Openclaw Plus

# 1. Install dependencies
install_package("pandas requests")

# 2. Fetch data from API
data = call_api("https://api.example.com/dataset")

# 3. Process with Python
run_python("""
import pandas as pd
import json

with open('raw_data.json', 'r') as f:
    data = json.load(f)

df = pd.DataFrame(data)
df_cleaned = df.dropna()
df_cleaned.to_csv('cleaned_data.csv', index=False)
print(f'Processed {len(df_cleaned)} records')
""")

# 4. Commit results
git_commit("feat: add cleaned dataset")

# 1. Install scraping tools
install_package("beautifulsoup4 lxml requests")

# 2. Fetch webpage
html = fetch_url("https://example.com/data-page")

# 3. Parse and analyze
run_python("""
from bs4 import BeautifulSoup
import json

with open('page.html', 'r') as f:
    soup = BeautifulSoup(f, 'lxml')

data = []
for item in soup.find_all('div', class_='data-item'):
    data.append({
        'title': item.find('h2').text,
        'value': item.find('span', class_='value').text
    })

with open('scraped_data.json', 'w') as f:
    json.dump(data, f, indent=2)
""")

# 4. Check and commit
git_status()
git_commit("chore: update scraped data")

# 1. Install testing tools
install_package("pytest requests-mock")

# 2. Run tests
run_python("""
import requests
import json

# Test API endpoint
response = requests.get('https://api.example.com/health')
assert response.status_code == 200

# Test with authentication
headers = {'Authorization': 'Bearer test-token'}
response = requests.get('https://api.example.com/data', headers=headers)
print(f'Status: {response.status_code}')
print(f'Data: {response.json()}')
""")

# 3. Commit test results
git_commit("test: add API integration tests")

# 1. Fetch data from multiple sources
api_data = call_api("https://api.example.com/metrics")
web_data = fetch_url("https://example.com/reports/latest")

# 2. Process and generate report
install_package("matplotlib pandas")
run_python("""
import pandas as pd
import matplotlib.pyplot as plt
import json

with open('api_data.json', 'r') as f:
    data = json.load(f)

df = pd.DataFrame(data)
df['date'] = pd.to_datetime(df['date'])

plt.figure(figsize=(10, 6))
plt.plot(df['date'], df['value'])
plt.title('Metrics Over Time')
plt.savefig('report.png')
print('Report generated')
""")

# 3. Commit report
git_commit("docs: add automated metrics report")

try:
    result = run_python(code)
except SyntaxError as e:
    print(f"Syntax error: {e}")
except RuntimeError as e:
    print(f"Runtime error: {e}")

# Handle already installed
if package_installed("pandas"):
    print("Package already installed")
else:
    install_package("pandas")

# Handle installation failure
try:
    install_package("nonexistent-package")
except Exception as e:
    print(f"Installation failed: {e}")

# Not a git repository
if not is_git_repo():
    print("Not a git repository")
    exit(1)

# Nothing to commit
status = git_status()
if "nothing to commit" in status:
    print("No changes to commit")

# Handle timeouts
try:
    data = fetch_url(url, timeout=5)
except TimeoutError:
    print("Request timed out")

# Handle HTTP errors
try:
    response = call_api(url)
except requests.HTTPError as e:
    print(f"HTTP error: {e.response.status_code}")

# User request: "Fetch weather data, analyze it, and commit results"

# Step 1: Install dependencies
install_package("requests pandas matplotlib")

# Step 2: Fetch data
weather_data = call_api(
    "https://api.weather.com/data",
    auth_token="your-api-key"
)

# Step 3: Save and analyze
run_python("""
import pandas as pd
import matplotlib.pyplot as plt
import json

# Load data
with open('weather_data.json', 'r') as f:
    data = json.load(f)

# Create DataFrame
df = pd.DataFrame(data['forecast'])
df['date'] = pd.to_datetime(df['date'])

# Analyze
avg_temp = df['temperature'].mean()
max_temp = df['temperature'].max()
min_temp = df['temperature'].min()

# Generate plot
plt.figure(figsize=(12, 6))
plt.plot(df['date'], df['temperature'], marker='o')
plt.title('Temperature Forecast')
plt.xlabel('Date')
plt.ylabel('Temperature (°F)')
plt.grid(True)
plt.savefig('temperature_forecast.png')

# Save summary
summary = {
    'avg_temp': avg_temp,
    'max_temp': max_temp,
    'min_temp': min_temp,
    'records': len(df)
}

with open('weather_summary.json', 'w') as f:
    json.dump(summary, f, indent=2)

print(f'Analysis complete: {len(df)} records processed')
print(f'Average temperature: {avg_temp:.1f}°F')
""")

# Step 4: Commit results
git_status()
git_commit("""
feat: add weather data analysis

- Fetch 7-day forecast from API
- Generate temperature plot
- Create summary statistics
""")

# User request: "Scrape product data and save to database"

# Step 1: Install tools
install_package("beautifulsoup4 lxml requests sqlite3")

# Step 2: Fetch webpage
html = fetch_url("https://example-shop.com/products")

# Step 3: Parse and store
run_python("""
from bs4 import BeautifulSoup
import sqlite3
import json

# Parse HTML
with open('products.html', 'r') as f:
    soup = BeautifulSoup(f, 'lxml')

products = []
for item in soup.find_all('div', class_='product'):
    product = {
        'name': item.find('h3').text.strip(),
        'price': float(item.find('span', class_='price').text.strip('$')),
        'rating': float(item.find('span', class_='rating').text),
        'url': item.find('a')['href']
    }
    products.append(product)

# Store in SQLite
conn = sqlite3.connect('products.db')
cursor = conn.cursor()

cursor.execute('''
    CREATE TABLE IF NOT EXISTS products (
        id INTEGER PRIMARY KEY,
        name TEXT,
        price REAL,
        rating REAL,
        url TEXT
    )
''')

for p in products:
    cursor.execute('''
        INSERT INTO products (name, price, rating, url)
        VALUES (?, ?, ?, ?)
    ''', (p['name'], p['price'], p['rating'], p['url']))

conn.commit()
conn.close()

print(f'Scraped and stored {len(products)} products')
""")

# Step 4: Commit
git_commit("chore: update product database")

# User request: "Test our API endpoints and generate report"

# Step 1: Install testing framework
install_package("pytest requests pytest-html")

# Step 2: Create test file and run
run_python("""
import requests
import json
from datetime import datetime

BASE_URL = "https://api.example.com"
results = []

# Test 1: Health check
try:
    response = requests.get(f"{BASE_URL}/health")
    results.append({
        'test': 'Health Check',
        'status': response.status_code,
        'passed': response.status_code == 200,
        'response_time': response.elapsed.total_seconds()
    })
except Exception as e:
    results.append({
        'test': 'Health Check',
        'status': 'Error',
        'passed': False,
        'error': str(e)
    })

# Test 2: Authentication
try:
    headers = {'Authorization': 'Bearer test-token'}
    response = requests.get(f"{BASE_URL}/auth/validate", headers=headers)
    results.append({
        'test': 'Authentication',
        'status': response.status_code,
        'passed': response.status_code == 200,
        'response_time': response.elapsed.total_seconds()
    })
except Exception as e:
    results.append({
        'test': 'Authentication',
        'status': 'Error',
        'passed': False,
        'error': str(e)
    })

# Test 3: Data retrieval
try:
    response = requests.get(f"{BASE_URL}/data/users")
    data = response.json()
    results.append({
        'test': 'Data Retrieval',
        'status': response.status_code,
        'passed': response.status_code == 200 and len(data) > 0,
        'records': len(data) if response.status_code == 200 else 0,
        'response_time': response.elapsed.total_seconds()
    })
except Exception as e:
    results.append({
        'test': 'Data Retrieval',
        'status': 'Error',
        'passed': False,
        'error': str(e)
    })

# Generate report
report = {
    'timestamp': datetime.now().isoformat(),
    'total_tests': len(results),
    'passed': sum(1 for r in results if r.get('passed')),
    'failed': sum(1 for r in results if not r.get('passed')),
    'results': results
}

with open('api_test_report.json', 'w') as f:
    json.dump(report, f, indent=2)

print(f"Tests complete: {report['passed']}/{report['total_tests']} passed")
for r in results:
    status = '✓' if r.get('passed') else '✗'
    print(f"{status} {r['test']}")
""")

# Step 3: Check and commit
git_status()
git_commit("test: add API endpoint tests")

# Generate data, then create report
call_api("https://api.example.com/stats")
run_python("process_stats.py")
# Then use docx skill to create formatted report

# Fetch data, process with Python, export to Excel
fetch_url("https://data-source.com/raw.csv")
run_python("clean_and_transform.py")
# Then use xlsx skill to create formatted spreadsheet

# Generate charts and data visualizations
install_package("matplotlib seaborn")
run_python("generate_charts.py")
# Then use pptx skill to create presentation

run_python(code_string)

install_package("package_name")
install_package("package==1.0.0")
install_package("-r requirements.txt")

git_status()
git_commit("message")
git_commit("message", stage_all=True)

fetch_url(url, timeout=30)
call_api(url, method="GET", auth_token="token")