Fix #19: Improve CONTRIBUTING.md with architecture guide and good-first-issue links

CONTRIBUTING.md

@@ -2,139 +2,10 @@
 
 Thanks for your interest in contributing! CAT is an open-source project and we welcome contributions of all kinds.
 
-## New here? Start with a Good First Issue
+## Start Here
 
-We maintain a curated list of beginner-friendly issues — each one includes clear context, the files to look at, and a suggested approach:
+New to the codebase? This section will help you get oriented quickly.
 
-**[Browse Good First Issues](https://github.com/roeimichael/ContextAnalyzerTerminal/labels/good%20first%20issue)**
+### Architecture Overview
 
-These range from one-line bug fixes to small feature additions, and are a great way to get familiar with the codebase and dev workflow before tackling bigger tasks.
-
-## Understanding the Architecture
-
-CAT's data flows in a pipeline:
-
-```
-Claude Code Hooks → FastAPI Collector → Delta Engine → Anomaly Detection → Notifications
-     (hooks/)        (collector/)       (collector/)      (engine/)         (notify/)
-```
-
-**Recommended reading order for new contributors:**
-1. `src/context_analyzer_tool/config.py` — how configuration works
-2. `src/context_analyzer_tool/collector/models.py` — data shapes (Pydantic models)
-3. `src/context_analyzer_tool/collector/delta_engine.py` — core logic: correlating events with token snapshots
-4. `src/context_analyzer_tool/engine/anomaly.py` — how anomalies are detected (Z-score statistics)
-
-For deeper dives, see the architecture docs:
-- [`docs/phase1-architecture.md`](docs/phase1-architecture.md) — Delta engine design and schema
-- [`docs/phase2-architecture.md`](docs/phase2-architecture.md) — Anomaly detection, classifier, and notifications
-
-## Getting Started
-
-### Prerequisites
-
-- Python 3.11+
-- [uv](https://docs.astral.sh/uv/) package manager
-
-### Development Setup
-
-```bash
-# Clone the repo
-git clone https://github.com/roeimichael/ContextAnalyzerTerminal.git
-cd ContextAnalyzerTerminal
-
-# Install dependencies (including dev tools)
-uv sync --all-extras
-
-# Verify everything works
-uv run pytest tests/ -v
-uv run ruff check src tests
-```
-
-### Project Structure
-
-```
-src/context_analyzer_tool/
-  cli.py              # Typer CLI entry point
-  config.py           # TOML + env var configuration
-  collector/           # FastAPI HTTP server + delta engine
-  db/                  # SQLite async data layer
-  engine/              # Baseline, anomaly detection, LLM classifier
-  dashboard/           # Rich TUI dashboard
-  notify/              # Multi-channel notifications
-hooks/                 # Claude Code hook scripts
-tests/                 # Pytest suite
-```
-
-## Development Workflow
-
-### Running Tests
-
-```bash
-uv run pytest tests/ -v          # Full suite
-uv run pytest tests/test_db.py   # Single module
-```
-
-### Linting & Type Checking
-
-```bash
-uv run ruff check src tests      # Linter
-uv run ruff check --fix src      # Auto-fix
-uv run pyright                   # Type checker (strict mode)
-```
-
-### Running Locally
-
-```bash
-# Start the collector server
-uv run context-analyzer-tool serve
-
-# In another terminal, view the dashboard
-uv run context-analyzer-tool dashboard
-
-# Check health
-uv run context-analyzer-tool health
-```
-
-## Submitting Changes
-
-1. Fork the repository
-2. Create a feature branch (`git checkout -b feature/my-feature`)
-3. Make your changes
-4. Ensure tests pass and lint is clean
-5. Commit with a clear message describing what and why
-6. Open a Pull Request against `main`
-
-### PR Guidelines
-
-- Keep PRs focused — one feature or fix per PR
-- Add tests for new functionality
-- Update the README if you're adding user-facing features
-- Run the full test suite before submitting
-
-## Areas Where Help Is Welcome
-
-- **New notification channels** (email, Telegram, etc.)
-- **Dashboard improvements** (web UI, more visualizations)
-- **Platform support** (Windows notifications, broader OS testing)
-- **Documentation** (tutorials, examples, translations)
-- **Performance** (database query optimization, memory profiling)
-
-## Code Style
-
-- We use [Ruff](https://docs.astral.sh/ruff/) for linting (config in `pyproject.toml`)
-- [Pyright](https://github.com/microsoft/pyright) strict mode for type checking
-- Line length: 99 characters
-- All I/O code should be async
-
-## Reporting Bugs
-
-Open an issue with:
-- What you expected vs what happened
-- Steps to reproduce
-- Your Python version and OS
-- Relevant logs or error output
-
-## License
-
-By contributing, you agree that your contributions will be licensed under the MIT License.
+CAT's data flows in a pipeline: