These exercise pure logic: config parsing, prompt derivation, LoRA spec parsing, frame-length fitting, library round-robin selection. They do not touch CUDA, Wan2.2, MuseTalk, or ffmpeg. Safe to run on Windows, outside Docker, without any models installed.

Component tests — slow, GPU-required, run inside Docker

Each script in tests/component/ exercises one subsystem end-to-end against the real models. They are ordered to match the implementation phases:

Script	Phase	Tests
`test_01_video_skeleton.py`	1	VideoEngine loads, config gate respected
`test_02_wan22_loras.py`	2	Wan2.2 pipeline loads, LoRA stack applies
`test_03_idle_clip.py`	3	set_avatar → idle MP4, written to disk for eyeballing
`test_04_library_prebake.py`	4	library mode pre-bakes N base clips
`test_05_musetalk_lipsync.py`	5	MuseTalk lip-sync on library frames + ffmpeg mux
`test_06_reflective.py`	6	reflective mode: fresh Wan2.2 per reply
`test_07_endpoints.py`	7	HTTP endpoints return sane responses
`test_08_lora_reload.py`	8	/api/reload-loras swaps LoRAs live

Run one:

# Inside the container:
docker compose exec voice-chat python -m tests.component.test_03_idle_clip

Run all (slow, ~20+ minutes on 5090):

docker compose exec voice-chat python -m tests.component.run_all

Each component script writes its artifacts (MP4s, PNG frame dumps, logs) to tests/component/_out/ so you can visually inspect results. That directory is gitignored.