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# CLAUDE.md
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
## Keeping This File Up to Date
Update this file whenever a lesson is learned during development. Specifically, update CLAUDE.md when:
- A non-obvious architectural decision is made or discovered
- A gotcha, footgun, or surprising behavior is encountered (and the fix/workaround)
- A new command, workflow, or tool is added to the project
- A convention is established that isn't obvious from reading the code
- An integration detail is clarified (e.g., how the WebSocket protocol actually behaves, edge cases in the agent tool call cycle)
Do **not** add things already derivable from reading the code, generic best practices, or ephemeral task notes — only durable, reusable knowledge.
## Repository Layout
This repo contains two independent projects:
- **`adiuva/`** — Electron desktop app (TypeScript/React)
- **`adiuva-api/`** — FastAPI backend (Python)
---
## adiuva (Electron App)
### Commands
```bash
npm run start # Start dev server (Electron + Vite)
npm run lint # ESLint
npm run knip # Dead code analysis
npm run make # Build installers (Windows/Linux/macOS)
npm run package # Package without creating installers
```
Database schema changes require running Drizzle Kit — check `package.json` for db commands.
### Architecture
```
Renderer (React 19 + TanStack Router)
↓ tRPC over contextBridge
Main Process (Electron)
├── SQLite (better-sqlite3 + Drizzle ORM) — local app data
├── LanceDB — local vector embeddings
└── WebSocket client → adiuva-api backend
```
**IPC model**: The renderer calls tRPC procedures defined in `src/main/router/`. The preload script (`src/preload/`) bridges them with `contextIsolation: true`.
**Backend integration**: The Electron main process connects to the FastAPI backend via WebSocket. The backend sends tool calls (e.g., `insert`, `vector_search`) which the main process executes against local SQLite via Drizzle and returns results. All AI intelligence lives on the backend — the app is a smart terminal.
**Key source directories**:
- `src/main/agents/` — Agent scheduler
- `src/main/ai/` — Orchestrator, token management
- `src/main/db/` — SQLite schema (Drizzle) + LanceDB
- `src/main/router/` — tRPC router (all IPC procedures)
- `src/renderer/components/` — UI components (tasks, notes, projects, timeline, auth)
- `src/renderer/routes/` — TanStack Router pages
- `src/shared/` — Zod schemas shared between main/renderer (WebSocket frame types, casing utils)
**Path aliases** (tsconfig): `@/*` → `src/renderer/`, `@shared/*` → `src/shared/`
**WebSocket frame types** are defined in `src/shared/api-types.ts` using Zod. Client sends: `chat_request`, `floating_request`, `tool_result`. Server sends: `text_chunk`, `tool_call`, `final`, `ping`.
---
## adiuva-api (FastAPI Backend)
### Commands
```bash
# Development
uvicorn app.main:app --reload --host 0.0.0.0 --port 8000
# Production
gunicorn app.main:app -k uvicorn.workers.UvicornWorker -w 4 --timeout 120
# Database migrations
alembic upgrade head
# Testing
pytest
pytest -v
pytest tests/test_agents.py # single test file
# Linting/formatting
ruff check .
ruff format .
# Docker (full stack)
docker compose up --build
```
### Architecture
```
FastAPI app (app/main.py)
├── Middleware: RateLimiter → Sanitizer → CORS
├── HTTP Routes (app/api/routes/)
│ ├── auth.py — register, login, token refresh
│ ├── chat.py — POST /chat, POST /chat/embed, WS /chat/stream
│ └── billing.py — Stripe subscriptions
├── Agent System (app/agents/)
│ ├── task_agent.py — 8 tools
│ ├── project_agent.py — 6 tools
│ ├── timeline_agent.py — 4 tools
│ └── note_agent.py — 5 tools
└── Orchestration (app/core/)
├── agent_registry.py
├── agent_runner.py
├── llm.py — LiteLLM factory (100+ providers)
└── memory_middleware.py
```
**LLM routing**: GPT-4o-mini classifies incoming intent → routes to appropriate domain agent → agent uses GPT-4o with its tool set → sends tool calls back to Electron client for local execution.
**Zero-trust data model**: The backend never decrypts user data. PostgreSQL stores only auth, billing, and metadata. All user content stays local on the Electron client.
**Tier system**: Free / Pro / Power / Team — enforced in `app/api/middleware/rate_limit.py` (20–200 req/min sliding window) and `app/billing/tier_manager.py`.
**Key config**: `app/config/settings.py` — all env vars via Pydantic Settings. Copy `.env.example` to `.env` for local dev.
**Database**: PostgreSQL with async SQLAlchemy 2.0 + asyncpg. Migrations in `alembic/versions/`. Models in `app/models.py`, Pydantic schemas in `app/schemas.py`.
**Testing**: pytest with pytest-asyncio. Fixtures in `tests/conftest.py`. Use in-memory SQLite for DB tests.
---
## Microservices Migration (In Progress)
The monolith (`adiuva-api/app/`) is being split into independent services under `adiuva-api/services/`. Architectural decisions are tracked in repo memory (`/memories/repo/microservices-architecture.md`).
### Target Services (MVP)
| Service | Owns | Scaling |
|---------|------|---------|
| **Auth** | JWT RS256 issuance, users, refresh_tokens, subscriptions | Stateless |
| **WS Gateway** | WebSocket connections, Redis frame routing, device registry | Sticky (user_id) |
| **Chat** | deep_agent, memory, domain agents (task/note/project/timeline), LLM | Stateless |
| **Batch Agent** | agent_runner, journey builder, filesystem_agent, integrations (+ Langfuse tracing TODO) | Stateless |
| **Billing** | Stripe, tier_manager | Stateless |
**API Gateway**: Traefik with ForwardAuth → Auth `/verify`. Injects `X-User-Id`, `X-User-Email`, `X-User-Tier` headers. Downstream services trust these headers.
### Monorepo Structure
```
adiuva-api/
├── shared/ ← SQLAlchemy models, Pydantic schemas, config, redis utils
├── services/
│ ├── auth/
│ ├── ws-gateway/
│ ├── chat/
│ ├── batch-agent/
│ └── billing/
├── alembic/ ← Centralized migrations (shared DB)
├── docker-compose.yml
└── traefik/
```
### Key Conventions
- **shared/ module**: Imported by all services. Contains models, schemas, config, DB session factory, Redis client. Changes here affect all services — be careful.
- **Redis is the glue**: WS Gateway ↔ Chat/Batch communication is entirely via Redis pub/sub and lists. See `/memories/repo/microservices-architecture.md` for channel naming.
- **No JWT validation in downstream services**: Only Auth Service has the private key. Other services receive pre-validated identity via Traefik headers.
- **Tool call round-trip**: Chat/Batch → publish `tool_call` to `ws:out:{user_id}` → WS Gateway forwards to Electron → Electron replies `tool_result` → WS Gateway LPUSH to `tool:result:{call_id}` → Chat/Batch BRPOP with 30s timeout.
- **Migration strategy**: Strangler fig. Extract one service at a time. The monolith `app/` continues to work until all services are extracted. Don't delete monolith code until the service replacement is tested.
- **Storage & Plugin services**: Removed from codebase. Will be re-evaluated in future feature planning.
### Commands (Microservices)
```bash
# Full stack
docker compose up --build
# Single service dev (example: chat)
docker compose up redis postgres auth
cd services/chat && uvicorn app.main:app --reload --port 8002
# Migrations (still centralized)
alembic upgrade head
```

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{
"mcpServers": {
"langfuse-docs": {
"transportType": "http",
"url": "https://langfuse.com/api/mcp",
"verifySsl": true
}
}
}

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{
"enabledMcpjsonServers": [
"langfuse-docs"
],
"enableAllProjectMcpServers": true
}

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---
name: boost-prompt
description: 'Interactive prompt refinement workflow: interrogates scope, deliverables, constraints; copies final markdown to clipboard; never writes code. Requires the Joyride extension.'
---
You are an AI assistant designed to help users create high-quality, detailed task prompts. DO NOT WRITE ANY CODE.
Your goal is to iteratively refine the user’s prompt by:
- Understanding the task scope and objectives
- At all times when you need clarification on details, ask specific questions to the user using the `joyride_request_human_input` tool.
- Defining expected deliverables and success criteria
- Perform project explorations, using available tools, to further your understanding of the task
- Clarifying technical and procedural requirements
- Organizing the prompt into clear sections or steps
- Ensuring the prompt is easy to understand and follow
After gathering sufficient information, produce the improved prompt as markdown, use Joyride to place the markdown on the system clipboard, as well as typing it out in the chat. Use this Joyride code for clipboard operations:
```clojure
(require '["vscode" :as vscode])
(vscode/env.clipboard.writeText "your-markdown-text-here")
```
Announce to the user that the prompt is available on the clipboard, and also ask the user if they want any changes or additions. Repeat the copy + chat + ask after any revisions of the prompt.

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---
name: brand-guidelines
description: Applies Anthropic's official brand colors and typography to any sort of artifact that may benefit from having Anthropic's look-and-feel. Use it when brand colors or style guidelines, visual formatting, or company design standards apply.
license: Complete terms in LICENSE.txt
---
# Anthropic Brand Styling
## Overview
To access Anthropic's official brand identity and style resources, use this skill.
**Keywords**: branding, corporate identity, visual identity, post-processing, styling, brand colors, typography, Anthropic brand, visual formatting, visual design
## Brand Guidelines
### Colors
**Main Colors:**
- Dark: `#141413` - Primary text and dark backgrounds
- Light: `#faf9f5` - Light backgrounds and text on dark
- Mid Gray: `#b0aea5` - Secondary elements
- Light Gray: `#e8e6dc` - Subtle backgrounds
**Accent Colors:**
- Orange: `#d97757` - Primary accent
- Blue: `#6a9bcc` - Secondary accent
- Green: `#788c5d` - Tertiary accent
### Typography
- **Headings**: Poppins (with Arial fallback)
- **Body Text**: Lora (with Georgia fallback)
- **Note**: Fonts should be pre-installed in your environment for best results
## Features
### Smart Font Application
- Applies Poppins font to headings (24pt and larger)
- Applies Lora font to body text
- Automatically falls back to Arial/Georgia if custom fonts unavailable
- Preserves readability across all systems
### Text Styling
- Headings (24pt+): Poppins font
- Body text: Lora font
- Smart color selection based on background
- Preserves text hierarchy and formatting
### Shape and Accent Colors
- Non-text shapes use accent colors
- Cycles through orange, blue, and green accents
- Maintains visual interest while staying on-brand
## Technical Details
### Font Management
- Uses system-installed Poppins and Lora fonts when available
- Provides automatic fallback to Arial (headings) and Georgia (body)
- No font installation required - works with existing system fonts
- For best results, pre-install Poppins and Lora fonts in your environment
### Color Application
- Uses RGB color values for precise brand matching
- Applied via python-pptx's RGBColor class
- Maintains color fidelity across different systems

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---
name: frontend-design
description: Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, artifacts, posters, or applications (examples include websites, landing pages, dashboards, React components, HTML/CSS layouts, or when styling/beautifying any web UI). Generates creative, polished code and UI design that avoids generic AI aesthetics.
license: Complete terms in LICENSE.txt
---
This skill guides creation of distinctive, production-grade frontend interfaces that avoid generic "AI slop" aesthetics. Implement real working code with exceptional attention to aesthetic details and creative choices.
The user provides frontend requirements: a component, page, application, or interface to build. They may include context about the purpose, audience, or technical constraints.
## Design Thinking
Before coding, understand the context and commit to a BOLD aesthetic direction:
- **Purpose**: What problem does this interface solve? Who uses it?
- **Tone**: Pick an extreme: brutally minimal, maximalist chaos, retro-futuristic, organic/natural, luxury/refined, playful/toy-like, editorial/magazine, brutalist/raw, art deco/geometric, soft/pastel, industrial/utilitarian, etc. There are so many flavors to choose from. Use these for inspiration but design one that is true to the aesthetic direction.
- **Constraints**: Technical requirements (framework, performance, accessibility).
- **Differentiation**: What makes this UNFORGETTABLE? What's the one thing someone will remember?
**CRITICAL**: Choose a clear conceptual direction and execute it with precision. Bold maximalism and refined minimalism both work - the key is intentionality, not intensity.
Then implement working code (HTML/CSS/JS, React, Vue, etc.) that is:
- Production-grade and functional
- Visually striking and memorable
- Cohesive with a clear aesthetic point-of-view
- Meticulously refined in every detail
## Frontend Aesthetics Guidelines
Focus on:
- **Typography**: Choose fonts that are beautiful, unique, and interesting. Avoid generic fonts like Arial and Inter; opt instead for distinctive choices that elevate the frontend's aesthetics; unexpected, characterful font choices. Pair a distinctive display font with a refined body font.
- **Color & Theme**: Commit to a cohesive aesthetic. Use CSS variables for consistency. Dominant colors with sharp accents outperform timid, evenly-distributed palettes.
- **Motion**: Use animations for effects and micro-interactions. Prioritize CSS-only solutions for HTML. Use Motion library for React when available. Focus on high-impact moments: one well-orchestrated page load with staggered reveals (animation-delay) creates more delight than scattered micro-interactions. Use scroll-triggering and hover states that surprise.
- **Spatial Composition**: Unexpected layouts. Asymmetry. Overlap. Diagonal flow. Grid-breaking elements. Generous negative space OR controlled density.
- **Backgrounds & Visual Details**: Create atmosphere and depth rather than defaulting to solid colors. Add contextual effects and textures that match the overall aesthetic. Apply creative forms like gradient meshes, noise textures, geometric patterns, layered transparencies, dramatic shadows, decorative borders, custom cursors, and grain overlays.
NEVER use generic AI-generated aesthetics like overused font families (Inter, Roboto, Arial, system fonts), cliched color schemes (particularly purple gradients on white backgrounds), predictable layouts and component patterns, and cookie-cutter design that lacks context-specific character.
Interpret creatively and make unexpected choices that feel genuinely designed for the context. No design should be the same. Vary between light and dark themes, different fonts, different aesthetics. NEVER converge on common choices (Space Grotesk, for example) across generations.
**IMPORTANT**: Match implementation complexity to the aesthetic vision. Maximalist designs need elaborate code with extensive animations and effects. Minimalist or refined designs need restraint, precision, and careful attention to spacing, typography, and subtle details. Elegance comes from executing the vision well.
Remember: Claude is capable of extraordinary creative work. Don't hold back, show what can truly be created when thinking outside the box and committing fully to a distinctive vision.

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---
name: webapp-testing
description: Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.
license: Complete terms in LICENSE.txt
---
# Web Application Testing
To test local web applications, write native Python Playwright scripts.
**Helper Scripts Available**:
- `scripts/with_server.py` - Manages server lifecycle (supports multiple servers)
**Always run scripts with `--help` first** to see usage. DO NOT read the source until you try running the script first and find that a customized solution is abslutely necessary. These scripts can be very large and thus pollute your context window. They exist to be called directly as black-box scripts rather than ingested into your context window.
## Decision Tree: Choosing Your Approach
```
User task → Is it static HTML?
├─ Yes → Read HTML file directly to identify selectors
│ ├─ Success → Write Playwright script using selectors
│ └─ Fails/Incomplete → Treat as dynamic (below)
│
└─ No (dynamic webapp) → Is the server already running?
├─ No → Run: python scripts/with_server.py --help
│ Then use the helper + write simplified Playwright script
│
└─ Yes → Reconnaissance-then-action:
1. Navigate and wait for networkidle
2. Take screenshot or inspect DOM
3. Identify selectors from rendered state
4. Execute actions with discovered selectors
```
## Example: Using with_server.py
To start a server, run `--help` first, then use the helper:
**Single server:**
```bash
python scripts/with_server.py --server "npm run dev" --port 5173 -- python your_automation.py
```
**Multiple servers (e.g., backend + frontend):**
```bash
python scripts/with_server.py \
--server "cd backend && python server.py" --port 3000 \
--server "cd frontend && npm run dev" --port 5173 \
-- python your_automation.py
```
To create an automation script, include only Playwright logic (servers are managed automatically):
```python
from playwright.sync_api import sync_playwright
with sync_playwright() as p:
browser = p.chromium.launch(headless=True) # Always launch chromium in headless mode
page = browser.new_page()
page.goto('http://localhost:5173') # Server already running and ready
page.wait_for_load_state('networkidle') # CRITICAL: Wait for JS to execute
# ... your automation logic
browser.close()
```
## Reconnaissance-Then-Action Pattern
1. **Inspect rendered DOM**:
```python
page.screenshot(path='/tmp/inspect.png', full_page=True)
content = page.content()
page.locator('button').all()
```
2. **Identify selectors** from inspection results
3. **Execute actions** using discovered selectors
## Common Pitfall
❌ **Don't** inspect the DOM before waiting for `networkidle` on dynamic apps
✅ **Do** wait for `page.wait_for_load_state('networkidle')` before inspection
## Best Practices
- **Use bundled scripts as black boxes** - To accomplish a task, consider whether one of the scripts available in `scripts/` can help. These scripts handle common, complex workflows reliably without cluttering the context window. Use `--help` to see usage, then invoke directly.
- Use `sync_playwright()` for synchronous scripts
- Always close the browser when done
- Use descriptive selectors: `text=`, `role=`, CSS selectors, or IDs
- Add appropriate waits: `page.wait_for_selector()` or `page.wait_for_timeout()`
## Reference Files
- **examples/** - Examples showing common patterns:
- `element_discovery.py` - Discovering buttons, links, and inputs on a page
- `static_html_automation.py` - Using file:// URLs for local HTML
- `console_logging.py` - Capturing console logs during automation

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from playwright.sync_api import sync_playwright
# Example: Capturing console logs during browser automation
url = 'http://localhost:5173' # Replace with your URL
console_logs = []
with sync_playwright() as p:
browser = p.chromium.launch(headless=True)
page = browser.new_page(viewport={'width': 1920, 'height': 1080})
# Set up console log capture
def handle_console_message(msg):
console_logs.append(f"[{msg.type}] {msg.text}")
print(f"Console: [{msg.type}] {msg.text}")
page.on("console", handle_console_message)
# Navigate to page
page.goto(url)
page.wait_for_load_state('networkidle')
# Interact with the page (triggers console logs)
page.click('text=Dashboard')
page.wait_for_timeout(1000)
browser.close()
# Save console logs to file
with open('/mnt/user-data/outputs/console.log', 'w') as f:
f.write('\n'.join(console_logs))
print(f"\nCaptured {len(console_logs)} console messages")
print(f"Logs saved to: /mnt/user-data/outputs/console.log")

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from playwright.sync_api import sync_playwright
# Example: Discovering buttons and other elements on a page
with sync_playwright() as p:
browser = p.chromium.launch(headless=True)
page = browser.new_page()
# Navigate to page and wait for it to fully load
page.goto('http://localhost:5173')
page.wait_for_load_state('networkidle')
# Discover all buttons on the page
buttons = page.locator('button').all()
print(f"Found {len(buttons)} buttons:")
for i, button in enumerate(buttons):
text = button.inner_text() if button.is_visible() else "[hidden]"
print(f" [{i}] {text}")
# Discover links
links = page.locator('a[href]').all()
print(f"\nFound {len(links)} links:")
for link in links[:5]: # Show first 5
text = link.inner_text().strip()
href = link.get_attribute('href')
print(f" - {text} -> {href}")
# Discover input fields
inputs = page.locator('input, textarea, select').all()
print(f"\nFound {len(inputs)} input fields:")
for input_elem in inputs:
name = input_elem.get_attribute('name') or input_elem.get_attribute('id') or "[unnamed]"
input_type = input_elem.get_attribute('type') or 'text'
print(f" - {name} ({input_type})")
# Take screenshot for visual reference
page.screenshot(path='/tmp/page_discovery.png', full_page=True)
print("\nScreenshot saved to /tmp/page_discovery.png")
browser.close()

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from playwright.sync_api import sync_playwright
import os
# Example: Automating interaction with static HTML files using file:// URLs
html_file_path = os.path.abspath('path/to/your/file.html')
file_url = f'file://{html_file_path}'
with sync_playwright() as p:
browser = p.chromium.launch(headless=True)
page = browser.new_page(viewport={'width': 1920, 'height': 1080})
# Navigate to local HTML file
page.goto(file_url)
# Take screenshot
page.screenshot(path='/mnt/user-data/outputs/static_page.png', full_page=True)
# Interact with elements
page.click('text=Click Me')
page.fill('#name', 'John Doe')
page.fill('#email', 'john@example.com')
# Submit form
page.click('button[type="submit"]')
page.wait_for_timeout(500)
# Take final screenshot
page.screenshot(path='/mnt/user-data/outputs/after_submit.png', full_page=True)
browser.close()
print("Static HTML automation completed!")

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#!/usr/bin/env python3
"""
Start one or more servers, wait for them to be ready, run a command, then clean up.
Usage:
# Single server
python scripts/with_server.py --server "npm run dev" --port 5173 -- python automation.py
python scripts/with_server.py --server "npm start" --port 3000 -- python test.py
# Multiple servers
python scripts/with_server.py \
--server "cd backend && python server.py" --port 3000 \
--server "cd frontend && npm run dev" --port 5173 \
-- python test.py
"""
import subprocess
import socket
import time
import sys
import argparse
def is_server_ready(port, timeout=30):
"""Wait for server to be ready by polling the port."""
start_time = time.time()
while time.time() - start_time < timeout:
try:
with socket.create_connection(('localhost', port), timeout=1):
return True
except (socket.error, ConnectionRefusedError):
time.sleep(0.5)
return False
def main():
parser = argparse.ArgumentParser(description='Run command with one or more servers')
parser.add_argument('--server', action='append', dest='servers', required=True, help='Server command (can be repeated)')
parser.add_argument('--port', action='append', dest='ports', type=int, required=True, help='Port for each server (must match --server count)')
parser.add_argument('--timeout', type=int, default=30, help='Timeout in seconds per server (default: 30)')
parser.add_argument('command', nargs=argparse.REMAINDER, help='Command to run after server(s) ready')
args = parser.parse_args()
# Remove the '--' separator if present
if args.command and args.command[0] == '--':
args.command = args.command[1:]
if not args.command:
print("Error: No command specified to run")
sys.exit(1)
# Parse server configurations
if len(args.servers) != len(args.ports):
print("Error: Number of --server and --port arguments must match")
sys.exit(1)
servers = []
for cmd, port in zip(args.servers, args.ports):
servers.append({'cmd': cmd, 'port': port})
server_processes = []
try:
# Start all servers
for i, server in enumerate(servers):
print(f"Starting server {i+1}/{len(servers)}: {server['cmd']}")
# Use shell=True to support commands with cd and &&
process = subprocess.Popen(
server['cmd'],
shell=True,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE
)
server_processes.append(process)
# Wait for this server to be ready
print(f"Waiting for server on port {server['port']}...")
if not is_server_ready(server['port'], timeout=args.timeout):
raise RuntimeError(f"Server failed to start on port {server['port']} within {args.timeout}s")
print(f"Server ready on port {server['port']}")
print(f"\nAll {len(servers)} server(s) ready")
# Run the command
print(f"Running: {' '.join(args.command)}\n")
result = subprocess.run(args.command)
sys.exit(result.returncode)
finally:
# Clean up all servers
print(f"\nStopping {len(server_processes)} server(s)...")
for i, process in enumerate(server_processes):
try:
process.terminate()
process.wait(timeout=5)
except subprocess.TimeoutExpired:
process.kill()
process.wait()
print(f"Server {i+1} stopped")
print("All servers stopped")
if __name__ == '__main__':
main()