OpenClaw+ 🚀

A modular super-skill that combines essential developer tools and web capabilities into a unified, powerful workflow.

Overview

OpenClaw+ integrates seven core capabilities into one streamlined skill:

Developer Skills:

• run_python - Execute Python code with proper environment management
• git_status - Check repository status and track changes
• git_commit - Commit changes with meaningful messages
• install_package - Install Python packages with dependency handling

Web Skills:

• fetch_url - Retrieve web content with robust error handling
• call_api - Make API requests with authentication and response parsing

This modular design allows you to chain operations efficiently - install packages, run code, fetch data, commit results - all in one cohesive workflow.

---

When to Use OpenClaw+

Use this skill when the user's request involves:

• Running Python scripts or code snippets
• Installing Python packages (pip, conda, system packages)
• Checking git repository status
• Committing code changes
• Fetching content from URLs
• Making API calls (REST, GraphQL, etc.)
• Combining any of the above in a workflow

Common patterns:

• "Install pandas and run this analysis"
• "Fetch data from this API and save it"
• "Check git status and commit my changes"
• "Run this script and call this endpoint"
• "Install these packages, run the code, then commit"

---

Core Capabilities

1. Python Execution (run_python)

Execute Python code with proper environment management and output capture.

Key features:

• Captures stdout, stderr, and return values
• Handles exceptions gracefully
• Supports multi-line scripts
• Access to installed packages
• Environment variable support

Usage patterns:

# Simple execution
result = run_python("print('Hello, world!')")

# With installed packages
run_python("""
import pandas as pd
import numpy as np

data = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]})
print(data.describe())
""")

# File operations
run_python("""
with open('output.txt', 'w') as f:
    f.write('Results: ...')
""")

Best practices:

• Always check for syntax errors before execution
• Handle file paths carefully (use absolute paths when needed)
• Capture exceptions and provide clear error messages
• For large scripts, consider creating a .py file first

---

2. Package Installation (install_package)

Install Python packages with intelligent dependency resolution.

Key features:

• Pip package installation
• System package support (apt, brew, etc.)
• Conda environment support
• Dependency conflict detection
• Version pinning

Usage patterns:

# Install single package
install_package("pandas")

# Install specific version
install_package("numpy==1.24.0")

# Install multiple packages
install_package("requests beautifulsoup4 lxml")

# Install from requirements.txt
install_package("-r requirements.txt")

# System packages (when needed)
install_package("libpq-dev", system=True)

Best practices:

• Always use --break-system-packages flag for pip in this environment
• Check if package is already installed before installing
• Handle version conflicts explicitly
• Provide clear feedback on installation success/failure

Implementation:

pip install <package> --break-system-packages

---

3. Git Status (git_status)

Check repository status and track changes.

Key features:

• Shows modified, added, deleted files
• Displays untracked files
• Shows current branch
• Indicates if ahead/behind remote
• Supports custom git directories

Usage patterns:

# Check current directory
git_status()

# Check specific directory
git_status("/path/to/repo")

# Parse output for automation
status = git_status()
if "modified:" in status:
    print("Changes detected")

Best practices:

• Always check status before committing
• Parse output to detect specific changes
• Handle cases where directory isn't a git repo
• Provide context about what changed

Implementation:

git status
git diff --stat
git log -1 --oneline

---

4. Git Commit (git_commit)

Commit changes with meaningful messages following best practices.

Key features:

• Conventional commit format support
• Multi-line commit messages
• Automatic staging option
• Commit message validation
• Amend support

Usage patterns:

# Simple commit
git_commit("Add new feature")

# Conventional commit
git_commit("feat: add user authentication")

# Multi-line with description
git_commit("""
feat: add data processing pipeline

- Implement CSV reader
- Add data validation
- Create output formatter
""")

# Stage and commit
git_commit("fix: resolve parsing error", stage_all=True)

Best practices:

• Use conventional commit format: type(scope): description
• Types: feat, fix, docs, style, refactor, test, chore
• Keep first line under 50 characters
• Add detailed description if needed
• Reference issue numbers when applicable

Implementation:

git add <files>  # if stage_all
git commit -m "<message>"
git log -1 --oneline  # confirm commit

---

5. URL Fetching (fetch_url)

Retrieve content from URLs with robust error handling.

Key features:

• HTTP/HTTPS support
• Custom headers
• Authentication support
• Redirect following
• Timeout handling
• Response parsing (JSON, XML, HTML, text)

Usage patterns:

# Fetch HTML
html = fetch_url("https://example.com")

# Fetch JSON
data = fetch_url("https://api.example.com/data", 
                 parse_json=True)

# With authentication
content = fetch_url("https://api.example.com/protected",
                    headers={"Authorization": "Bearer TOKEN"})

# With custom timeout
content = fetch_url("https://slow-site.com", timeout=30)

# POST request
response = fetch_url("https://api.example.com/submit",
                     method="POST",
                     data={"key": "value"})

Best practices:

• Always handle network errors gracefully
• Set appropriate timeouts
• Validate URLs before fetching
• Parse response based on content type
• Handle rate limiting
• Respect robots.txt

Implementation:

import requests

response = requests.get(url, headers=headers, timeout=timeout)
response.raise_for_status()
return response.text  # or response.json()

---

6. API Calls (call_api)

Make API requests with authentication and response parsing.

Key features:

• REST API support
• GraphQL support
• Authentication (Bearer, Basic, API Key)
• Request/response logging
• Error handling with retries
• Response validation

Usage patterns:

# Simple GET request
data = call_api("https://api.example.com/users")

# With authentication
data = call_api("https://api.example.com/data",
                auth_token="your-token")

# POST with JSON body
result = call_api("https://api.example.com/create",
                  method="POST",
                  json_data={"name": "John", "age": 30})

# With custom headers
data = call_api("https://api.example.com/endpoint",
                headers={"X-Custom-Header": "value"})

# GraphQL query
result = call_api("https://api.example.com/graphql",
                  method="POST",
                  json_data={
                      "query": "{ users { id name } }"
                  })

Best practices:

• Validate API keys/tokens before use
• Handle rate limits with exponential backoff
• Parse response format (JSON, XML, etc.)
• Log requests for debugging
• Handle pagination for large datasets
• Validate response schemas
• Use appropriate HTTP methods (GET, POST, PUT, DELETE, PATCH)

Implementation:

import requests

headers = {"Authorization": f"Bearer {token}"}
response = requests.request(
    method=method,
    url=url,
    headers=headers,
    json=json_data,
    timeout=30
)
response.raise_for_status()
return response.json()

---

Workflow Patterns

OpenClaw+ shines when combining multiple capabilities:

Pattern 1: Data Pipeline

# 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")

Pattern 2: Web Scraping & Analysis

# 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")

Pattern 3: API Integration Testing

# 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")

Pattern 4: Automated Reporting

# 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")

---

Error Handling

Each capability includes robust error handling:

Python Execution Errors

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

Package Installation Errors

# 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}")

Git Operation Errors

# 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")

Network Errors

# 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}")

---

Best Practices

1. Environment Management

• Always use --break-system-packages for pip
• Check if packages are installed before installing
• Use virtual environments when appropriate
• Document package versions

2. Git Operations

• Check status before committing
• Use meaningful commit messages
• Follow conventional commit format
• Stage only relevant files

3. Code Execution

• Validate syntax before running
• Handle exceptions gracefully
• Capture and log output
• Clean up temporary files

4. API/Web Requests

• Set appropriate timeouts
• Handle rate limiting
• Validate responses
• Log requests for debugging
• Respect API usage limits

5. Workflow Composition

• Chain operations logically
• Handle errors at each step
• Provide progress feedback
• Document dependencies

---

Security Considerations

API Keys & Credentials

• Never hardcode credentials
• Use environment variables
• Validate before use
• Rotate regularly

Code Execution

• Validate input code
• Sandbox when possible
• Limit resource usage
• Monitor execution

Web Requests

• Validate URLs
• Use HTTPS when possible
• Handle redirects carefully
• Respect robots.txt

---

Debugging & Troubleshooting

Common Issues

Python execution fails:

• Check syntax with python -m py_compile script.py
• Verify packages are installed
• Check file paths
• Review error messages

Package installation fails:

• Ensure pip is up to date
• Check internet connectivity
• Verify package name
• Review dependencies

Git operations fail:

• Verify it's a git repository
• Check file permissions
• Ensure clean working directory
• Review git configuration

API/URL requests fail:

• Verify URL is correct
• Check authentication
• Review rate limits
• Check network connectivity

---

Examples

Example 1: Complete Data Pipeline

# 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
""")

Example 2: Web Scraping & Storage

# 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")

Example 3: API Testing Suite

# 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")

---

Integration with Other Skills

OpenClaw+ works seamlessly with other skills:

With docx skill:

# 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

With xlsx skill:

# 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

With pptx skill:

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

---

Quick Reference

Python Execution

run_python(code_string)

Package Management

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

Git Operations

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

Web Requests

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

---

Conclusion

OpenClaw+ provides a unified, powerful toolkit for development and web automation workflows. By combining Python execution, package management, git operations, and web capabilities, it enables complex multi-step workflows with a single cohesive skill.

Key strengths:

• ✅ Modular design - use only what you need
• ✅ Error handling - robust failure recovery
• ✅ Workflow composition - chain operations easily
• ✅ Production-ready - follows best practices
• ✅ Well-documented - clear examples and patterns

Use OpenClaw+ whenever your task involves code execution, package management, version control, or web interactions - or any combination thereof!