前端
cd frontend && npm run build
gcloud run deploy frontend --source .
# 建置和部署後端
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...
# 後端(.env)
DATABASE_URL=postgresql://...
ANTHROPIC_API_KEY=sk-ant-...
SUPABASE_URL=https://xxx.supabase.co
SUPABASE_KEY=eyJ...
coding-standards.md - 一般程式碼最佳實務backend-patterns.md - API 和資料庫模式frontend-patterns.md - React 和 Next.js 模式tdd-workflow/ - 測試驅動開發方法論