Command Library Reference

July 2, 2025 · View on GitHub

Comprehensive reference for all available commands in the auto-claude-code template system

📋 Quick Reference

The template system includes 26+ professional-grade commands organized by specialization. Each command includes framework-specific implementations, comprehensive validation checklists, and production-ready code examples.

🔒 Security Commands

security-audit

Purpose: Comprehensive security vulnerability assessment with OWASP Top 10 coverage

Usage: /project:security-audit [--framework] [--depth] [--output-format]

Features:

  • OWASP Top 10 vulnerability scanning
  • Framework-specific security checks (FastAPI, Django, Flask)
  • Automated tool integration (bandit, safety, semgrep)
  • Multi-severity vulnerability reporting
  • Remediation suggestions with code examples

Example: /project:security-audit --framework django --depth comprehensive

secrets-scan

Purpose: Detect and remediate secrets in codebase and Git history

Usage: /project:secrets-scan [--scope] [--remediate] [--patterns]

Features:

  • Git history scanning for exposed secrets
  • Pattern-based detection (API keys, passwords, tokens)
  • False positive filtering
  • Automated remediation with .gitignore updates
  • Pre-commit hook integration

Example: /project:secrets-scan --scope git-history --remediate

security-headers

Purpose: HTTP security headers configuration and implementation

Usage: /project:security-headers [--framework] [--policy-type]

Features:

  • Content Security Policy (CSP) generation
  • HSTS, X-Frame-Options, X-Content-Type-Options
  • Framework-specific implementations
  • Security policy testing and validation

Example: /project:security-headers --framework fastapi --policy-type strict

🚀 DevOps Commands

setup-ci

Purpose: Multi-platform CI/CD pipeline generation and configuration

Usage: /project:setup-ci [--platform] [--stages] [--security-integration]

Features:

  • Support for GitHub Actions, GitLab CI, Azure DevOps, Jenkins, CircleCI
  • Multi-stage pipelines (lint, test, security, build, deploy)
  • Security scanning integration
  • Artifact management and caching
  • Environment-specific configurations

Example: /project:setup-ci --platform github-actions --stages lint,test,security,deploy

containerize

Purpose: Docker containerization with security hardening and orchestration

Usage: /project:containerize [--strategy] [--orchestration] [--security-hardening]

Features:

  • Multi-stage Docker builds for optimization
  • Security hardening with non-root users and minimal base images
  • Kubernetes deployment manifests
  • Docker Compose configurations
  • Health checks and monitoring integration

Example: /project:containerize --strategy multi-stage --orchestration kubernetes --security-hardening

deploy-config

Purpose: Cloud deployment configurations with multiple strategies

Usage: /project:deploy-config [--cloud] [--strategy] [--environment]

Features:

  • Support for AWS, Google Cloud, Azure, DigitalOcean
  • Blue-green, rolling, and canary deployment strategies
  • Infrastructure as Code (Terraform, CloudFormation)
  • Environment-specific configurations
  • Monitoring and alerting setup

Example: /project:deploy-config --cloud aws --strategy blue-green --environment production

⚡ Performance Commands

performance-audit

Purpose: Comprehensive performance analysis and optimization

Usage: /project:performance-audit [--scope] [--profiling] [--database-optimization]

Features:

  • Code profiling and bottleneck identification
  • Database query optimization
  • Memory usage analysis
  • Asynchronous processing recommendations
  • Performance monitoring integration

Example: /project:performance-audit --scope full --profiling --database-optimization

load-test

Purpose: Progressive load testing with multiple tools and scenarios

Usage: /project:load-test [--tool] [--scenario] [--monitoring]

Features:

  • Support for Locust, k6, Artillery, JMeter
  • Progressive test scenarios (smoke, load, stress, spike, endurance)
  • Real-time monitoring and alerting
  • Performance regression detection
  • Scalability recommendations

Example: /project:load-test --tool locust --scenario progressive --monitoring

🔗 Data & API Commands

api-design

Purpose: RESTful API design with comprehensive specifications

Usage: /project:api-design [--framework] [--auth-strategy] [--documentation]

Features:

  • OpenAPI specification generation
  • Authentication strategies (JWT, OAuth2, API keys)
  • Rate limiting and throttling
  • API versioning strategies
  • Framework-specific implementations (FastAPI, Django REST)

Example: /project:api-design --framework fastapi --auth-strategy oauth2 --documentation openapi

data-migration

Purpose: Database migration tools with cross-platform support

Usage: /project:data-migration [--source] [--target] [--strategy]

Features:

  • Cross-platform migration (PostgreSQL, MySQL, SQLite, MongoDB)
  • Schema and data transformation
  • Incremental migration strategies
  • Backup and rollback mechanisms
  • Data validation and integrity checks

Example: /project:data-migration --source mysql --target postgresql --strategy incremental

backup-strategy

Purpose: Disaster recovery with 3-2-1 backup rule implementation

Usage: /project:backup-strategy [--storage] [--frequency] [--encryption]

Features:

  • 3-2-1 backup rule implementation
  • Multiple storage backends (S3, Google Cloud, Azure)
  • Automated backup scheduling
  • Encryption and security
  • Recovery testing and validation

Example: /project:backup-strategy --storage s3 --frequency daily --encryption

🧪 Integration Commands

integration-test

Purpose: End-to-end testing with comprehensive service integration

Usage: /project:integration-test [--scope] [--framework] [--mocking]

Features:

  • API integration testing
  • Database integration testing
  • External service mocking
  • End-to-end user journey testing
  • Performance integration testing

Example: /project:integration-test --scope full --framework pytest --mocking

🧠 Data Science Commands

data-exploration

Purpose: Automated exploratory data analysis with comprehensive profiling

Usage: /project:data-exploration [--dataset] [--depth] [--output-format]

Features:

  • Pandas profiling with automated reports
  • Statistical analysis and normality testing
  • Data quality assessment and outlier detection
  • Correlation analysis and feature relationships
  • Bias detection and sampling analysis
  • Comprehensive visualizations

Example: /project:data-exploration --dataset data/customers.csv --depth comprehensive --output-format html

model-development

Purpose: ML model training with automated hyperparameter tuning

Usage: /project:model-development [--model-type] [--target] [--validation]

Features:

  • Multiple algorithm support (classification, regression, clustering)
  • Automated hyperparameter tuning with GridSearchCV and Optuna
  • Cross-validation and model evaluation
  • Feature importance analysis
  • MLflow integration for experiment tracking
  • Model persistence and versioning

Example: /project:model-development --model-type classification --target churn --validation stratified

experiment-tracking

Purpose: ML experiment tracking with multiple platform integrations

Usage: /project:experiment-tracking [--platform] [--experiment-name] [--auto-logging]

Features:

  • MLflow, Weights & Biases, Neptune platform support
  • Automated parameter and metric logging
  • Model versioning with signatures and metadata
  • Experiment comparison and visualization
  • Data versioning with DVC integration
  • Collaborative experiment sharing

Example: /project:experiment-tracking --platform mlflow --experiment-name customer-churn --auto-logging

data-pipeline

Purpose: ETL/ELT data pipelines with orchestration and monitoring

Usage: /project:data-pipeline [--pipeline-type] [--orchestrator] [--schedule]

Features:

  • ETL, ELT, streaming, and lambda architecture support
  • Apache Airflow, Prefect, Dagster orchestration
  • Real-time streaming with Apache Kafka
  • Comprehensive error handling and retry mechanisms
  • Data quality validation at each stage
  • Monitoring and alerting integration

Example: /project:data-pipeline --pipeline-type etl --orchestrator airflow --schedule daily

🚧 Coming Soon

model-deployment

Purpose: ML model serving and production deployment

  • Status: In development
  • Features: Model serving APIs, containerization, A/B testing, monitoring

model-monitoring

Purpose: Model drift detection and performance tracking

  • Status: In development
  • Features: Drift detection, performance monitoring, alerting, retraining triggers

feature-engineering

Purpose: Automated feature selection and engineering pipelines

  • Status: Planned
  • Features: Automated feature selection, engineering pipelines, feature stores

data-governance

Purpose: Data lineage, quality monitoring, and compliance

  • Status: Planned
  • Features: Data lineage tracking, quality monitoring, compliance frameworks

🎯 Command Usage Patterns

Project vs User Commands

  • /project:command: Project-specific implementation with full setup
  • /user:command: User-level configuration and examples

Framework-Specific Implementations

All commands include optimized implementations for:

  • FastAPI: High-performance async APIs
  • Django: Full-stack web applications
  • Flask: Lightweight web applications
  • Data Science: Jupyter notebook integration
  • CLI Tools: Command-line applications

Validation Checklists

Every command includes comprehensive validation checklists ensuring:

  • ✅ Proper implementation and configuration
  • ✅ Security best practices applied
  • ✅ Performance optimization completed
  • ✅ Documentation and testing coverage
  • ✅ Production readiness validation

🔧 Customization

Commands can be customized by:

  1. Modifying templates in templates/global/commands/
  2. Adding framework-specific sections for new frameworks
  3. Extending validation checklists for additional requirements
  4. Creating custom command variants for specific use cases

📚 Documentation

Each command includes:

  • Complete usage documentation with examples
  • Framework-specific implementation guides
  • Validation checklists for quality assurance
  • Professional-grade code examples ready for production
  • Best practices and recommendations
  • Integration guides with related tools and services

For detailed implementation of any command, see the corresponding file in templates/global/commands/