Lord1Egypt Task Automation
Turn repetitive work into automated workflows. Save time, reduce errors, scale operations.
Automation Types
1. File Operations
Batch Rename:
import os
import re
def batch_rename(directory, pattern, replacement):
"""Rename files matching pattern"""
for filename in os.listdir(directory):
if re.match(pattern, filename):
new_name = re.sub(pattern, replacement, filename)
os.rename(
os.path.join(directory, filename),
os.path.join(directory, new_name)
)
print(f"Renamed: {filename} -> {new_name}")
Batch Convert:
from PIL import Image
import os
def convert_images(input_dir, output_dir, format='webp'):
"""Convert all images to format"""
os.makedirs(output_dir, exist_ok=True)
for filename in os.listdir(input_dir):
if filename.lower().endswith(('.png', '.jpg', '.jpeg')):
img = Image.open(os.path.join(input_dir, filename))
name = os.path.splitext(filename)[0]
img.save(os.path.join(output_dir, f"{name}.{format}"), format.upper())
print(f"Converted: {filename}")
Organize Files:
import os
import shutil
def organize_by_type(directory):
"""Move files into type folders"""
extensions = {
'images': ['.jpg', '.jpeg', '.png', '.gif', '.webp'],
'documents': ['.pdf', '.doc', '.docx', '.txt', '.md'],
'videos': ['.mp4', '.mov', '.avi', '.mkv'],
'audio': ['.mp3', '.wav', '.flac'],
'code': ['.py', '.js', '.ts', '.go', '.rs'],
}
for filename in os.listdir(directory):
filepath = os.path.join(directory, filename)
if os.path.isfile(filepath):
ext = os.path.splitext(filename)[1].lower()
for folder, exts in extensions.items():
if ext in exts:
target = os.path.join(directory, folder)
os.makedirs(target, exist_ok=True)
shutil.move(filepath, os.path.join(target, filename))
print(f"Moved {filename} to {folder}/")
break
2. Data Processing
Batch Transform:
import pandas as pd
def process_csv_batch(input_dir, output_file, transform_func):
"""Process multiple CSVs and combine"""
dfs = []
for filename in os.listdir(input_dir):
if filename.endswith('.csv'):
df = pd.read_csv(os.path.join(input_dir, filename))
df = transform_func(df)
dfs.append(df)
combined = pd.concat(dfs, ignore_index=True)
combined.to_csv(output_file, index=False)
print(f"Processed {len(dfs)} files into {output_file}")
Data Pipeline:
def create_pipeline(steps):
"""Create reusable data pipeline"""
def pipeline(data):
result = data
for step in steps:
result = step(result)
return result
return pipeline
# Example usage:
pipeline = create_pipeline([
lambda x: x.dropna(),
lambda x: x.drop_duplicates(),
lambda x: x[x['value'] > 0],
lambda x: x.sort_values('date')
])
clean_data = pipeline(raw_data)
3. API Operations
Rate-Limited API Client:
import time
from functools import wraps
def rate_limit(calls_per_second=2):
"""Decorator to rate limit API calls"""
min_interval = 1.0 / calls_per_second
last_call = [0.0]
def decorator(func):
@wraps(func)
def wrapper(*args, **kwargs):
elapsed = time.time() - last_call[0]
if elapsed < min_interval:
time.sleep(min_interval - elapsed)
last_call[0] = time.time()
return func(*args, **kwargs)
return wrapper
return decorator
@rate_limit(2) # 2 calls per second
def api_call(endpoint, data):
return requests.post(endpoint, json=data)
Batch API Calls:
def batch_api_calls(items, endpoint, batch_size=100):
"""Process API calls in batches"""
results = []
for i in range(0, len(items), batch_size):
batch = items[i:i + batch_size]
# Process batch
response = requests.post(endpoint, json={'items': batch})
if response.status_code == 200:
results.extend(response.json())
else:
print(f"Batch {i//batch_size} failed: {response.status_code}")
time.sleep(1) # Rate limiting
return results
Retry with Backoff:
import time
import random
def retry_with_backoff(func, max_retries=3, base_delay=1):
"""Retry failed calls with exponential backoff"""
for attempt in range(max_retries):
try:
return func()
except Exception as e:
if attempt == max_retries - 1:
raise
delay = base_delay * (2 ** attempt) + random.random()
print(f"Retry {attempt + 1}/{max_retries} in {delay:.1f}s: {e}")
time.sleep(delay)
4. Scheduled Tasks
Cron Jobs:
# Every hour
0 * * * * /path/to/script.sh
# Every day at 9 AM
0 9 * * * /path/to/script.sh
# Every Monday at 9 AM
0 9 * * 1 /path/to/script.sh
# Every hour on weekdays
0 * * * 1-5 /path/to/script.sh
Python Scheduler:
import schedule
import time
def job():
print("Running scheduled task...")
schedule.every(10).minutes.do(job)
schedule.every().hour.do(job)
schedule.every().day.at("09:00").do(job)
schedule.every().monday.do(job)
while True:
schedule.run_pending()
time.sleep(60)
OpenClaw Cron:
openclaw cron add \
--name "Daily Report" \
--schedule "0 9 * * *" \
--task "Generate daily report and send to slack"
Workflow Patterns
Sequential Pipeline
def sequential_workflow(steps):
"""Run steps in sequence"""
results = []
for i, step in enumerate(steps):
try:
result = step['action'](**step.get('params', {}))
results.append({'step': i, 'status': 'success', 'result': result})
except Exception as e:
results.append({'step': i, 'status': 'error', 'error': str(e)})
if step.get('stop_on_error', True):
break
return results
Parallel Execution
from concurrent.futures import ThreadPoolExecutor, as_completed
def parallel_workflow(tasks, max_workers=5):
"""Run tasks in parallel"""
results = []
with ThreadPoolExecutor(max_workers=max_workers) as executor:
futures = {executor.submit(task['action'], **task.get('params', {})): task
for task in tasks}
for future in as_completed(futures):
task = futures[future]
try:
result = future.result()
results.append({'task': task['name'], 'status': 'success', 'result': result})
except Exception as e:
results.append({'task': task['name'], 'status': 'error', 'error': str(e)})
return results
Conditional Workflow
def conditional_workflow(steps):
"""Run steps based on conditions"""
context = {}
for step in steps:
# Check condition
if 'condition' in step:
if not step['condition'](context):
print(f"Skipping {step['name']}: condition not met")
continue
# Execute step
result = step['action'](**step.get('params', {}), context=context)
context[step['name']] = result
return context
Error Handling
Graceful Degradation
def robust_operation(data, fallback=None):
"""Try operation with fallback"""
try:
return primary_operation(data)
except SpecificError as e:
print(f"Primary failed: {e}, trying fallback")
return fallback_operation(data) if fallback else None
except Exception as e:
print(f"All options failed: {e}")
return fallback
Error Notification
def notify_on_error(func, notify_func):
"""Decorator to notify on errors"""
@wraps(func)
def wrapper(*args, **kwargs):
try:
return func(*args, **kwargs)
except Exception as e:
notify_func(f"Error in {func.__name__}: {e}")
raise
return wrapper
@notify_on_error(send_slack_message)
def important_operation():
# ...
Monitoring
Progress Tracking
from tqdm import tqdm
def process_with_progress(items):
"""Process items with progress bar"""
results = []
for item in tqdm(items, desc="Processing"):
results.append(process(item))
return results
Logging
import logging
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(levelname)s - %(message)s',
filename='automation.log'
)
logger = logging.getLogger(__name__)
def logged_operation(data):
logger.info(f"Starting operation with {len(data)} items")
try:
result = process(data)
logger.info(f"Operation completed: {len(result)} results")
return result
except Exception as e:
logger.error(f"Operation failed: {e}")
raise
Best Practices
1. Start Simple
Manual → Script → Scheduled → Monitored
Don't over-engineer. Start with a manual process, then automate.
2. Make Idempotent
def safe_operation(data):
"""Can be run multiple times safely"""
# Check if already done
if already_processed(data):
return get_cached_result(data)
# Process
result = process(data)
# Mark as done
mark_processed(data)
return result
3. Add Checkpoints
def long_running_workflow(data):
"""Save progress at checkpoints"""
checkpoint_file = "workflow_checkpoint.json"
# Load checkpoint if exists
if os.path.exists(checkpoint_file):
with open(checkpoint_file) as f:
state = json.load(f)
start_from = state['step']
data = state['data']
else:
start_from = 0
# Process with checkpoints
for i, step in enumerate(steps[start_from:], start=start_from):
result = step(data)
# Save checkpoint
with open(checkpoint_file, 'w') as f:
json.dump({'step': i + 1, 'data': result}, f)
# Clean up
os.remove(checkpoint_file)
return result
4. Test Thoroughly
def test_automation():
"""Test automation with mock data"""
test_data = create_mock_data()
# Dry run
result = automation(test_data, dry_run=True)
# Validate
assert result['status'] == 'success'
assert len(result['output']) == expected_count
print("All tests passed!")
5. Document Everything
def automated_task(config):
"""
Process daily sales data and generate report.
Args:
config: Dict with keys:
- input_dir: Directory with CSV files
- output_file: Path for output report
- notify: Email to notify on completion
Returns:
Dict with keys:
- status: 'success' or 'error'
- records_processed: Number of records
- output_file: Path to generated report
Example:
result = automated_task({
'input_dir': '/data/sales',
'output_file': '/reports/daily.csv',
'notify': 'team@company.com'
})
"""
# Implementation...
Common Use Cases
Daily Report Automation
def daily_report():
# 1. Fetch data
data = fetch_from_sources()
# 2. Process
processed = process_data(data)
# 3. Generate report
report = generate_report(processed)
# 4. Distribute
send_email(report)
upload_to_slack(report)
# 5. Archive
archive_report(report)
Data Synchronization
def sync_data():
# 1. Get last sync state
last_sync = get_last_sync_time()
# 2. Fetch changes
changes = fetch_changes_since(last_sync)
# 3. Apply changes
for change in changes:
apply_change(change)
# 4. Update sync state
update_sync_time()
Cleanup Automation
def cleanup():
# 1. Remove old files
remove_old_files(days=30)
# 2. Clear temp directories
clear_temp_dirs()
# 3. Archive old logs
archive_logs()
# 4. Optimize database
optimize_database()