Skill Reference
Skills are organized by category and tagged with their requirements. This page provides detailed technical specifications for every skill available in the Aegis AI marketplace.
Detection Skills
YOLO 2026 Object Detection
Real-time object detection using the latest YOLO architecture. Detects 80+ COCO object classes with colored bounding boxes, class labels, and confidence scores rendered directly on camera feeds.
| Specification | Value |
|---|---|
| Category | Detection |
| Platforms | macOS (Apple Silicon + Intel), Windows (x64), Linux (x64 + ARM64) |
| Requirements | Python 3.9+ |
| Unlocks | Detection Studio view, detection overlay on Monitor tiles |
Model sizes: Choose the right tradeoff for your hardware:
| Size | Speed | Accuracy | Best For |
|---|---|---|---|
| Nano | Fastest | Good | CPU-only systems, Raspberry Pi, resource-constrained setups |
| Small | Fast | Better | General use, balanced speed and accuracy |
| Medium | Moderate | High | GPU users who want detailed detections |
| Large | Slowest | Highest | Maximum accuracy, dedicated GPU recommended |
Adjustable settings:
- Confidence threshold (0–1) — minimum detection confidence to display a bounding box. Higher values reduce false positives.
- Processing interval — how often frames are analyzed (presets from 0.2 to 15 fps)
80+ COCO classes include: Person, bicycle, car, motorcycle, bus, truck, dog, cat, bird, backpack, umbrella, handbag, suitcase, bottle, cup, fork, knife, laptop, phone, book, clock, and many more.
Privacy / Transformation Skills
Depth Estimation (Privacy)
Privacy-first depth map transforms using Depth Anything v2. Converts camera feeds into colorized depth maps where near objects appear warm and far objects appear cool. In "depth only" mode, the scene is fully anonymized — you can monitor spatial activity without revealing anyone's identity.
| Specification | Value |
|---|---|
| Category | Privacy |
| Platforms | macOS (Apple Silicon + Intel), Windows (x64), Linux (x64 + ARM64) |
| Requirements | Python 3.9+ |
| Unlocks | 3D Depth Vision view |
Display modes:
| Mode | Description |
|---|---|
| Depth Only | Full anonymization — shows only the depth map, no identifiable visual information |
| Overlay | Depth map blended over the original image at adjustable opacity (0.0 to 1.0) |
| Side by Side | Original camera feed and depth map shown adjacent for comparison |
Colormaps: Inferno, Viridis, Plasma, Magma, Jet, Turbo, Hot, Cool — each provides a different visual style for depth visualization.
Hardware acceleration:
- macOS (Apple Silicon): Uses CoreML on the Neural Engine — extremely fast, leaves GPU free
- Windows/Linux (NVIDIA): Uses CUDA for GPU acceleration
- CPU fallback: Works on any system, but slower
Depth models available:
depth-anything-v2-small— fastest, smallest memory footprintdepth-anything-v2-base— balanced quality and speeddepth-anything-v2-large— highest quality depth estimation
Segmentation Skills
SAM2 Segmentation
Interactive segmentation using Meta's Segment Anything 2. Click any point on a camera frame, or draw a bounding box, to get pixel-perfect segmentation masks. SAM2 excels at separating objects from backgrounds with surgical precision.
| Specification | Value |
|---|---|
| Category | Segmentation |
| Platforms | macOS (Apple Silicon + Intel), Windows (x64), Linux (x64 + ARM64) |
| Requirements | Python 3.9+ |
| Unlocks | Segmentation Studio view |
Capabilities:
- Point prompts — click a single point on an object to segment it
- Box prompts — draw a rectangle around an area to segment everything inside
- Video tracking — select an object once and SAM2 tracks it across consecutive frames
- Multi-object — segment multiple objects simultaneously with different masks
Annotation Skills
Annotation Data
Dataset annotation management for building custom training datasets from your camera footage.
| Specification | Value |
|---|---|
| Category | Annotation |
| Platforms | macOS (Apple Silicon + Intel), Windows (x64), Linux (x64 + ARM64) |
| Requirements | Python 3.9+ |
| Unlocks | Annotation Studio view |
Capabilities:
- Import camera frames for annotation
- COCO-format label management
- Organize annotations into sequences
- Export datasets for model training
- Upload to Kaggle for sharing and collaboration
Training Skills
Model Training
Agent-driven YOLO fine-tuning workflow. Train custom detection models on your own camera data so the detector learns objects specific to your environment.
| Specification | Value |
|---|---|
| Category | Training |
| Platforms | macOS (Apple Silicon + Intel), Windows (x64), Linux (x64 + ARM64) |
| Requirements | Python 3.9+ |
Export targets:
- TensorRT — optimized for NVIDIA GPUs
- CoreML — optimized for Apple Silicon
- OpenVINO — optimized for Intel hardware
- ONNX — universal cross-platform format
Workflow:
- Annotate your camera data using the Annotation Studio
- The agent guides you through training configuration
- Training runs on your local hardware with progress reporting
- Trained models are automatically exported in the optimal format for your GPU
- Deploy the trained model as a custom detection skill
Analysis Skills
Home Security AI Benchmark
Evaluation suite for testing LLM and VLM performance on real home security tasks.
| Specification | Value |
|---|---|
| Category | Analysis |
| Platforms | macOS (Apple Silicon + Intel), Windows (x64), Linux (x64 + ARM64) |
| Requirements | Node.js 18+ |
Tests include:
- Deduplication accuracy — can the model correctly identify duplicate events?
- Scene classification — how accurately does the model categorize activity types?
- Tool usage — does the model correctly invoke tools when needed?
- Description quality — how detailed and accurate are scene descriptions?
Integration Skills
Camera Claw
Security camera integration for OpenClaw robotic arm systems. Sandbox, record, and monitor OpenClaw activity with full AI observation.
| Specification | Value |
|---|---|
| Category | Integrations |
| Platforms | macOS (Apple Silicon + Intel), Windows (x64), Linux (x64 + ARM64) |
| Requirements | Docker |
| Unlocks | OpenClaw view in sidebar |
Building Your Own Skills
Skills are Python packages that follow a standard interface. If you know Python and have experience with ML models, you can create your own:
- Study an existing skill from the SharpAI GitHub as a template
- Follow the standard skill structure (entrypoint, model loading, frame processing)
- Host it in a Git repository
- Add that repository URL in the Skill Source Browser (gear icon in Skills panel)
- Your skill appears in the Marketplace and can be installed like any other
The skill specification supports configurable parameters, multiple platform targets, and custom capabilities. Community-created skills go through the same install, start, stop, and remove lifecycle as official skills.