Senior Devops
- 1.2k installs
- 29.9k repo stars
- Updated July 27, 2026
- davila7/claude-code-templates
senior-devops provides documented workflows for Comprehensive DevOps skill for CI/CD, infrastructure automation, containerization, and cloud platforms (AWS, GCP, Azure). Includes pipeline setup, infrastructur
About
The senior-devops skill comprehensive DevOps skill for CI/CD, infrastructure automation, containerization, and cloud platforms (AWS, GCP, Azure). Includes pipeline setup, infrastructure as code, deployment automation, and monitoring. Use when setting up pipelines, deploying applications, managing infrastructure, implementing monitoring, or optimizing deployment processes. # Senior Devops Complete toolkit for senior devops with modern tools and best practices. ## Quick Start ### Main Capabilities This skill provides three core capabilities through automated scripts: ```bash # Script 1: Pipeline Generator python scripts/pipeline_generator.py [options] # Script 2: Terraform Scaffolder python scripts/terraform_scaffolder.py [options] # Script 3: Deployment Manager python scripts/deployment_manager.py [options] ``` ## Core Capabilities ### 1. Pipeline Generator Automated tool for pipeline generator tasks. **Features:** - Automated scaffolding - Best practices built-in - Configurable templates - Quality checks **Usage:** ```bash python scripts/pipeline_generator.py <project-path> [options] ``` ### 2. Terraform Scaffolder Comprehensive analysis and optimization tool. **Features:** - Deep.
- Automated scaffolding
- Best practices built-in
- Configurable templates
- Performance metrics
- Expert-level automation
Senior Devops by the numbers
- 1,165 all-time installs (skills.sh)
- +24 installs in the week ending Jul 28, 2026 (Skillselion tracking)
- Ranked #210 of 1,048 Mobile Development skills by installs in the Skillselion catalog
- Security screen: LOW risk (skills.sh audit)
- Data as of Jul 28, 2026 (Skillselion catalog sync)
senior-devops capabilities & compatibility
- Capabilities
- automated scaffolding · best practices built in · configurable templates · performance metrics · expert level automation
- Use cases
- documentation
What senior-devops says it does
# Senior Devops Complete toolkit for senior devops with modern tools and best practices.
Pipeline Generator Automated tool for pipeline generator tasks.
npx skills add https://github.com/davila7/claude-code-templates --skill senior-devopsAdd your badge
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| Installs | 1.2k |
|---|---|
| repo stars | ★ 29.9k |
| Security audit | 3 / 3 scanners passed |
| Last updated | July 27, 2026 |
| Repository | davila7/claude-code-templates ↗ |
How do I use senior-devops for the task described in its SKILL.md triggers?
Comprehensive DevOps skill for CI/CD, infrastructure automation, containerization, and cloud platforms (AWS, GCP, Azure). Includes pipeline setup, infrastructure as code, deployment automation, and m.
Who is it for?
Teams invoking senior-devops when the user request matches documented triggers and prerequisites.
Skip if: Skip when cached docs are missing, the request is a negative trigger, or another sibling skill owns the workflow.
When should I use this skill?
Comprehensive DevOps skill for CI/CD, infrastructure automation, containerization, and cloud platforms (AWS, GCP, Azure). Includes pipeline setup, infrastructure as code, deployment automation, and monitoring. Use when s
What you get
Step-by-step guidance grounded in senior-devops documentation and reference files.
- Pipeline pattern recommendations
- Security and anti-pattern checklist
Files
Senior Devops
Complete toolkit for senior devops with modern tools and best practices.
Quick Start
Main Capabilities
This skill provides three core capabilities through automated scripts:
# Script 1: Pipeline Generator
python scripts/pipeline_generator.py [options]
# Script 2: Terraform Scaffolder
python scripts/terraform_scaffolder.py [options]
# Script 3: Deployment Manager
python scripts/deployment_manager.py [options]Core Capabilities
1. Pipeline Generator
Automated tool for pipeline generator tasks.
Features:
- Automated scaffolding
- Best practices built-in
- Configurable templates
- Quality checks
Usage:
python scripts/pipeline_generator.py <project-path> [options]2. Terraform Scaffolder
Comprehensive analysis and optimization tool.
Features:
- Deep analysis
- Performance metrics
- Recommendations
- Automated fixes
Usage:
python scripts/terraform_scaffolder.py <target-path> [--verbose]3. Deployment Manager
Advanced tooling for specialized tasks.
Features:
- Expert-level automation
- Custom configurations
- Integration ready
- Production-grade output
Usage:
python scripts/deployment_manager.py [arguments] [options]Reference Documentation
Cicd Pipeline Guide
Comprehensive guide available in references/cicd_pipeline_guide.md:
- Detailed patterns and practices
- Code examples
- Best practices
- Anti-patterns to avoid
- Real-world scenarios
Infrastructure As Code
Complete workflow documentation in references/infrastructure_as_code.md:
- Step-by-step processes
- Optimization strategies
- Tool integrations
- Performance tuning
- Troubleshooting guide
Deployment Strategies
Technical reference guide in references/deployment_strategies.md:
- Technology stack details
- Configuration examples
- Integration patterns
- Security considerations
- Scalability guidelines
Tech Stack
Languages: TypeScript, JavaScript, Python, Go, Swift, Kotlin Frontend: React, Next.js, React Native, Flutter Backend: Node.js, Express, GraphQL, REST APIs Database: PostgreSQL, Prisma, NeonDB, Supabase DevOps: Docker, Kubernetes, Terraform, GitHub Actions, CircleCI Cloud: AWS, GCP, Azure
Development Workflow
1. Setup and Configuration
# Install dependencies
npm install
# or
pip install -r requirements.txt
# Configure environment
cp .env.example .env2. Run Quality Checks
# Use the analyzer script
python scripts/terraform_scaffolder.py .
# Review recommendations
# Apply fixes3. Implement Best Practices
Follow the patterns and practices documented in:
references/cicd_pipeline_guide.mdreferences/infrastructure_as_code.mdreferences/deployment_strategies.md
Best Practices Summary
Code Quality
- Follow established patterns
- Write comprehensive tests
- Document decisions
- Review regularly
Performance
- Measure before optimizing
- Use appropriate caching
- Optimize critical paths
- Monitor in production
Security
- Validate all inputs
- Use parameterized queries
- Implement proper authentication
- Keep dependencies updated
Maintainability
- Write clear code
- Use consistent naming
- Add helpful comments
- Keep it simple
Common Commands
# Development
npm run dev
npm run build
npm run test
npm run lint
# Analysis
python scripts/terraform_scaffolder.py .
python scripts/deployment_manager.py --analyze
# Deployment
docker build -t app:latest .
docker-compose up -d
kubectl apply -f k8s/Troubleshooting
Common Issues
Check the comprehensive troubleshooting section in references/deployment_strategies.md.
Getting Help
- Review reference documentation
- Check script output messages
- Consult tech stack documentation
- Review error logs
Resources
- Pattern Reference:
references/cicd_pipeline_guide.md - Workflow Guide:
references/infrastructure_as_code.md - Technical Guide:
references/deployment_strategies.md - Tool Scripts:
scripts/directory
Cicd Pipeline Guide
Overview
This reference guide provides comprehensive information for senior devops.
Patterns and Practices
Pattern 1: Best Practice Implementation
Description: Detailed explanation of the pattern.
When to Use:
- Scenario 1
- Scenario 2
- Scenario 3
Implementation:
// Example code implementation
export class Example {
// Implementation details
}Benefits:
- Benefit 1
- Benefit 2
- Benefit 3
Trade-offs:
- Consider 1
- Consider 2
- Consider 3
Pattern 2: Advanced Technique
Description: Another important pattern for senior devops.
Implementation:
// Advanced example
async function advancedExample() {
// Code here
}Guidelines
Code Organization
- Clear structure
- Logical separation
- Consistent naming
- Proper documentation
Performance Considerations
- Optimization strategies
- Bottleneck identification
- Monitoring approaches
- Scaling techniques
Security Best Practices
- Input validation
- Authentication
- Authorization
- Data protection
Common Patterns
Pattern A
Implementation details and examples.
Pattern B
Implementation details and examples.
Pattern C
Implementation details and examples.
Anti-Patterns to Avoid
Anti-Pattern 1
What not to do and why.
Anti-Pattern 2
What not to do and why.
Tools and Resources
Recommended Tools
- Tool 1: Purpose
- Tool 2: Purpose
- Tool 3: Purpose
Further Reading
- Resource 1
- Resource 2
- Resource 3
Conclusion
Key takeaways for using this reference guide effectively.
Deployment Strategies
Overview
This reference guide provides comprehensive information for senior devops.
Patterns and Practices
Pattern 1: Best Practice Implementation
Description: Detailed explanation of the pattern.
When to Use:
- Scenario 1
- Scenario 2
- Scenario 3
Implementation:
// Example code implementation
export class Example {
// Implementation details
}Benefits:
- Benefit 1
- Benefit 2
- Benefit 3
Trade-offs:
- Consider 1
- Consider 2
- Consider 3
Pattern 2: Advanced Technique
Description: Another important pattern for senior devops.
Implementation:
// Advanced example
async function advancedExample() {
// Code here
}Guidelines
Code Organization
- Clear structure
- Logical separation
- Consistent naming
- Proper documentation
Performance Considerations
- Optimization strategies
- Bottleneck identification
- Monitoring approaches
- Scaling techniques
Security Best Practices
- Input validation
- Authentication
- Authorization
- Data protection
Common Patterns
Pattern A
Implementation details and examples.
Pattern B
Implementation details and examples.
Pattern C
Implementation details and examples.
Anti-Patterns to Avoid
Anti-Pattern 1
What not to do and why.
Anti-Pattern 2
What not to do and why.
Tools and Resources
Recommended Tools
- Tool 1: Purpose
- Tool 2: Purpose
- Tool 3: Purpose
Further Reading
- Resource 1
- Resource 2
- Resource 3
Conclusion
Key takeaways for using this reference guide effectively.
Infrastructure As Code
Overview
This reference guide provides comprehensive information for senior devops.
Patterns and Practices
Pattern 1: Best Practice Implementation
Description: Detailed explanation of the pattern.
When to Use:
- Scenario 1
- Scenario 2
- Scenario 3
Implementation:
// Example code implementation
export class Example {
// Implementation details
}Benefits:
- Benefit 1
- Benefit 2
- Benefit 3
Trade-offs:
- Consider 1
- Consider 2
- Consider 3
Pattern 2: Advanced Technique
Description: Another important pattern for senior devops.
Implementation:
// Advanced example
async function advancedExample() {
// Code here
}Guidelines
Code Organization
- Clear structure
- Logical separation
- Consistent naming
- Proper documentation
Performance Considerations
- Optimization strategies
- Bottleneck identification
- Monitoring approaches
- Scaling techniques
Security Best Practices
- Input validation
- Authentication
- Authorization
- Data protection
Common Patterns
Pattern A
Implementation details and examples.
Pattern B
Implementation details and examples.
Pattern C
Implementation details and examples.
Anti-Patterns to Avoid
Anti-Pattern 1
What not to do and why.
Anti-Pattern 2
What not to do and why.
Tools and Resources
Recommended Tools
- Tool 1: Purpose
- Tool 2: Purpose
- Tool 3: Purpose
Further Reading
- Resource 1
- Resource 2
- Resource 3
Conclusion
Key takeaways for using this reference guide effectively.
#!/usr/bin/env python3
"""
Deployment Manager
Automated tool for senior devops tasks
"""
import os
import sys
import json
import argparse
from pathlib import Path
from typing import Dict, List, Optional
class DeploymentManager:
"""Main class for deployment manager functionality"""
def __init__(self, target_path: str, verbose: bool = False):
self.target_path = Path(target_path)
self.verbose = verbose
self.results = {}
def run(self) -> Dict:
"""Execute the main functionality"""
print(f"🚀 Running {self.__class__.__name__}...")
print(f"📁 Target: {self.target_path}")
try:
self.validate_target()
self.analyze()
self.generate_report()
print("✅ Completed successfully!")
return self.results
except Exception as e:
print(f"❌ Error: {e}")
sys.exit(1)
def validate_target(self):
"""Validate the target path exists and is accessible"""
if not self.target_path.exists():
raise ValueError(f"Target path does not exist: {self.target_path}")
if self.verbose:
print(f"✓ Target validated: {self.target_path}")
def analyze(self):
"""Perform the main analysis or operation"""
if self.verbose:
print("📊 Analyzing...")
# Main logic here
self.results['status'] = 'success'
self.results['target'] = str(self.target_path)
self.results['findings'] = []
# Add analysis results
if self.verbose:
print(f"✓ Analysis complete: {len(self.results.get('findings', []))} findings")
def generate_report(self):
"""Generate and display the report"""
print("\n" + "="*50)
print("REPORT")
print("="*50)
print(f"Target: {self.results.get('target')}")
print(f"Status: {self.results.get('status')}")
print(f"Findings: {len(self.results.get('findings', []))}")
print("="*50 + "\n")
def main():
"""Main entry point"""
parser = argparse.ArgumentParser(
description="Deployment Manager"
)
parser.add_argument(
'target',
help='Target path to analyze or process'
)
parser.add_argument(
'--verbose', '-v',
action='store_true',
help='Enable verbose output'
)
parser.add_argument(
'--json',
action='store_true',
help='Output results as JSON'
)
parser.add_argument(
'--output', '-o',
help='Output file path'
)
args = parser.parse_args()
tool = DeploymentManager(
args.target,
verbose=args.verbose
)
results = tool.run()
if args.json:
output = json.dumps(results, indent=2)
if args.output:
with open(args.output, 'w') as f:
f.write(output)
print(f"Results written to {args.output}")
else:
print(output)
if __name__ == '__main__':
main()
#!/usr/bin/env python3
"""
Pipeline Generator
Automated tool for senior devops tasks
"""
import os
import sys
import json
import argparse
from pathlib import Path
from typing import Dict, List, Optional
class PipelineGenerator:
"""Main class for pipeline generator functionality"""
def __init__(self, target_path: str, verbose: bool = False):
self.target_path = Path(target_path)
self.verbose = verbose
self.results = {}
def run(self) -> Dict:
"""Execute the main functionality"""
print(f"🚀 Running {self.__class__.__name__}...")
print(f"📁 Target: {self.target_path}")
try:
self.validate_target()
self.analyze()
self.generate_report()
print("✅ Completed successfully!")
return self.results
except Exception as e:
print(f"❌ Error: {e}")
sys.exit(1)
def validate_target(self):
"""Validate the target path exists and is accessible"""
if not self.target_path.exists():
raise ValueError(f"Target path does not exist: {self.target_path}")
if self.verbose:
print(f"✓ Target validated: {self.target_path}")
def analyze(self):
"""Perform the main analysis or operation"""
if self.verbose:
print("📊 Analyzing...")
# Main logic here
self.results['status'] = 'success'
self.results['target'] = str(self.target_path)
self.results['findings'] = []
# Add analysis results
if self.verbose:
print(f"✓ Analysis complete: {len(self.results.get('findings', []))} findings")
def generate_report(self):
"""Generate and display the report"""
print("\n" + "="*50)
print("REPORT")
print("="*50)
print(f"Target: {self.results.get('target')}")
print(f"Status: {self.results.get('status')}")
print(f"Findings: {len(self.results.get('findings', []))}")
print("="*50 + "\n")
def main():
"""Main entry point"""
parser = argparse.ArgumentParser(
description="Pipeline Generator"
)
parser.add_argument(
'target',
help='Target path to analyze or process'
)
parser.add_argument(
'--verbose', '-v',
action='store_true',
help='Enable verbose output'
)
parser.add_argument(
'--json',
action='store_true',
help='Output results as JSON'
)
parser.add_argument(
'--output', '-o',
help='Output file path'
)
args = parser.parse_args()
tool = PipelineGenerator(
args.target,
verbose=args.verbose
)
results = tool.run()
if args.json:
output = json.dumps(results, indent=2)
if args.output:
with open(args.output, 'w') as f:
f.write(output)
print(f"Results written to {args.output}")
else:
print(output)
if __name__ == '__main__':
main()
#!/usr/bin/env python3
"""
Terraform Scaffolder
Automated tool for senior devops tasks
"""
import os
import sys
import json
import argparse
from pathlib import Path
from typing import Dict, List, Optional
class TerraformScaffolder:
"""Main class for terraform scaffolder functionality"""
def __init__(self, target_path: str, verbose: bool = False):
self.target_path = Path(target_path)
self.verbose = verbose
self.results = {}
def run(self) -> Dict:
"""Execute the main functionality"""
print(f"🚀 Running {self.__class__.__name__}...")
print(f"📁 Target: {self.target_path}")
try:
self.validate_target()
self.analyze()
self.generate_report()
print("✅ Completed successfully!")
return self.results
except Exception as e:
print(f"❌ Error: {e}")
sys.exit(1)
def validate_target(self):
"""Validate the target path exists and is accessible"""
if not self.target_path.exists():
raise ValueError(f"Target path does not exist: {self.target_path}")
if self.verbose:
print(f"✓ Target validated: {self.target_path}")
def analyze(self):
"""Perform the main analysis or operation"""
if self.verbose:
print("📊 Analyzing...")
# Main logic here
self.results['status'] = 'success'
self.results['target'] = str(self.target_path)
self.results['findings'] = []
# Add analysis results
if self.verbose:
print(f"✓ Analysis complete: {len(self.results.get('findings', []))} findings")
def generate_report(self):
"""Generate and display the report"""
print("\n" + "="*50)
print("REPORT")
print("="*50)
print(f"Target: {self.results.get('target')}")
print(f"Status: {self.results.get('status')}")
print(f"Findings: {len(self.results.get('findings', []))}")
print("="*50 + "\n")
def main():
"""Main entry point"""
parser = argparse.ArgumentParser(
description="Terraform Scaffolder"
)
parser.add_argument(
'target',
help='Target path to analyze or process'
)
parser.add_argument(
'--verbose', '-v',
action='store_true',
help='Enable verbose output'
)
parser.add_argument(
'--json',
action='store_true',
help='Output results as JSON'
)
parser.add_argument(
'--output', '-o',
help='Output file path'
)
args = parser.parse_args()
tool = TerraformScaffolder(
args.target,
verbose=args.verbose
)
results = tool.run()
if args.json:
output = json.dumps(results, indent=2)
if args.output:
with open(args.output, 'w') as f:
f.write(output)
print(f"Results written to {args.output}")
else:
print(output)
if __name__ == '__main__':
main()
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Senior Devops has 2 known copies in the catalog totaling 55 installs. They canonicalize to this original listing.
- ovachiever - 48 installs
- aaaaqwq - 7 installs
FAQ
What does senior-devops do?
Comprehensive DevOps skill for CI/CD, infrastructure automation, containerization, and cloud platforms (AWS, GCP, Azure). Includes pipeline setup, infrastructure as code, deployment automation, and monitoring. Use when s
When should I use senior-devops?
Comprehensive DevOps skill for CI/CD, infrastructure automation, containerization, and cloud platforms (AWS, GCP, Azure). Includes pipeline setup, infrastructure as code, deployment automation, and monitoring. Use when s
What are common prerequisites?
--- name: senior-devops description: Comprehensive DevOps skill for CI/CD, infrastructure automation, containerization, and cloud platforms (AWS, GCP, Azure).
Is Senior Devops safe to install?
skills.sh reports 3 of 3 security scanners passed. Review the Security Audits panel on this page before installing in production.