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 dataset-managementgit 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/dataset-management)<a href="https://agentmods.dev/skills/sharpai/deepcamera/dataset-management"><img src="https://agentmods.dev/badge/skills/sharpai/deepcamera/dataset-management/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/sharpai/deepcamera/dataset-management"><img src="https://agentmods.dev/badge/skills/sharpai/deepcamera/dataset-management.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00019 | $0.00565 |
| Opus 5.5 | $0.00008 | $0.00226 |
| Sonnet 5.5 | $0.00004 | $0.00113 |
| Haiku 4.5 | $0.00002 | $0.00056 |
Grade A, and why
annotation-data 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 1mo 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.
What it actually says
Annotation Data Management
Manages annotation datasets for Aegis Annotation Studio. Handles dataset CRUD, label management, COCO-format export, and Kaggle upload.
Protocol (stdin/stdout JSONL)
Aegis → Skill
{"command": "list_datasets", "request_id": "req_001"}
{"command": "get_dataset", "name": "my_dataset", "request_id": "req_002"}
{"command": "save_dataset", "name": "my_dataset", "labels": [...], "request_id": "req_003"}
{"command": "delete_dataset", "name": "my_dataset", "request_id": "req_004"}
{"command": "save_annotation", "dataset": "my_dataset", "frame_id": "f1", "annotations": [...], "request_id": "req_005"}
{"command": "list_labels", "dataset": "my_dataset", "request_id": "req_006"}
{"command": "export_coco", "dataset": "my_dataset", "request_id": "req_007"}
{"command": "get_stats", "dataset": "my_dataset", "request_id": "req_008"}
{"command": "stop"}
Skill → Aegis
{"event": "annotation", "type": "ready", "request_id": "", "data": {"version": "1.0.0"}}
{"event": "annotation", "type": "datasets", "request_id": "req_001", "data": [...]}
{"event": "annotation", "type": "dataset", "request_id": "req_002", "data": {...}}
{"event": "annotation", "type": "saved", "request_id": "req_005", "data": {"frame_id": "f1", "count": 3}}
{"event": "annotation", "type": "exported", "request_id": "req_007", "data": {"path": "/path/to/coco.json"}}
What ships with it
4 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.
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.
- 1mo ago First seen · 52 lines · 19 tokens per session scan A c9d454dd5d48
annotation-data is a skill published in the GitHub repository SharpAI/DeepCamera (3,100 stars, last pushed 1mo ago), licensed MIT. It adds 19 tokens to every session and 565 once invoked, about $0.0001 per session on Opus 5.5. 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.
Other skills, from other repositories
flow-nexus-neural
Train and deploy neural networks in distributed E2B sandboxes with Flow Nexus.
physicsnemo-shard-tensor
Official NVIDIA-authored guidance for PhysicsNeMo ShardTensor domain parallelism — integrate domain parallelism into training/inference scripts (new or existing) with DDP or FSDP2, write and register shard patches to enable new layers/ops, and bootstrap multi-GPU correctness tests. Use when working with ShardTensor…
physicsnemo-discover
Official NVIDIA-authored guidance for navigating PhysicsNeMo — pick the model, datapipe, or example for a SciML/AI4Science task (surrogates, forecasting, downscaling, physics-informed, inverse, generative). Points at existing files via live repo search; never writes code. Do NOT use for installation or environment…
perforatedai
Expert in PerforatedAI library for adding artificial dendrites to PyTorch neural networks. Triggers: 'Perforate my model' (start interactive setup), 'debug my perforated model' (debug/optimize existing integration), 'load my perforated model for inference' (deploy trained models), 'export my perforated model'…
perforatedai-analyze
Analyze PerforatedAI training results and provide optimization recommendations. Trigger: 'Analyze my perforated results' (after training completes). Reviews CSV outputs, identifies performance patterns, recommends configuration improvements. For initial setup or debugging, use the perforatedai skill instead.
perforatedai-distributed
Multi-GPU setup for PerforatedAI with DataParallel or DistributedDataParallel (DDP). Invoked automatically by the perforatedai skill when multi-GPU training is detected. Handles initialization workflow, checkpoint loading, rank 0 handling, and shell script generation for DDP.