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feat: rewrite config.yaml for Aegis config schema (model_size, fps, device selectors)
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# YOLO 2026 Detection Skill — Default Configuration
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# These values can be overridden via AEGIS_SKILL_PARAMS or CLI arguments.
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model_size: nano # nano | small | medium | large
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confidence: 0.5 # 0.1 – 1.0
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classes: "person,car,dog,cat" # Comma-separated COCO class names
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device: auto # auto | cpu | cuda | mps | rocm
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fps: 5 # 0.2 | 0.5 | 1 | 3 | 5 | 15
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# YOLO 2026 Detection Skill — Configuration Schema
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# Parsed by Aegis skill-registry-service.cjs → parseConfigYaml()
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# Format: params[] with key, type, label, default, description, options
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params:
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- key: model_size
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label: Model Size
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type: select
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default: nano
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description: "YOLO26 model variant — larger = more accurate but slower"
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options:
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- { value: nano, label: "Nano (fastest, ~2ms)" }
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- { value: small, label: "Small (balanced, ~5ms)" }
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- { value: medium, label: "Medium (accurate, ~12ms)" }
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- { value: large, label: "Large (most accurate, ~25ms)" }
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- key: confidence
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label: Confidence Threshold
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type: number
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default: 0.5
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description: "Minimum detection confidence (0.1–1.0)"
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- key: fps
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label: Frame Rate
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type: select
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default: 5
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description: "Detection processing rate — higher = more CPU/GPU usage"
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options:
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- { value: 0.2, label: "Ultra Low (0.2 FPS)" }
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- { value: 0.5, label: "Low (0.5 FPS)" }
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- { value: 1, label: "Normal (1 FPS)" }
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- { value: 3, label: "Active (3 FPS)" }
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- { value: 5, label: "High (5 FPS)" }
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- { value: 15, label: "Real-time (15 FPS)" }
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- key: classes
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label: Detection Classes
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type: string
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default: "person,car,dog,cat"
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description: "Comma-separated COCO class names to detect"
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- key: device
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label: Inference Device
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type: select
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default: auto
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description: "Compute backend for inference"
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options:
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- { value: auto, label: "Auto-detect" }
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- { value: cpu, label: "CPU" }
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- { value: cuda, label: "NVIDIA CUDA" }
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- { value: mps, label: "Apple Silicon (MPS)" }
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- { value: rocm, label: "AMD ROCm" }

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