{
  "version": "1.0.0",
  "repository": "SharpAI/DeepCamera",
  "updated": "2026-03-02",
  "description": "AI skill catalog for SharpAI Aegis. Each skill is a self-contained folder with a SKILL.md manifest.",
  "categories": {
    "detection": "Object detection, person recognition, visual grounding",
    "analysis": "VLM scene understanding, interactive segmentation",
    "transformation": "Depth estimation, style transfer, video effects",
    "privacy": "Privacy transforms — depth maps, blur, anonymization for blind mode",
    "annotation": "Dataset labeling, COCO export, training data",
    "segmentation": "Pixel-level object segmentation — SAM2, interactive masks",
    "training": "Model fine-tuning, hardware-optimized export, deployment",
    "camera-providers": "Camera brand integrations — clip feed, live stream",
    "streaming": "RTSP/WebRTC live view via go2rtc",
    "channels": "Messaging platform channels for Clawdbot agent",
    "automation": "MQTT, webhooks, Home Assistant triggers",
    "integrations": "Smart home and IoT platform bridges"
  },
  "skills": [
    {
      "id": "home-security-benchmark",
      "name": "Home Security AI Benchmark",
      "description": "LLM & VLM evaluation suite for home security AI — tests dedup, classification, tool use, and scene analysis.",
      "version": "1.0.0",
      "category": "analysis",
      "path": "skills/analysis/home-security-benchmark",
      "tags": [
        "benchmark",
        "llm",
        "vlm",
        "testing",
        "evaluation",
        "security"
      ],
      "platforms": [
        "linux-x64",
        "linux-arm64",
        "darwin-arm64",
        "darwin-x64",
        "win-x64"
      ],
      "requirements": {
        "node": ">=18",
        "ram_gb": 1
      },
      "capabilities": [
        "benchmark",
        "report_generation"
      ],
      "ui_unlocks": [
        "benchmark_report"
      ]
    },
    {
      "id": "yolo-detection-2026",
      "name": "YOLO 2026",
      "description": "State-of-the-art real-time object detection — 80+ COCO classes, bounding box overlays, multi-size model selection.",
      "version": "1.0.0",
      "category": "detection",
      "path": "skills/detection/yolo-detection-2026",
      "tags": [
        "detection",
        "yolo",
        "object-detection",
        "real-time",
        "coco"
      ],
      "platforms": [
        "linux-x64",
        "linux-arm64",
        "darwin-arm64",
        "darwin-x64",
        "win-x64"
      ],
      "requirements": {
        "python": ">=3.9",
        "ram_gb": 2
      },
      "capabilities": [
        "live_detection",
        "bbox_overlay"
      ],
      "ui_unlocks": [
        "detection_overlay",
        "detection_results"
      ],
      "fps_presets": [
        0.2,
        0.5,
        1,
        3,
        5,
        15
      ],
      "model_sizes": [
        "nano",
        "small",
        "medium",
        "large"
      ]
    },
    {
      "id": "camera-claw",
      "name": "Camera Claw",
      "description": "Security camera for your AI agent — sandbox, record, and monitor OpenClaw activity.",
      "version": "2026.3.12",
      "category": "integrations",
      "url": "https://github.com/SharpAI/CameraClaw",
      "repo_url": "https://github.com/SharpAI/CameraClaw",
      "code_structure": [
        {
          "path": "SKILL.md",
          "desc": "Aegis skill manifest (11 params)"
        },
        {
          "path": "package.json",
          "desc": "Node.js dependencies"
        },
        {
          "path": "config.yaml",
          "desc": "Default params"
        },
        {
          "path": "deploy.sh",
          "desc": "Node.js + Docker bootstrapper"
        },
        {
          "path": "deploy.bat",
          "desc": "Windows bootstrapper"
        },
        {
          "path": "scripts/monitor.js",
          "desc": "Main entry — Docker orchestrator + JSONL protocol"
        },
        {
          "path": "scripts/health-check.js",
          "desc": "Container health checker"
        },
        {
          "path": "docs/aegis_openclaw_note.md",
          "desc": "Aegis integration requirements"
        }
      ],
      "tags": [
        "security",
        "sandbox",
        "monitoring",
        "openclaw",
        "ai-agent"
      ],
      "platforms": [
        "linux-x64",
        "linux-arm64",
        "darwin-arm64",
        "darwin-x64",
        "win-x64"
      ],
      "requirements": {
        "docker": true
      },
      "capabilities": [
        "monitoring",
        "recording"
      ]
    },
    {
      "id": "depth-estimation",
      "name": "Depth Anything V2",
      "description": "Privacy-first depth map transforms — anonymize camera feeds with Depth Anything v2 while preserving spatial awareness.",
      "version": "1.1.0",
      "category": "privacy",
      "path": "skills/transformation/depth-estimation",
      "tags": [
        "privacy",
        "depth",
        "transform",
        "anonymization",
        "blind-mode"
      ],
      "platforms": [
        "linux-x64",
        "linux-arm64",
        "darwin-arm64",
        "darwin-x64",
        "win-x64"
      ],
      "requirements": {
        "python": ">=3.9",
        "ram_gb": 2
      },
      "capabilities": [
        "live_transform",
        "privacy_overlay"
      ],
      "ui_unlocks": [
        "privacy_overlay",
        "blind_mode"
      ]
    },
    {
      "id": "model-training",
      "name": "Model Training",
      "disabled": true,
      "description": "Agent-driven YOLO fine-tuning — annotate, train, auto-export to TensorRT/CoreML/OpenVINO, deploy as detection skill.",
      "version": "1.0.0",
      "category": "training",
      "path": "skills/training/model-training",
      "tags": [
        "training",
        "fine-tuning",
        "yolo",
        "custom-model",
        "export"
      ],
      "platforms": [
        "linux-x64",
        "linux-arm64",
        "darwin-arm64",
        "darwin-x64",
        "win-x64"
      ],
      "requirements": {
        "python": ">=3.9",
        "ram_gb": 4
      },
      "capabilities": [
        "fine_tuning",
        "model_export",
        "deployment"
      ]
    },
    {
      "id": "segmentation-sam2",
      "name": "SAM2 Segmentation",
      "disabled": true,
      "description": "Interactive click-to-segment using Segment Anything 2 — pixel-perfect masks, point/box prompts, video tracking.",
      "version": "1.0.0",
      "category": "segmentation",
      "path": "skills/segmentation/sam2-segmentation",
      "tags": [
        "annotation",
        "segmentation",
        "sam2",
        "labeling",
        "masks"
      ],
      "platforms": [
        "linux-x64",
        "linux-arm64",
        "darwin-arm64",
        "darwin-x64",
        "win-x64"
      ],
      "requirements": {
        "python": ">=3.9",
        "ram_gb": 4
      },
      "capabilities": [
        "interactive_segmentation",
        "video_tracking"
      ]
    },
    {
      "id": "annotation-data",
      "name": "Annotation Data",
      "disabled": true,
      "description": "Dataset annotation management — COCO labels, sequences, export, and Kaggle upload for Annotation Studio.",
      "version": "1.0.0",
      "category": "annotation",
      "path": "skills/annotation/dataset-management",
      "tags": [
        "annotation",
        "dataset",
        "coco",
        "labeling"
      ],
      "platforms": [
        "linux-x64",
        "linux-arm64",
        "darwin-arm64",
        "darwin-x64",
        "win-x64"
      ],
      "requirements": {
        "python": ">=3.9"
      },
      "capabilities": [
        "dataset_management",
        "coco_export"
      ],
      "ui_unlocks": [
        "annotation_studio"
      ]
    },
    {
      "id": "yolo-detection-2026-coral-tpu-macos",
      "name": "YOLO 2026 Coral TPU (macOS)",
      "description": "Google Coral Edge TPU natively via ai-edge-litert on macOS",
      "category": "detection",
      "path": "skills/detection/yolo-detection-2026-coral-tpu-macos",
      "tags": [
        "detection",
        "yolo",
        "coral",
        "edge-tpu"
      ],
      "platforms": [
        "darwin-arm64",
        "darwin-x64"
      ]
    },
    {
      "id": "yolo-detection-2026-coral-tpu-win-wsl",
      "name": "YOLO 2026 Coral TPU (Windows/WSL)",
      "description": "Google Coral Edge TPU natively mapped to WSL2",
      "category": "detection",
      "path": "skills/detection/yolo-detection-2026-coral-tpu-win-wsl",
      "tags": [
        "detection",
        "yolo",
        "coral",
        "edge-tpu",
        "wsl"
      ],
      "platforms": [
        "win-x64"
      ]
    }
  ]
}