# Changelog

All notable changes to this project will be documented in this file.

The format is based on [Common Changelog](https://common-changelog.org/),
and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).

## [Unreleased]

### Added

- feat: Sprint 3 - Landing Page Development Started
  - Initialize React + Vite + TypeScript project in `frontend/` directory
  - Install and configure Tailwind CSS, PostCSS, Autoprefixer
  - Setup project structure following docs/frontend_landing_plan.md
  - Configure .gitignore for frontend dependencies and build artifacts
  - Add Tailwind directives to src/index.css
  - Ready for component development and styling

- feat: Create n8n workflow `LogWhisperer_Ingest` for secure log ingestion
  - Webhook trigger on POST `/webhook/logwhisperer/ingest`
  - HMAC-SHA256 signature validation with anti-replay protection
  - Data validation (UUID, severity levels, non-empty raw_log)
  - PostgreSQL storage with automatic table creation
  - Conditional AI processing for critical severity logs
  - JSON export at `workflows/logwhisperer_ingest.json`
  - Test suite at `workflows/test_workflow.sh`
  - Integration guide at `workflows/INTEGRATION.md`
  - Documentation at `workflows/README.md`
  - Implements Metodo Sacchi: Safety First, Little Often, Double Check

- feat: Configure MCP servers for enhanced AI capabilities
  - sequential-thinking MCP for structured problem solving
  - context7 MCP for contextual library documentation retrieval
  - n8n MCP for workflow automation integration
- docs: Add agent-specific configurations in `.opencode/agents/`
  - @n8n_specialist_agent for n8n workflow management
  - @context_auditor_agent for documentation alignment checks
- docs: Add skill playbooks in `.opencode/skills/`
  - TDD_Python_Specialist: Test-driven development workflow
  - Git_and_Changelog: Conventional commits and changelog standards
  - n8n_automation_mastery: n8n workflow best practices
  - context7_documentation_retrivial: Context-aware documentation lookup
- docs: Add requirements.txt with Python dependencies (pytest, requests)
- docs: Add AI Pipeline technical specification
  - System prompt with Metodo Sacchi integration (Safety First, Little Often, Double Check)
  - OpenRouter configuration (migrated from OpenAI direct)
    - 25% cost savings (~$0.00015/call vs ~$0.0002/call)
    - Multi-provider fallback (300+ models available)
    - Automatic failover if primary provider down
  - Complete n8n Code Node JavaScript implementation
    - OpenRouter endpoint: https://openrouter.ai/api/v1
    - Required headers: HTTP-Referer, X-Title for ranking
    - Model format: provider/model (e.g., openai/gpt-4o-mini)
  - JSON output schema with severity mapping (critical/medium/low)
  - Error handling with circuit breaker pattern
  - Security guidelines (data sanitization, rate limiting)
  - 10 acceptance criteria defined
  - 5 test scenarios with expected input/output
  - 5 real-world examples (OOM, disk full, connection refused, etc.)
  - Implementation checklist for developers

### Changed

- docs: Update README.md with complete project structure
  - Add MCP configuration section
  - Document all agent configurations
  - Include skill playbooks in project tree
  - Update setup instructions with requirements.txt
- docs: Refactor setup documentation structure (moved to `docs/1.setup_procedure/`)

## [0.1.1] - 2026-04-02

### Added

- docs: Project Review Sprint 1 complete analysis
  - Product Manager review: UVP alignment (7.05/10)
  - Tech Lead review: Architecture assessment (7.5/10)
  - Security Auditor review: Risk analysis (5.75/10)
  - Comprehensive recommendations for Sprint 2

### Changed

- docs: Major README.md refactoring with badges and improved navigation
- docs: Updated all sprint documentation to "Completed" status

## [0.1.0] - 2026-04-02

### Added

- feat: Implement log ingestion script (`logwhisperer.sh`) for monitoring system logs
  - Monitor multiple log sources: syslog, nginx, postgresql
  - Pattern matching for critical errors (FATAL, ERROR, OOM, segfault, disk full)
  - JSON payload generation with severity levels (low, medium, critical)
  - Rate limiting to prevent alert flooding (30s per source/pattern)
  - Offset tracking for each log file to avoid reprocessing
  - HTTP POST dispatch to configurable webhook with retry logic
  - Dry-run mode for testing pattern matching without sending webhooks
  - Configuration file support (`/etc/logwhisperer/config.env`)
  - Command-line flags: `--help`, `--validate`, `--config`, `--dry-run`, `--test-line`

- feat: Create installation script (`install.sh`)
  - Interactive configuration wizard
  - UUID v4 generation for CLIENT_ID
  - Systemd service creation (when run as root)
  - Support for both system-wide and user-local installation
  - Prerequisite checking (bash, curl)
  - Connectivity test to webhook URL

- test: Add comprehensive test suite (`tests/test_logwhisperer.py`)
  - Script existence and executable validation
  - Configuration validation tests
  - Pattern matching tests (FATAL, OOM, ERROR patterns)
  - JSON payload structure validation
  - Severity mapping verification

- docs: Create technical specification for Feature 1 (Log Ingestion)
  - Architecture diagram and component description
  - Requirements (functional and non-functional)
  - Safety guidelines (Metodo Sacchi)
  - Acceptance criteria

- docs: Create Sprint 1 verification report (`docs/sprint1_verification.md`)
  - Complete verification of all Sprint 1 deliverables
  - Test results summary (12/12 tests passed)
  - Acceptance criteria checklist
  - Security audit results
  - Code quality assessment

### Security

- Configuration files created with restrictive permissions (600)
- No hardcoded credentials in scripts
- HTTPS validation for webhook URLs (warning for non-HTTPS)
- Read-only access to log files (no modifications)