install
source · Clone the upstream repo
git clone https://github.com/diegosouzapw/awesome-omni-skill
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/diegosouzapw/awesome-omni-skill "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/frontend/cc-skill-project-guidelines-example" ~/.claude/skills/diegosouzapw-awesome-omni-skill-cc-skill-project-guidelines-example-c5a2e2 && rm -rf "$T"
manifest:
skills/frontend/cc-skill-project-guidelines-example/SKILL.mdsource content
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
-
succeeds (frontend)npm run build -
passes (backend)poetry run pytest - 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
- General coding best practicescoding-standards.md
- API and database patternsbackend-patterns.md
- React and Next.js patternsfrontend-patterns.md
- Test-driven development methodologytdd-workflow/