docs(depth-estimation): add README with privacy focus, hardware support, model table

Author: solderzzc

skills/transformation/depth-estimation/README.md

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+# Depth Estimation — Privacy Transform
+
+Transform camera feeds into **colorized depth maps** using [Depth Anything v2](https://github.com/DepthAnything/Depth-Anything-V2), providing real-time privacy protection for security monitoring.
+
+In **privacy mode** (`depth_only`), the scene is fully anonymized — no faces, no clothing, no identifying features — while preserving spatial layout and activity patterns for security awareness.
+
+![Privacy Transform Flow](https://img.shields.io/badge/category-privacy-blue)
+![Depth Anything v2](https://img.shields.io/badge/model-Depth%20Anything%20v2-green)
+
+## How It Works
+
+```
+Camera Frame → Depth Anything v2 → Colorized Depth Map → Aegis Overlay
+   (BGR)         (monocular)         (warm=near, cool=far)    (0.5 FPS)
+```
+
+The depth model converts each frame into a distance map where **warm colors** (red/orange) indicate nearby objects and **cool colors** (blue/purple) indicate distant ones. This preserves enough spatial information to understand activity (someone approaching, car in driveway, etc.) without revealing identity.
+
+## Hardware Support
+
+Auto-detected via `HardwareEnv` from `skills/lib/env_config.py`:
+
+| Platform | Backend | Notes |
+|----------|---------|-------|
+| **NVIDIA** | CUDA | FP16 on GPU |
+| **AMD** | ROCm | PyTorch HIP |
+| **Apple Silicon** | MPS | Unified memory, leaves Neural Engine free |
+| **Intel** | OpenVINO | CPU + NPU support |
+| **CPU** | PyTorch | Fallback, slower |
+
+## Models
+
+| Model | Size | Speed | Quality |
+|-------|------|-------|---------|
+| `depth-anything-v2-small` | 25MB | Fast | Good (default) |
+| `depth-anything-v2-base` | 98MB | Medium | Better |
+| `depth-anything-v2-large` | 335MB | Slow | Best |
+
+Weights are downloaded from HuggingFace Hub on first run and cached locally.
+
+## Display Modes
+
+- **`depth_only`** (default) — Full anonymization. Only the depth map is shown.
+- **`overlay`** — Depth map blended on top of the original feed (adjustable opacity).
+- **`side_by_side`** — Original and depth map shown next to each other.
+
+## Setup
+
+```bash
+python3 -m venv .venv && source .venv/bin/activate
+pip install -r requirements.txt
+```
+
+## Integration with Aegis
+
+This skill communicates with Aegis via **JSONL over stdin/stdout**. Aegis sends frame events, the skill returns transformed frames (base64 JPEG). See [SKILL.md](SKILL.md) for the full protocol specification and the `TransformSkillBase` interface for building new privacy skills.
+
+## Creating New Privacy Skills
+
+Subclass `TransformSkillBase` and implement two methods:
+
+```python
+from transform_base import TransformSkillBase
+
+class MyPrivacySkill(TransformSkillBase):
+    def load_model(self, config):
+        self.model = load_my_model()
+        return {"model": "my-model", "device": self.device}
+
+    def transform_frame(self, image, metadata):
+        return self.model.anonymize(image)
+
+if __name__ == "__main__":
+    MyPrivacySkill().run()
+```
+
+The base class handles JSONL protocol, performance tracking, hardware detection, rate limiting, and graceful shutdown.