# GEMINI.md

## Documentation Status
- Synced with repository refactor baseline on 2026-02-16 (source of truth: `CHANGELOG.md`).

This document provides a comprehensive overview of the Video Library Manager (VLM) project, intended to be used as instructional context for Gemini.

## Project Overview

The Video Library Manager (VLM) is a Python-based CLI tool designed for managing personal video collections. It emphasizes a "safety-first" and "human-in-the-loop" approach, ensuring that no file operations are performed without explicit user confirmation and that all actions are reversible through a rollback mechanism.

**Core Functionality:**

*   **Scanning & Parsing:** Discovers video files, extracts metadata (file info, video properties via `ffprobe`), and parses filenames to identify titles, years, seasons, and episodes.
*   **Metadata Enrichment:** Augments local data with information from TMDB, including bilingual titles and reputation scores. It uses a local SQLite cache to improve performance.
*   **Analysis:** Detects duplicate files (with quality comparisons) and identifies gaps in TV series episodes.
*   **Planning & Execution:** Generates a reviewable JSON-based execution plan for file operations (move, rename, quarantine). The plan is executed only upon user confirmation.
*   **Reporting:** Creates reports for inventory, duplicate files, and series completeness.
*   **State Management:** Tracks the status of files throughout the organization workflow.

**Technologies:**

*   **Language:** Python 3.10+
*   **CLI Framework:** Click
*   **Configuration:** YAML
*   **Dependencies:** `pyyaml`, `click`
*   **Development:** `pytest` for testing, `hypothesis` for property-based testing.
*   **Package Management:** `uv` is mentioned in the documentation.

**Architecture:**

The project follows a modular structure located in the `src/vlm` directory. Key modules include:

*   `cli.py`: The main entry point for the CLI, using Click.
*   `commands/*.py`: Implementation of the individual CLI commands (scan, parse, enrich, etc.).
*   `scanner.py`, `parser.py`, `enrichment.py`, `analysis.py`, `planner.py`, `executor.py`: Core logic for the different stages of the workflow.
*   `providers/tmdb.py`: Client for interacting with the TMDB API.
*   `models.py`: Defines the data structures used throughout the application.
*   `config.py`: Manages application configuration from a YAML file.

## Building and Running

The project uses `uv` for dependency management.

**Installation:**

*   Install dependencies: `uv pip install -e .`
*   Install development dependencies: `uv pip install -e ".[dev]"`

**Running the application:**

The main entry point is the `vlm` command.

*   Initialize configuration: `uv run vlm config init`
*   Scan the library: `uv run vlm scan`
*   Parse filenames: `uv run vlm parse`
*   Enrich metadata: `uv run vlm enrich`
*   Analyze the library: `uv run vlm analyze`
*   Generate a plan: `uv run vlm plan`
*   Execute the plan (dry-run): `uv run vlm execute`
*   Execute the plan (with confirmation): `uv run vlm execute --confirm`
*   Rollback the last execution: `uv run vlm rollback`

**Running tests:**

*   Run all tests: `uv run pytest`

## Development Conventions

*   **Code Style:** Adheres to PEP 8, uses 4-space indentation, and includes type hints for public functions.
*   **Testing:** Tests are located in the `tests/` directory and written using `pytest`. The project also uses `hypothesis` for property-based testing. New features should be accompanied by tests.
*   **Commits:** Commit messages should be clear and descriptive.
*   **Documentation:** The `README.md` file is very comprehensive and should be kept up-to-date.
*   **Safety:** A core principle is safety. Changes to the filesystem should be gated behind user confirmation (`--confirm`) and be reversible. The tool should never permanently delete files.