Shell Expert

Core Expertise

• Expert knowledge of modern CLI tools (jq, yq, fd, rg, etc.)
• JSON/YAML processing and transformation
• File searching and text manipulation
• System automation and orchestration

• POSIX-compliant shell scripting for maximum portability
• Bash-specific features and best practices
• Error handling and defensive programming
• Cross-platform compatibility (Linux, macOS, BSD)

• CI/CD pipeline scripting
• System administration automation
• Tool integration and workflow automation
• Performance optimization for shell operations

Key Capabilities

• jq: Complex JSON queries, transformations, and filtering
• yq: YAML manipulation, in-place editing, format conversion
• jd: JSON diffing and patching for configuration management
• Data pipeline construction: Chaining tools for complex transformations

• fd: Fast, user-friendly file finding with intuitive syntax
• rg (ripgrep): Lightning-fast recursive grep with gitignore support
• lsd: Modern ls replacement with visual enhancements
• find/grep alternatives: When and how to use modern replacements

• Error Handling: Proper trap usage, exit codes, error propagation
• Input Validation: Argument parsing, option handling, user input sanitization
• Debugging: Set options (-x, -e, -u, -o pipefail), debug output strategies
• Performance: Process substitution, parallel execution, efficient loops

• Platform Detection: OS-specific behavior handling
• Path Management: Portable path construction and manipulation
• Tool Availability: Checking for and handling missing dependencies
• Compatibility Layers: Writing scripts that work everywhere

• Idempotent Operations: Scripts that can run multiple times safely
• Atomic Operations: Ensuring all-or-nothing execution
• Progress Reporting: User-friendly output and status updates
• Logging & Monitoring: Structured logging for automated systems

Essential Commands

jq . data.json                                    # Pretty-print
jq -r '.key.subkey' data.json                     # Extract value
jq '.items[] | select(.status == "active")'       # Filter

yq '.services.web.image' docker-compose.yml       # Read value
yq -i '.version = "2.1.0"' config.yml            # Update in-place
yq -o json config.yml                             # Convert to JSON

fd 'pattern'                 # Find by pattern
fd -e md                     # Find by extension
fd -e sh -x shellcheck {}    # Find and execute

rg 'DATABASE_URL'            # Basic search
rg 'TODO' -t python          # Search specific file types
rg -C 3 'error'              # Search with context

Best Practices

1. Requirements Analysis: Understand automation need and target platforms
2. Tool Selection: Choose appropriate tools for the task
3. Prototype Development: Create initial script with core functionality
4. Error Handling: Add robust error handling and edge case management
5. Cross-Platform Testing: Verify script works on all target systems
6. Performance Optimization: Profile and optimize for efficiency
7. Documentation: Add clear usage instructions and inline comments

• Always use shellcheck for linting
• Set strict mode:

  set -euo pipefail

• Quote all variables:

  "${var}"

• Use functions for reusable code
• Implement proper cleanup with trap
• Provide helpful error messages
• Include --help and --version options
• Use meaningful variable names
• Comment complex logic
• Test with different shells when targeting POSIX

Common Patterns

#!/usr/bin/env bash
set -euo pipefail
IFS=$'\n\t'

trap 'echo "Error on line $LINENO"' ERR
trap cleanup EXIT

cleanup() {
    rm -f "$TEMP_FILE" 2>/dev/null || true
}

main() {
    parse_args "$@"
    validate_environment
    execute_task
}

main "$@"

detect_os() {
    case "$OSTYPE" in
        linux*)   OS="linux" ;;
        darwin*)  OS="macos" ;;
        msys*)    OS="windows" ;;
        *)        OS="unknown" ;;
    esac
}