agskills.dev
MARKETPLACE

cc-skill-project-guidelines-example

Project Guidelines Skill (Example)

davila721.2k1.9k

预览

SKILL.md
Metadata
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:

┌─────────────────────────────────────────────────────────────┐
│                         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   │
        └──────────┘   └──────────┘   └──────────┘

File Structure

project/
├── frontend/
│   └── src/
│       ├── app/              # Next.js app router pages
│       │   ├── api/          # API routes
│       │   ├── (auth)/       # Auth-protected routes
│       │   └── workspace/    # Main app workspace
│       ├── components/       # React components
│       │   ├── ui/           # Base UI components
│       │   ├── forms/        # Form components
│       │   └── layouts/      # Layout components
│       ├── hooks/            # Custom React hooks
│       ├── lib/              # Utilities
│       ├── types/            # TypeScript definitions
│       └── config/           # Configuration
│
├── backend/
│   ├── routers/              # FastAPI route handlers
│   ├── models.py             # Pydantic models
│   ├── main.py               # FastAPI app entry
│   ├── auth_system.py        # Authentication
│   ├── database.py           # Database operations
│   ├── services/             # Business logic
│   └── tests/                # pytest tests
│
├── deploy/                   # Deployment configs
├── docs/                     # Documentation
└── 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 build succeeds (frontend)
  • poetry run pytest passes (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

  1. No emojis in code, comments, or documentation
  2. Immutability - never mutate objects or arrays
  3. TDD - write tests before implementation
  4. 80% coverage minimum
  5. Many small files - 200-400 lines typical, 800 max
  6. No console.log in production code
  7. Proper error handling with try/catch
  8. Input validation with Pydantic/Zod

Related Skills

  • coding-standards.md - General coding best practices
  • backend-patterns.md - API and database patterns
  • frontend-patterns.md - React and Next.js patterns
  • tdd-workflow/ - Test-driven development methodology