SharpAI/DeepCamera is an open-source camera platform that analyzes surveillance video locally with vision-language models, object detection, and related AI skills. It is for home and CCTV monitoring, and its catalogue skills add pluggable camera capabilities to the platform.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add SharpAI/DeepCamera --skill yolo-detection-2026-openvinogit clone --depth 1 https://github.com/SharpAI/DeepCameraWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/sharpai/deepcamera/yolo-detection-2026-openvino)<a href="https://agentmods.dev/skills/sharpai/deepcamera/yolo-detection-2026-openvino"><img src="https://agentmods.dev/badge/skills/sharpai/deepcamera/yolo-detection-2026-openvino.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00030 | $0.01529 |
| Opus 5 | $0.00015 | $0.00764 |
| Sonnet 5 | $0.00006 | $0.00306 |
| Haiku 4.5 | $0.00003 | $0.00153 |
Grade A, and why
yolo-detection-2026-openvino scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 177 lines — stays where its author put it; the contents beside it link to each section on GitHub.
OpenVINO Object Detection
Real-time object detection using Intel OpenVINO runtime. Runs inside Docker for cross-platform support. Supports Intel NCS2 USB stick, Intel integrated GPU, Intel Arc discrete GPU, and any x86_64 CPU.
Requirements
- Docker Desktop 4.35+ (all platforms)
- Optional hardware: Intel NCS2 USB, Intel iGPU, Intel Arc GPU
- Falls back to CPU if no accelerator present
How It Works
┌─────────────────────────────────────────────────────┐
│ Host (Aegis-AI) │
│ frame.jpg → /tmp/aegis_detection/ │
│ stdin ──→ ┌──────────────────────────────┐ │
│ │ Docker Container │ │
│ │ detect.py │ │
│ │ ├─ loads OpenVINO IR model │ │
│ │ ├─ reads frame from volume │ │
│ │ └─ runs inference on device │ │
│ stdout ←── │ → JSONL detections │ │
│ └──────────────────────────────┘ │
│ USB ──→ /dev/bus/usb (NCS2) │
│ DRI ──→ /dev/dri (Intel GPU) │
└─────────────────────────────────────────────────────┘
- Aegis writes camera frame JPEG to shared
/tmp/aegis_detection/volume - Sends
frameevent via stdin JSONL to Docker container detect.pyreads frame, runs inference via OpenVINO- Returns
detectionsevent via stdout JSONL - Same protocol as
yolo-detection-2026— Aegis sees no difference
Platform Setup
Linux
# Intel GPU and NCS2 auto-detected via /dev/dri and /dev/bus/usb
# Docker uses --device flags for direct device access
./deploy.sh
macOS (Docker Desktop 4.35+)
# Docker Desktop USB/IP handles NCS2 passthrough
# CPU fallback always available
./deploy.sh
Windows
# Docker Desktop 4.35+ with USB/IP support
# Or WSL2 backend with usbipd-win for NCS2
.\deploy.bat
Model
Ships without a pre-compiled model by default. On first run, detect.py will auto-download yolo26n.pt and export to OpenVINO IR format. To pre-export:
What ships with it
11 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- config.yaml 2.0 KB
- deploy.bat 3.2 KB runs code
- deploy.sh 5.7 KB runs code
- docker-compose.yml 975 B
- Dockerfile 1.9 KB
- models/README.md 540 B
- requirements.txt 228 B
- scripts/compile_model_colab.py 3.0 KB runs code
- scripts/compile_model.py 3.9 KB runs code
- scripts/detect.py 11 KB runs code
- scripts/device_probe.py 2.7 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 177 lines · 30 tokens per session scan A 975970dab60e
yolo-detection-2026-openvino is a skill published in the GitHub repository SharpAI/DeepCamera (3,047 stars, last pushed 2mo ago), licensed MIT. It adds 30 tokens to every session and 1,529 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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