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Merge pull request #148 from SharpAI/develop
chore: remove third-party benchmarks from skill registry and README
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README.md

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@@ -70,8 +70,6 @@ Each skill is a self-contained module with its own model, parameters, and [commu
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|----------|-------|--------------|:------:|
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| **Detection** | [`yolo-detection-2026`](skills/detection/yolo-detection-2026/) | Real-time 80+ class detection — auto-accelerated via TensorRT / CoreML / OpenVINO / ONNX ||
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| **Analysis** | [`home-security-benchmark`](skills/analysis/home-security-benchmark/) | [143-test evaluation suite](#-homesec-bench--how-secure-is-your-local-ai) for LLM & VLM security performance ||
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| | [`smarthome-bench`](skills/analysis/smarthome-bench/) | Video anomaly detection benchmark — 105 clips across 7 smart home categories ||
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| | [`homesafe-bench`](skills/analysis/homesafe-bench/) | Indoor safety hazard detection — 40 tests across 5 categories ||
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| | [`sam2-segmentation`](skills/analysis/sam2-segmentation/) | Click-to-segment with pixel-perfect masks | 📐 |
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| **Transformation** | [`depth-estimation`](skills/transformation/depth-estimation/) | Monocular depth maps with Depth Anything v2 | 📐 |
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| **Annotation** | [`dataset-annotation`](skills/annotation/dataset-annotation/) | AI-assisted labeling → COCO export | 📐 |

skills.json

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]
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},
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{
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"id": "smarthome-bench",
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"name": "SmartHome Video Anomaly Benchmark",
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"description": "VLM evaluation suite for video anomaly detection in smart home camera footage — 7 categories, 105 curated clips from SmartHome-Bench.",
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"version": "1.0.0",
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"category": "analysis",
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"path": "skills/analysis/smarthome-bench",
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"tags": [
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"benchmark",
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"vlm",
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"video",
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"anomaly-detection",
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"smart-home"
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"id": "camera-claw",
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"name": "Camera Claw",
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"description": "Security camera for your AI agent — sandbox, record, and monitor OpenClaw activity.",
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"version": "1.1.0",
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"category": "integrations",
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"repo_url": "https://github.com/SharpAI/CameraClaw",
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"code_structure": [
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{ "path": "SKILL.md", "desc": "Aegis skill manifest (11 params)" },
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{ "path": "package.json", "desc": "Node.js dependencies" },
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{ "path": "config.yaml", "desc": "Default params" },
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{ "path": "deploy.sh", "desc": "Node.js + Docker bootstrapper" },
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{ "path": "deploy.bat", "desc": "Windows bootstrapper" },
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{ "path": "scripts/monitor.js", "desc": "Main entry — Docker orchestrator + JSONL protocol" },
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{ "path": "scripts/health-check.js", "desc": "Container health checker" },
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{ "path": "docs/aegis_openclaw_note.md", "desc": "Aegis integration requirements" }
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],
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"tags": ["security", "sandbox", "monitoring", "openclaw"],
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"platforms": [
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"linux-x64",
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"linux-arm64",
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"win-x64"
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],
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"requirements": {
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"node": ">=18",
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"ram_gb": 2,
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"system_deps": [
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"yt-dlp",
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"ffmpeg"
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]
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"docker": true
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},
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"capabilities": [
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"benchmark",
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"report_generation"
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],
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"ui_unlocks": [
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"benchmark_report"
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]
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},
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{
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"id": "homesafe-bench",
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"name": "HomeSafe Indoor Safety Benchmark",
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"description": "VLM evaluation suite for indoor home safety hazard detection — 40 tests across 5 categories: fire/smoke, electrical, trip/fall, child safety, falling objects.",
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"version": "1.0.0",
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"category": "analysis",
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"path": "skills/analysis/homesafe-bench",
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"tags": [
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"benchmark",
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"vlm",
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"safety",
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"hazard",
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"indoor"
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],
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"platforms": [
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"linux-x64",
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"linux-arm64",
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"darwin-arm64",
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"darwin-x64",
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"win-x64"
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],
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"requirements": {
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"node": ">=18",
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"ram_gb": 2
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},
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"capabilities": [
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"benchmark"
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],
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"ui_unlocks": [
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"benchmark_report"
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"monitoring",
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"recording"
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]
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}
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]

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