cc-skill-project-guidelines-example
Project Guidelines Skill (Example)
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
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Project Guidelines Skill (Example)
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
Based on SOC occupation classification
Build AI agents that interact with computers like humans do - viewing screens, moving cursors, clicking buttons, and typing text. Covers Anthropic's Computer Use, OpenAI's Operator/CUA, and open-source alternatives. Critical focus on sandboxing, security, and handling the unique challenges of vision-based control. Use when: computer use, desktop automation agent, screen control AI, vision-based agent, GUI automation.
Expert in building products that wrap AI APIs (OpenAI, Anthropic, etc.) into focused tools people will pay for. Not just 'ChatGPT but different' - products that solve specific problems with AI. Covers prompt engineering for products, cost management, rate limiting, and building defensible AI businesses. Use when: AI wrapper, GPT product, AI tool, wrap AI, AI SaaS.
Automate GitHub workflows with AI assistance. Includes PR reviews, issue triage, CI/CD integration, and Git operations. Use when automating GitHub workflows, setting up PR review automation, creating GitHub Actions, or triaging issues.
Audit and rewrite content to remove AI writing patterns ("AI-isms"). Use this skill when asked to "remove AI-isms," "clean up AI writing," "edit writing for AI patterns," "audit writing for AI tells," or "make this sound less like AI." Supports a detect-only mode, an edit-in-place mode for files, an optional voice profile (casual / professional / technical / warm / blunt), and an iterate-to-convergence pass.
Free lite skill โ drafts one friendly (level-1) client document-request email for a single tax client from a short brief plus the practice profile. A strict subset of client-doc-chaser (no batch, no escalation levels 2/3), carrying the pack's full guard. Use to try the kit before buying, or as the directory-published funnel skill. Triggers - "client document request email", "chase a client's documents", "free document request writer", "doc-chaser-lite".
Generate patent-office-compliant figures via the PatentFig AI API โ patent line art from text (PNG or SVG), vectorize drawings to SVG/DXF/vector PDF, AI-upscale images, and convert to filing-ready TIFF/PDF/PNG. Use when the user asks for patent drawings, patent figures, invention diagrams, USPTO/EPO-compliant line art, vectorizing a drawing for CAD, or preparing figures for a patent filing. Requires a PATENTFIG_API_KEY environment variable.
| name | cc-skill-project-guidelines-example |
| description | Project Guidelines Skill (Example) |
| author | affaan-m |
| version | 1.0 |
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.
Reference this skill when working on the specific project it's designed for. Project skills contain:
Tech Stack:
Services:
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โ Frontend โ
โ Next.js 15 + TypeScript + TailwindCSS โ
โ Deployed: Vercel / Cloud Run โ
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โ
โผ
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โ Backend โ
โ FastAPI + Python 3.11 + Pydantic โ
โ Deployed: Cloud Run โ
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โ
โโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโ
โผ โผ โผ
โโโโโโโโโโโโ โโโโโโโโโโโโ โโโโโโโโโโโโ
โ Supabase โ โ Claude โ โ Redis โ
โ Database โ โ API โ โ Cache โ
โโโโโโโโโโโโ โโโโโโโโโโโโ โโโโโโโโโโโโ
project/
โโโ frontend/
โ โโโ src/
โ โโโ app/ # Next.js app router pages
โ โ โโโ api/ # API routes
โ โ โโโ (auth)/ # Auth-protected routes
โ โ โโโ workspace/ # Main app workspace
โ โโโ components/ # React components
โ โ โโโ ui/ # Base UI components
โ โ โโโ forms/ # Form components
โ โ โโโ layouts/ # Layout components
โ โโโ hooks/ # Custom React hooks
โ โโโ lib/ # Utilities
โ โโโ types/ # TypeScript definitions
โ โโโ config/ # Configuration
โ
โโโ backend/
โ โโโ routers/ # FastAPI route handlers
โ โโโ models.py # Pydantic models
โ โโโ main.py # FastAPI app entry
โ โโโ auth_system.py # Authentication
โ โโโ database.py # Database operations
โ โโโ services/ # Business logic
โ โโโ tests/ # pytest tests
โ
โโโ deploy/ # Deployment configs
โโโ docs/ # Documentation
โโโ scripts/ # Utility scripts
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"}
)
# Extract tool use result
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 }
}
# 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"
# 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()
})
})
npm run build succeeds (frontend)poetry run pytest passes (backend)# 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 .
# 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...
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