Refactor high-complexity React components in Dify frontend. Use when `pnpm analyze-component...
npx skills add sickn33/antigravity-awesome-skills --skill "cc-skill-project-guidelines-example"
Install specific skill from multi-skill repository
# Description
Project Guidelines Skill (Example)
# SKILL.md
name: cc-skill-project-guidelines-example
description: Project Guidelines Skill (Example)
author: affaan-m
version: "1.0"
Project Guidelines Skill (Example)
This is an example of a project-specific skill. Use this as a template for your own projects.
Based on a real production application: Zenith - AI-powered customer discovery platform.
When to Use
Reference this skill when working on the specific project it's designed for. Project skills contain:
- Architecture overview
- File structure
- Code patterns
- Testing requirements
- Deployment workflow
Architecture Overview
Tech Stack:
- Frontend: Next.js 15 (App Router), TypeScript, React
- Backend: FastAPI (Python), Pydantic models
- Database: Supabase (PostgreSQL)
- AI: Claude API with tool calling and structured output
- Deployment: Google Cloud Run
- Testing: Playwright (E2E), pytest (backend), React Testing Library
Services:
βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
β Frontend β
β Next.js 15 + TypeScript + TailwindCSS β
β Deployed: Vercel / Cloud Run β
βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
β
βΌ
βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
β Backend β
β FastAPI + Python 3.11 + Pydantic β
β Deployed: Cloud Run β
βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
β
βββββββββββββββββΌββββββββββββββββ
βΌ βΌ βΌ
ββββββββββββ ββββββββββββ ββββββββββββ
β Supabase β β Claude β β Redis β
β Database β β API β β Cache β
ββββββββββββ ββββββββββββ ββββββββββββ
File Structure
project/
βββ frontend/
β βββ src/
β βββ app/ # Next.js app router pages
β β βββ api/ # API routes
β β βββ (auth)/ # Auth-protected routes
β β βββ workspace/ # Main app workspace
β βββ components/ # React components
β β βββ ui/ # Base UI components
β β βββ forms/ # Form components
β β βββ layouts/ # Layout components
β βββ hooks/ # Custom React hooks
β βββ lib/ # Utilities
β βββ types/ # TypeScript definitions
β βββ config/ # Configuration
β
βββ backend/
β βββ routers/ # FastAPI route handlers
β βββ models.py # Pydantic models
β βββ main.py # FastAPI app entry
β βββ auth_system.py # Authentication
β βββ database.py # Database operations
β βββ services/ # Business logic
β βββ tests/ # pytest tests
β
βββ deploy/ # Deployment configs
βββ docs/ # Documentation
βββ scripts/ # Utility scripts
Code Patterns
API Response Format (FastAPI)
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)
Frontend API Calls (TypeScript)
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) }
}
}
Claude AI Integration (Structured Output)
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"}
)
# Extract tool use result
tool_use = next(
block for block in response.content
if block.type == "tool_use"
)
return AnalysisResult(**tool_use.input)
Custom Hooks (React)
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 }
}
Testing Requirements
Backend (pytest)
# Run all tests
poetry run pytest tests/
# Run with coverage
poetry run pytest tests/ --cov=. --cov-report=html
# Run specific test file
poetry run pytest tests/test_auth.py -v
Test structure:
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"
Frontend (React Testing Library)
# Run tests
npm run test
# Run with coverage
npm run test -- --coverage
# Run E2E tests
npm run test:e2e
Test structure:
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()
})
})
Deployment Workflow
Pre-Deployment Checklist
- [ ] All tests passing locally
- [ ]
npm run buildsucceeds (frontend) - [ ]
poetry run pytestpasses (backend) - [ ] No hardcoded secrets
- [ ] Environment variables documented
- [ ] Database migrations ready
Deployment Commands
# Build and deploy frontend
cd frontend && npm run build
gcloud run deploy frontend --source .
# Build and deploy backend
cd backend
gcloud run deploy backend --source .
Environment Variables
# Frontend (.env.local)
NEXT_PUBLIC_API_URL=https://api.example.com
NEXT_PUBLIC_SUPABASE_URL=https://xxx.supabase.co
NEXT_PUBLIC_SUPABASE_ANON_KEY=eyJ...
# Backend (.env)
DATABASE_URL=postgresql://...
ANTHROPIC_API_KEY=sk-ant-...
SUPABASE_URL=https://xxx.supabase.co
SUPABASE_KEY=eyJ...
Critical Rules
- No emojis in code, comments, or documentation
- Immutability - never mutate objects or arrays
- TDD - write tests before implementation
- 80% coverage minimum
- Many small files - 200-400 lines typical, 800 max
- No console.log in production code
- Proper error handling with try/catch
- Input validation with Pydantic/Zod
Related Skills
coding-standards.md- General coding best practicesbackend-patterns.md- API and database patternsfrontend-patterns.md- React and Next.js patternstdd-workflow/- Test-driven development methodology
# Supported AI Coding Agents
This skill is compatible with the SKILL.md standard and works with all major AI coding agents:
Learn more about the SKILL.md standard and how to use these skills with your preferred AI coding agent.