New DeepCamera analysis skill wrapping the SmartHome-Bench dataset
for evaluating VLM performance on video anomaly detection.

- SKILL.md with YAML manifest, params, and protocol docs
- config.yaml with mode/maxVideos/categories params
- run-benchmark.cjs: video download (yt-dlp), frame sampling (ffmpeg),
  multi-image VLM evaluation, binary anomaly scoring, JSONL protocol
- generate-report.cjs: HTML report with confusion matrix, per-category
  metrics (accuracy/precision/recall/F1), model comparison
- fixtures/annotations.json: 99 curated clips across 7 categories
  (Wildlife, Senior Care, Baby Monitoring, Pet Monitoring,
   Home Security, Package Delivery, General Activity)
- deploy.sh: system dep checks + npm install
- Added to skills.json registry and README catalog

- New HomeSafe-Bench skill: 40 indoor safety VLM tests across 5 categories
  (fire/smoke, electrical, trip/fall, child safety, falling objects)
- 26/40 AI-generated fixture frames (remaining pending image gen quota)
- Runtime disk space pre-check in SmartHome-Bench (15GB full / 2GB subset)
- Register homesafe-bench in skills.json
- All datasets download at runtime, not during deployment

Add remaining 14 AI-generated frames via Vertex AI Imagen 3:
- child_01, child_04-08 (child safety category)
- falling_01-08 (falling objects category)

All 40 test scenarios now have matching fixture images:
  8 fire/smoke, 8 electrical, 8 trip/fall, 8 child safety, 8 falling objects

numpy 2.x breaks coremltools PyTorch→MIL converter with:
  'only 0-dimensional arrays can be converted to Python scalars'

- Pin numpy>=1.24.0,<2.0.0 in requirements.txt and requirements_mps.txt
- Add runtime numpy version guard in env_config.py export_model()
  that detects numpy 2.x and gracefully skips CoreML export
- Track homesafe-bench package-lock.json