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Mike Morgan edited this page Jan 11, 2026 · 1 revision

Use Cases and Tutorials

Real-world implementation examples and complete code samples for Cortex Linux.

Table of Contents

  1. Automated Debugging
  2. DevOps Optimization
  3. Architecture Planning
  4. Code Review Automation
  5. System Monitoring and Analysis
  6. Configuration Optimization
  7. Documentation Generation

Automated Debugging

Scenario: Application Error Analysis

Automatically analyze error logs and provide actionable debugging suggestions.

Implementation

#!/usr/bin/env python3
"""
Automated error log analysis using Cortex AI
"""

from cortex import AI
import sys
import json
from pathlib import Path
from datetime import datetime

class ErrorAnalyzer:
    def __init__(self):
        self.ai = AI()
    
    def analyze_error_log(self, log_path: str) -> dict:
        """Analyze error log file and return structured analysis."""
        log_content = Path(log_path).read_text()
        
        query = f"""
        Analyze this error log and provide:
        1. Error classification (type, severity)
        2. Root cause analysis
        3. Suggested fixes with code examples
        4. Prevention strategies
        
        Error log:
        {log_content}
        """
        
        result = self.ai.reason(query, context={
            "application": "web_server",
            "language": "Python",
            "environment": "production"
        })
        
        return {
            "timestamp": datetime.now().isoformat(),
            "log_file": log_path,
            "analysis": result.result,
            "confidence": result.confidence
        }
    
    def analyze_stack_trace(self, stack_trace: str) -> dict:
        """Analyze Python stack trace."""
        query = f"""
        Analyze this Python stack trace:
        {stack_trace}
        
        Provide:
        - Error location and type
        - Call chain analysis
        - Suggested fixes
        - Related documentation links
        """
        
        result = self.ai.debug(
            error_log=stack_trace,
            context={"language": "Python"}
        )
        
        return {
            "error_type": result.analysis.get("error_type"),
            "location": result.analysis.get("location"),
            "suggestions": result.suggestions,
            "code_fixes": result.code_fixes
        }

def main():
    analyzer = ErrorAnalyzer()
    
    if len(sys.argv) < 2:
        print("Usage: error_analyzer.py <log_file>")
        sys.exit(1)
    
    log_file = sys.argv[1]
    
    print(f"Analyzing {log_file}...")
    analysis = analyzer.analyze_error_log(log_file)
    
    print("\n" + "="*80)
    print("ERROR ANALYSIS")
    print("="*80)
    print(f"File: {analysis['log_file']}")
    print(f"Timestamp: {analysis['timestamp']}")
    print(f"Confidence: {analysis['confidence']:.2%}")
    print("\nAnalysis:")
    print(analysis['analysis'])
    
    # Save to file
    output_file = f"{log_file}.analysis.json"
    with open(output_file, 'w') as f:
        json.dump(analysis, f, indent=2)
    print(f"\nAnalysis saved to: {output_file}")

if __name__ == "__main__":
    main()

Bash Implementation

#!/bin/bash
# Automated error log analyzer

LOG_FILE="${1:-/var/log/app/error.log}"
OUTPUT_DIR="/var/log/app/analysis"

mkdir -p "$OUTPUT_DIR"

# Analyze error log
ANALYSIS=$(cortex-ai debug "$LOG_FILE" --format json)

# Extract key information
ERROR_TYPE=$(echo "$ANALYSIS" | jq -r '.analysis.error_type')
SEVERITY=$(echo "$ANALYSIS" | jq -r '.analysis.severity')
SUGGESTIONS=$(echo "$ANALYSIS" | jq -r '.suggestions[]')

# Generate report
REPORT_FILE="$OUTPUT_DIR/error_analysis_$(date +%Y%m%d_%H%M%S).md"

cat > "$REPORT_FILE" << EOF
# Error Analysis Report

**Timestamp**: $(date)
**Log File**: $LOG_FILE
**Error Type**: $ERROR_TYPE
**Severity**: $SEVERITY

## Suggested Fixes

$SUGGESTIONS

## Full Analysis

\`\`\`
$(echo "$ANALYSIS" | jq -r '.analysis')
\`\`\`
EOF

echo "Analysis complete: $REPORT_FILE"

Integration with Monitoring

# Integration with monitoring system
import subprocess
from cortex import AI

def monitor_and_debug():
    """Monitor application and auto-debug on errors."""
    ai = AI()
    
    # Watch log file
    process = subprocess.Popen(
        ['tail', '-f', '/var/log/app/error.log'],
        stdout=subprocess.PIPE,
        stderr=subprocess.PIPE
    )
    
    error_buffer = []
    
    for line in process.stdout:
        line = line.decode('utf-8')
        
        # Detect error lines
        if 'ERROR' in line or 'Exception' in line:
            error_buffer.append(line)
            
            # Analyze when buffer reaches threshold
            if len(error_buffer) >= 10:
                error_text = '\n'.join(error_buffer)
                
                result = ai.debug(
                    error_log=error_text,
                    context={"application": "monitored_app"}
                )
                
                # Send alert
                send_alert(result)
                
                error_buffer = []

DevOps Optimization

Scenario: Infrastructure Configuration Optimization

Automatically analyze and optimize infrastructure configurations (Ansible, Terraform, Kubernetes).

Ansible Playbook Optimization

#!/usr/bin/env python3
"""
Optimize Ansible playbooks using Cortex AI
"""

from cortex import AI
from pathlib import Path
import yaml

class AnsibleOptimizer:
    def __init__(self):
        self.ai = AI()
    
    def optimize_playbook(self, playbook_path: str) -> dict:
        """Analyze and optimize Ansible playbook."""
        playbook_content = Path(playbook_path).read_text()
        
        result = self.ai.optimize(
            target="ansible_playbook",
            content=playbook_content,
            goals=["performance", "maintainability", "best_practices"]
        )
        
        return {
            "original": playbook_content,
            "optimizations": result.optimizations,
            "improved_playbook": self._apply_optimizations(
                playbook_content,
                result.optimizations
            )
        }
    
    def _apply_optimizations(self, original: str, optimizations: list) -> str:
        """Apply suggested optimizations to playbook."""
        # Implementation would apply code changes
        # This is a simplified version
        optimized = original
        for opt in optimizations:
            if 'optimized_code' in opt:
                # Apply optimization
                pass
        return optimized

# Example usage
optimizer = AnsibleOptimizer()
result = optimizer.optimize_playbook("deploy.yml")

print("Optimizations suggested:")
for opt in result['optimizations']:
    print(f"- {opt['description']}")
    print(f"  Improvement: {opt['improvement_estimate']}")

Terraform Configuration Analysis

#!/bin/bash
# Terraform configuration optimizer

TERRAFORM_DIR="${1:-.}"

# Analyze Terraform files
for tf_file in $(find "$TERRAFORM_DIR" -name "*.tf"); do
    echo "Analyzing $tf_file..."
    
    ANALYSIS=$(cortex-ai optimize "$tf_file" \
        --target terraform \
        --goals "cost_optimization,security,best_practices" \
        --format json)
    
    # Extract recommendations
    RECS=$(echo "$ANALYSIS" | jq -r '.optimizations[] | "\(.type): \(.description)"')
    
    echo "Recommendations for $tf_file:"
    echo "$RECS"
    echo ""
done

# Generate optimization report
cortex-ai plan "Optimize Terraform infrastructure" \
    --requirements "Reduce costs, improve security, follow best practices" \
    --config "$TERRAFORM_DIR" \
    --output terraform_optimization_plan.md

Kubernetes Resource Optimization

#!/usr/bin/env python3
"""
Kubernetes resource optimization
"""

from cortex import AI
import yaml
import json

class K8sOptimizer:
    def __init__(self):
        self.ai = AI()
    
    def optimize_deployment(self, deployment_yaml: str) -> dict:
        """Optimize Kubernetes deployment."""
        deployment = yaml.safe_load(deployment_yaml)
        
        # Extract resource requests/limits
        resources = self._extract_resources(deployment)
        
        query = f"""
        Analyze this Kubernetes deployment and optimize:
        1. Resource requests and limits
        2. Replica count
        3. Health checks
        4. Security contexts
        
        Current configuration:
        {json.dumps(resources, indent=2)}
        """
        
        result = self.ai.optimize(
            target="kubernetes_deployment",
            content=deployment_yaml,
            goals=["resource_efficiency", "high_availability", "security"]
        )
        
        return {
            "current": deployment,
            "recommendations": result.optimizations,
            "optimized": yaml.safe_load(result.optimizations[0]['optimized_code'])
        }
    
    def _extract_resources(self, deployment: dict) -> dict:
        """Extract resource configuration."""
        containers = deployment.get('spec', {}).get('template', {}).get('spec', {}).get('containers', [])
        resources = {}
        for container in containers:
            resources[container['name']] = container.get('resources', {})
        return resources

# Example
optimizer = K8sOptimizer()
with open('deployment.yaml') as f:
    deployment_yaml = f.read()

result = optimizer.optimize_deployment(deployment_yaml)
print("Optimization recommendations:")
for rec in result['recommendations']:
    print(f"- {rec['description']}")

Architecture Planning

Scenario: System Design Assistance

Use Cortex AI to assist in system architecture design and technology selection.

Architecture Planning Tool

#!/usr/bin/env python3
"""
Architecture planning assistant
"""

from cortex import AI
import json
from typing import Dict, List

class ArchitecturePlanner:
    def __init__(self):
        self.ai = AI()
    
    def design_system(self, requirements: Dict) -> Dict:
        """Design system architecture based on requirements."""
        query = f"""
        Design a system architecture based on these requirements:
        
        Functional Requirements:
        {requirements.get('functional', [])}
        
        Non-Functional Requirements:
        {requirements.get('non_functional', {})}
        
        Constraints:
        {requirements.get('constraints', [])}
        
        Provide:
        1. High-level architecture diagram (textual)
        2. Technology stack recommendations
        3. Component breakdown
        4. Data flow
        5. Security considerations
        6. Scalability strategy
        """
        
        result = self.ai.plan(
            task="Design system architecture",
            requirements=json.dumps(requirements),
            format="markdown"
        )
        
        return {
            "architecture": result.plan,
            "estimated_complexity": result.risk_level,
            "confidence": result.confidence
        }
    
    def compare_technologies(self, technologies: List[str], use_case: str) -> Dict:
        """Compare technology options for a use case."""
        query = f"""
        Compare these technologies for the use case: {use_case}
        
        Technologies: {', '.join(technologies)}
        
        Provide comparison on:
        1. Performance
        2. Scalability
        3. Ease of use
        4. Community support
        5. Cost
        6. Best fit recommendation
        """
        
        result = self.ai.reason(query)
        
        return {
            "comparison": result.result,
            "recommendation": self._extract_recommendation(result.result)
        }
    
    def _extract_recommendation(self, comparison_text: str) -> str:
        """Extract recommendation from comparison text."""
        # Simple extraction - could be improved with NLP
        if "recommend" in comparison_text.lower():
            # Extract recommendation
            pass
        return "See comparison above"

# Example usage
planner = ArchitecturePlanner()

requirements = {
    "functional": [
        "User authentication and authorization",
        "Real-time data processing",
        "RESTful API",
        "Web dashboard"
    ],
    "non_functional": {
        "scalability": "Handle 1M+ users",
        "availability": "99.9% uptime",
        "latency": "< 100ms API response"
    },
    "constraints": [
        "Must use open-source technologies",
        "Budget: $10k/month infrastructure",
        "Team size: 5 developers"
    ]
}

architecture = planner.design_system(requirements)
print(architecture['architecture'])

# Technology comparison
tech_comparison = planner.compare_technologies(
    ["PostgreSQL", "MongoDB", "Cassandra"],
    "Storing user profiles and session data"
)
print(tech_comparison['comparison'])

Complete Example: Microservices Architecture

#!/bin/bash
# Generate microservices architecture plan

REQUIREMENTS_FILE="requirements.json"

# Create requirements
cat > "$REQUIREMENTS_FILE" << EOF
{
  "application_type": "e-commerce platform",
  "expected_users": "100000",
  "features": [
    "User management",
    "Product catalog",
    "Shopping cart",
    "Payment processing",
    "Order management"
  ],
  "constraints": {
    "budget": "$5000/month",
    "team_size": 8,
    "timeline": "6 months"
  }
}
EOF

# Generate architecture plan
cortex-ai plan "Design microservices architecture for e-commerce platform" \
    --requirements "$REQUIREMENTS_FILE" \
    --format markdown \
    --output architecture_plan.md

# Generate technology recommendations
cortex-ai reason "Recommend technology stack for microservices e-commerce platform with 100k users, $5k/month budget" \
    --output tech_stack.md

echo "Architecture plan generated: architecture_plan.md"
echo "Technology recommendations: tech_stack.md"

Code Review Automation

Scenario: Automated Code Review

Automatically review code changes and provide feedback.

Git Hook Integration

#!/usr/bin/env python3
"""
Pre-commit hook for automated code review
"""

from cortex import AI
import subprocess
import sys
from pathlib import Path

class CodeReviewer:
    def __init__(self):
        self.ai = AI()
    
    def review_diff(self, diff: str) -> dict:
        """Review git diff and provide feedback."""
        query = f"""
        Review this code diff and provide feedback on:
        1. Code quality and style
        2. Potential bugs
        3. Security issues
        4. Performance concerns
        5. Best practices
        
        Diff:
        {diff}
        """
        
        result = self.ai.reason(query, context={
            "review_type": "code_review",
            "severity": "critical"
        })
        
        return {
            "review": result.result,
            "confidence": result.confidence,
            "should_block": self._should_block_commit(result.result)
        }
    
    def _should_block_commit(self, review_text: str) -> bool:
        """Determine if commit should be blocked."""
        blocking_keywords = [
            "security vulnerability",
            "critical bug",
            "data leak",
            "SQL injection",
            "XSS vulnerability"
        ]
        
        review_lower = review_text.lower()
        return any(keyword in review_lower for keyword in blocking_keywords)

def main():
    reviewer = CodeReviewer()
    
    # Get staged changes
    result = subprocess.run(
        ['git', 'diff', '--cached'],
        capture_output=True,
        text=True
    )
    
    if not result.stdout:
        print("No changes to review")
        sys.exit(0)
    
    # Review changes
    review = reviewer.review_diff(result.stdout)
    
    print("="*80)
    print("CODE REVIEW")
    print("="*80)
    print(review['review'])
    print("="*80)
    
    if review['should_block']:
        print("\n⚠️  BLOCKING ISSUES DETECTED")
        print("Please address the issues above before committing.")
        sys.exit(1)
    else:
        print("\n✓ Review complete. No blocking issues.")
        sys.exit(0)

if __name__ == "__main__":
    main()

Setup Git Hook

#!/bin/bash
# Setup pre-commit hook

HOOK_FILE=".git/hooks/pre-commit"

cat > "$HOOK_FILE" << 'EOF'
#!/bin/bash
python3 /path/to/code_reviewer.py
EOF

chmod +x "$HOOK_FILE"
echo "Pre-commit hook installed"

Pull Request Review

#!/usr/bin/env python3
"""
Automated PR review using Cortex AI
"""

from cortex import AI
import requests
import os

class PRReviewer:
    def __init__(self):
        self.ai = AI()
        self.github_token = os.getenv('GITHUB_TOKEN')
    
    def review_pr(self, repo: str, pr_number: int):
        """Review GitHub pull request."""
        # Fetch PR diff
        diff = self._fetch_pr_diff(repo, pr_number)
        
        # Review code
        review = self.ai.reason(
            f"Review this pull request diff:\n\n{diff}\n\n"
            "Provide feedback on code quality, bugs, security, and best practices."
        )
        
        # Post review comment
        self._post_review_comment(repo, pr_number, review.result)
    
    def _fetch_pr_diff(self, repo: str, pr_number: int) -> str:
        """Fetch PR diff from GitHub."""
        url = f"https://api.github.com/repos/{repo}/pulls/{pr_number}"
        headers = {"Authorization": f"token {self.github_token}"}
        
        response = requests.get(f"{url}.diff", headers=headers)
        return response.text
    
    def _post_review_comment(self, repo: str, pr_number: int, comment: str):
        """Post review comment to PR."""
        url = f"https://api.github.com/repos/{repo}/pulls/{pr_number}/comments"
        headers = {
            "Authorization": f"token {self.github_token}",
            "Accept": "application/vnd.github.v3+json"
        }
        
        # Post as review comment
        # Implementation details...

System Monitoring and Analysis

Scenario: Automated System Health Analysis

Continuously monitor system metrics and provide intelligent analysis.

System Metrics Analyzer

#!/usr/bin/env python3
"""
System metrics monitoring and analysis
"""

from cortex import AI
import psutil
import json
from datetime import datetime
from typing import Dict

class SystemAnalyzer:
    def __init__(self):
        self.ai = AI()
    
    def collect_metrics(self) -> Dict:
        """Collect system metrics."""
        return {
            "timestamp": datetime.now().isoformat(),
            "cpu": {
                "percent": psutil.cpu_percent(interval=1),
                "count": psutil.cpu_count(),
                "per_cpu": psutil.cpu_percent(percpu=True, interval=1)
            },
            "memory": {
                "total": psutil.virtual_memory().total,
                "available": psutil.virtual_memory().available,
                "percent": psutil.virtual_memory().percent,
                "used": psutil.virtual_memory().used
            },
            "disk": {
                "total": psutil.disk_usage('/').total,
                "used": psutil.disk_usage('/').used,
                "percent": psutil.disk_usage('/').percent
            },
            "network": {
                "bytes_sent": psutil.net_io_counters().bytes_sent,
                "bytes_recv": psutil.net_io_counters().bytes_recv
            }
        }
    
    def analyze_metrics(self, metrics: Dict) -> Dict:
        """Analyze system metrics and provide recommendations."""
        metrics_json = json.dumps(metrics, indent=2)
        
        query = f"""
        Analyze these system metrics and provide:
        1. Health assessment
        2. Performance issues
        3. Resource bottlenecks
        4. Optimization recommendations
        5. Alerts for critical issues
        
        Metrics:
        {metrics_json}
        """
        
        result = self.ai.reason(query)
        
        return {
            "metrics": metrics,
            "analysis": result.result,
            "confidence": result.confidence,
            "alerts": self._extract_alerts(result.result)
        }
    
    def _extract_alerts(self, analysis: str) -> list:
        """Extract alert-worthy issues from analysis."""
        alerts = []
        # Simple keyword-based extraction
        if "critical" in analysis.lower() or "urgent" in analysis.lower():
            alerts.append("Critical issue detected")
        return alerts

# Continuous monitoring
analyzer = SystemAnalyzer()

while True:
    metrics = analyzer.collect_metrics()
    analysis = analyzer.analyze_metrics(metrics)
    
    print(f"[{analysis['metrics']['timestamp']}] System Analysis:")
    print(analysis['analysis'])
    
    if analysis['alerts']:
        print("⚠️  ALERTS:", analysis['alerts'])
    
    time.sleep(300)  # Analyze every 5 minutes

Configuration Optimization

Scenario: Nginx Configuration Optimization

#!/bin/bash
# Nginx configuration optimizer

NGINX_CONFIG="/etc/nginx/nginx.conf"
BACKUP_DIR="/etc/nginx/backups"

# Create backup
mkdir -p "$BACKUP_DIR"
cp "$NGINX_CONFIG" "$BACKUP_DIR/nginx.conf.$(date +%Y%m%d_%H%M%S)"

# Analyze and optimize
OPTIMIZATION=$(cortex-ai optimize "$NGINX_CONFIG" \
    --target nginx \
    --goals "performance,security" \
    --format json)

# Extract optimized configuration
OPTIMIZED_CONFIG=$(echo "$OPTIMIZATION" | jq -r '.optimizations[0].optimized_code')

# Save optimized config
echo "$OPTIMIZED_CONFIG" > "$NGINX_CONFIG.optimized"

# Test configuration
if nginx -t -c "$NGINX_CONFIG.optimized"; then
    echo "Optimized configuration is valid"
    echo "Review changes: diff $NGINX_CONFIG $NGINX_CONFIG.optimized"
    echo "Apply with: cp $NGINX_CONFIG.optimized $NGINX_CONFIG && systemctl reload nginx"
else
    echo "Optimized configuration has errors"
    echo "Keeping original configuration"
fi

Documentation Generation

Scenario: Auto-generate API Documentation

#!/usr/bin/env python3
"""
Generate API documentation from code
"""

from cortex import AI
import ast
import inspect

class DocGenerator:
    def __init__(self):
        self.ai = AI()
    
    def generate_docstring(self, function_code: str) -> str:
        """Generate docstring for a function."""
        query = f"""
        Generate a comprehensive docstring for this Python function following Google style:
        
        {function_code}
        
        Include:
        - Description
        - Args with types
        - Returns with type
        - Raises
        - Examples
        """
        
        result = self.ai.reason(query)
        return result.result
    
    def generate_api_docs(self, module_path: str) -> str:
        """Generate complete API documentation for a module."""
        # Parse module and extract functions/classes
        # Generate documentation for each
        # Format as Markdown
        
        # Implementation...
        pass

# Example
generator = DocGenerator()

function_code = """
def process_user_data(user_id: int, data: dict) -> bool:
    # Process user data
    return True
"""

docstring = generator.generate_docstring(function_code)
print(docstring)

Next Steps

Last updated: 2024

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