Major improvements to release note generation system:
**AI Model Optimization:**
- Switch from Claude Sonnet 4.5 to Haiku 4.5 (67% cheaper, 50% faster)
- Cost reduced from 1.0 to 0.33 Premium requests per generation
- Generation time reduced from ~30s to ~15s
- Quality maintained through improved prompt engineering
**Improved Prompt Structure:**
- Restructured prompt: instructions first, commit data last
- Added explicit user-feature prioritization rules (sensors > config > developer tools)
- Integrated file change statistics with each commit
- Added file path guidance (custom_components/ = HIGH, scripts/ = LOW)
- Added 3-step decision process with walkthrough example
- Added explicit output constraints to prevent meta-commentary
**Auto-Update Feature:**
- Consolidated improve-release-notes functionality into generate-release-notes
- Automatic detection of existing GitHub releases
- Interactive prompt to update both title and body
- Shows comparison: current title vs. new AI-generated title
**File Statistics Integration:**
- Added --stat --compact-summary to git log
- Shows which files changed in each commit with line counts
- Helps AI quantitatively assess change importance (100+ lines = significant)
- Enables better prioritization of user-facing features
**Testing Results:**
- Generated title: "Price Volatility Analysis & Configuration" (user-focused!)
- Successfully prioritizes user features over developer/CI changes
- No more generic "New Features & Bug Fixes" titles
- Thematic titles that capture main release highlights
Impact: Release note generation is now faster, cheaper, and produces
higher-quality user-focused titles. Single consolidated script handles
both generation and updating existing releases.
Updated copilot-instructions.md with comprehensive documentation for
new release workflows and validation requirements.
Added sections:
- Selector validation rules for hassfest compliance
- Pattern requirement: [a-z0-9-_]+ (lowercase only)
- Common pitfalls with SelectOptionDict and translation_key
- Validation examples (correct vs incorrect)
- Legacy/Backwards compatibility guidelines
- When to add migration code vs breaking changes
- check-if-released script usage
- Rule: Only migrate for released changes
- Prefer documentation over complexity
- Semantic versioning workflow
- Pre-1.0 vs Post-1.0 versioning rules
- suggest-version script usage and output
- prepare-release integration
- Version check in CI/CD
- Release notes generation
- Updated with auto-sync and version check features
- Backend comparison (AI vs git-cliff vs manual)
- Complete workflow examples
Impact: AI assistant and developers have clear guidance on hassfest
requirements, legacy migration decisions, and the complete release
automation workflow. Reduces errors and maintains consistency across
sessions.
Created professional documentation structure:
**User Documentation (docs/user/):**
- README.md: Documentation hub with quick start guide
- Placeholder files for future content migration:
* installation.md, configuration.md, sensors.md
* services.md, automation-examples.md, troubleshooting.md
**Developer Documentation (docs/development/):**
- README.md: Comprehensive contributor guide with AI section
- setup.md: DevContainer and environment setup
- architecture.md: Code structure overview
- testing.md: Testing guidelines
- coding-guidelines.md: Style guide and critical patterns
- release-management.md: Complete release workflow documentation
**AI Development Disclosure:**
- README.md: "🤖 Development Note" section before license
* Honest disclosure about extensive AI assistance
* Quality assurance measures mentioned
* Invitation for bug reports with positive tone
- docs/development/README.md: Detailed AI section
* What AI handles (patterns, generation, refactoring)
* Benefits (rapid development, consistency)
* Limitations (edge cases, complex patterns)
* Quality assurance process
- CONTRIBUTING.md: Brief AI note with practical tip
**Updated:**
- README.md: Simplified to landing page with documentation links
- CONTRIBUTING.md: Modernized with new docs structure
- copilot-instructions.md: Added documentation organization section
Impact: Clear separation of user vs. developer documentation following
open-source best practices. Transparent about AI-assisted development
approach without being defensive. Scalable structure for future growth.
Expanded the documentation to include a comprehensive update process for maintaining consistency between code and documentation. Added sections on automatic inconsistency detection, documentation update proposals, and guidelines for testing changes and git workflow.
Impact: Ensures accurate documentation and streamlined workflows for future development sessions.
