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 smarthome-benchgit 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/smarthome-bench)<a href="https://agentmods.dev/skills/sharpai/deepcamera/smarthome-bench"><img src="https://agentmods.dev/badge/skills/sharpai/deepcamera/smarthome-bench/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/smarthome-bench"><img src="https://agentmods.dev/badge/skills/sharpai/deepcamera/smarthome-bench.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 | $0.00020 | $0.01610 |
| Opus 5.5 | $0.00008 | $0.00644 |
| Sonnet 5.5 | $0.00004 | $0.00322 |
| Haiku 4.5 | $0.00002 | $0.00161 |
Grade A, and why
SmartHome Video Anomaly Benchmark 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.
How it starts
The opening of the file, as written. The whole thing — 160 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SmartHome Video Anomaly Benchmark
Benchmark suite evaluating VLM models on video anomaly detection across 7 smart home categories — based on the SmartHome-Bench dataset (1,203 YouTube smart home camera clips).
Key differentiator from HomeSec-Bench: This benchmark requires video understanding (multi-frame sampling), not single-frame analysis.
Setup
Requires npm install + system dependencies. The deployment agent should:
- Check for
yt-dlpandffmpegin PATH - Run
npm installin the skill directory
Entry script: scripts/run-benchmark.cjs
Verification
node scripts/run-benchmark.cjs --help
Quick Start
As an Aegis Skill (automatic)
When spawned by Aegis, configuration is injected via environment variables. The benchmark downloads video clips, samples frames, evaluates with VLM, and generates an HTML report.
Standalone
# Run with local VLM (subset mode, 50 videos)
node scripts/run-benchmark.cjs --vlm http://localhost:5405
# Quick test with 10 videos
node scripts/run-benchmark.cjs --vlm http://localhost:5405 --max-videos 10
# Full benchmark (all curated clips)
node scripts/run-benchmark.cjs --vlm http://localhost:5405 --mode full
# Filter by category
node scripts/run-benchmark.cjs --vlm http://localhost:5405 --categories "Wildlife,Security"
# Skip download (re-evaluate cached videos)
node scripts/run-benchmark.cjs --vlm http://localhost:5405 --skip-download
# Skip report auto-open
node scripts/run-benchmark.cjs --vlm http://localhost:5405 --no-open
Configuration
Environment Variables (set by Aegis)
| Variable | Default | Description |
|---|---|---|
AEGIS_VLM_URL |
(required) | VLM server base URL |
AEGIS_VLM_MODEL |
— | Loaded VLM model ID |
AEGIS_SKILL_ID |
— | Skill identifier (enables skill mode) |
AEGIS_SKILL_PARAMS |
{} |
JSON params from skill config |
Note: This is a VLM-only benchmark. An LLM gateway is not required.
What ships with it
8 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 · 160 lines · 20 tokens per session scan A b5de7095d58a
SmartHome Video Anomaly Benchmark is a skill published in the GitHub repository SharpAI/DeepCamera (3,100 stars, last pushed 1mo ago), licensed MIT. It adds 20 tokens to every session and 1,610 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.
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