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FEATURES.md

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SLO Computer - User Experience Improvements

This document outlines potential user experience improvements and feature enhancements for the SLO Computer project.

Command Line Interface Improvements

  • Interactive Mode: Add an interactive CLI mode that guides users through setting up SLOs with step-by-step prompts
  • Rich Terminal UI: Implement a TUI (Terminal User Interface) with panels for input, visualization, and results
  • Color-Coded Output: Use colors to highlight critical information, warnings, and success messages
  • Progress Indicators: Add spinners or progress bars for calculations that take more than a few seconds
  • Command Autocomplete: Implement shell autocompletion for commands and flags
  • History: Save command history for easy reuse of previous calculations
  • Improved Output Formatting: Format error rates as percentages and durations in human-readable form
  • Alert Labeling: Clearly label "slow burn" and "fast burn" alerts in the output

Visualization

  • ASCII Charts: Add simple ASCII/Unicode charts to visualize burn rates and alert thresholds directly in the terminal
  • Export to SVG/PNG: Allow exporting visualizations to image formats for inclusion in documentation
  • Burn Rate Graphs: Visualize how quickly error budgets would be consumed at different error rates
  • Alert Timeline: Show when alerts would trigger on a timeline based on different error scenarios
  • Comparative Views: Allow side-by-side comparison of different SLO configurations
  • Error Budget Consumption Visualization: Show how quickly error budget would be consumed at current rates

Output Formats

  • Multiple Format Support: Add support for JSON, YAML, CSV, and markdown output formats
  • Template System: Allow users to define custom output templates
  • Clipboard Support: Add option to copy results directly to clipboard
  • Report Generation: Generate comprehensive PDF/HTML reports with explanations and visualizations
  • Alert Configuration Export: Export alert configurations directly to monitoring systems (Prometheus, Datadog, etc.)
  • Implementation Examples: Include sample configurations for popular monitoring systems

Usability Features

  • Saved Configurations: Allow saving and loading of common configurations
  • Presets: Provide industry-standard presets for common service types
  • Batch Processing: Process multiple services or configurations in a single run
  • What-If Analysis: Allow users to simulate different error scenarios and see the impact
  • Recommendations: Provide smart recommendations based on service characteristics
  • Alert Tuning Guidance: Offer suggestions for adjusting alerts if they're too sensitive or not sensitive enough

Educational Components

  • Explanation Mode: Add verbose mode that explains the mathematical reasoning behind recommendations
  • Built-in Examples: Include real-world examples that users can explore
  • Tooltips/Help: Contextual help for each parameter explaining its impact
  • Best Practices: Include best practices guidance alongside recommendations
  • Warning System: Warn users when configurations might lead to alert fatigue or missed incidents
  • Interactive Tutorial: Create guided walkthroughs for first-time users
  • Visual Guide: Develop visual explanations of multi-window, multi-burn-rate alerting concepts

Integration Capabilities

  • API Mode: Run as a service with REST API endpoints for integration with other tools
  • Import from Monitoring: Import service metrics from monitoring systems to inform recommendations
  • CI/CD Integration: Provide ways to validate SLO configurations in CI/CD pipelines
  • Webhook Support: Send results to webhooks for integration with chat platforms or other tools
  • Plugin System: Allow extending functionality through plugins
  • Monitoring System Templates: Provide ready-to-use templates for implementing alerts in various systems

Advanced Features

  • Multi-Service Analysis: Analyze dependencies between services and suggest coordinated SLOs
  • Historical Analysis: Import historical error data to validate SLO configurations
  • Seasonality Detection: Detect and account for traffic patterns and seasonality
  • Machine Learning: Use ML to suggest optimal SLOs based on service characteristics
  • Anomaly Detection: Identify unusual patterns in service behavior that might affect SLO setting
  • Alert Effectiveness Analysis: Analyze how effective suggested alerts would have been against historical incidents

Web Interface

  • Web UI: Create a simple web interface for users who prefer graphical interfaces
  • Shareable Results: Generate shareable links for collaboration
  • Dashboard: Create dashboards for monitoring multiple services' SLOs
  • Team Collaboration: Allow teams to collaborate on SLO definitions
  • Version Control: Track changes to SLO definitions over time

Documentation and Help

  • Interactive Tutorial: Create an interactive tutorial for first-time users
  • Contextual Documentation: Provide context-sensitive help throughout the application
  • Glossary: Include a glossary of SLO/SLI terms accessible from the CLI
  • Case Studies: Include real-world case studies showing how SLOs improved reliability
  • FAQ Section: Compile frequently asked questions with detailed answers
  • Implementation Guides: Create guides for implementing the suggested alerts in different monitoring systems
  • Troubleshooting Tips: Provide guidance on what to do when alerts fire and how to investigate

Accessibility and Inclusivity

  • Internationalization: Support for multiple languages
  • Screen Reader Support: Ensure output is accessible for screen readers
  • Configurable Output: Allow customizing output density and verbosity
  • Keyboard Navigation: Ensure all features are accessible via keyboard
  • Documentation Accessibility: Ensure all documentation follows accessibility best practices