# HomeSafe Bench

VLM indoor safety hazard detection benchmark inspired by HomeSafeBench (arXiv 2509.23690)

- Repository: https://github.com/SharpAI/DeepCamera (3124 stars)
- Category: Engineering
- License: MIT
- Last updated: 2026-09-17
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add SharpAI/DeepCamera --skill HomeSafe-Bench -g -a claude-code -y`
Codex: `npx skills add SharpAI/DeepCamera --skill HomeSafe-Bench -g -a codex -y`
Cursor: `npx skills add SharpAI/DeepCamera --skill HomeSafe-Bench -g -a cursor -y`
Gemini CLI: `npx skills add SharpAI/DeepCamera --skill HomeSafe-Bench -g -a gemini-cli -y`

## Description

VLM indoor safety hazard detection benchmark inspired by HomeSafeBench (arXiv 2509.23690)

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# HomeSafe-Bench, Indoor Safety Hazard Detection

VLM benchmark evaluating indoor home safety hazard detection from static camera frames. Inspired by [HomeSafeBench (arXiv 2509.23690)](https://arxiv.org/abs/2509.23690), adapted for **fixed indoor security cameras** with AI-generated fixture images.

**40 tests** across **5 hazard categories**:

| Category | Tests | Example Hazards |
|----------|-------|-----------------|
| 🔥 Fire / Smoke | 8 | Stove smoke, candle near curtain, iron left face-down |
| ⚡ Electrical | 8 | Overloaded power strip, exposed wire, frayed cord |
| 🚶 Trip / Fall | 8 | Toys on staircase, wet floor, loose rug edge |
| 👶 Child Safety | 8 | Open cabinet with chemicals, uncovered outlet, blind cord |
| 📦 Falling Objects | 8 | Heavy items on high shelf, leaning bookshelf, loose ceiling fan |

## Setup

**Requires `npm install`.** This skill has a `package.json` with dependencies (e.g. `openai` SDK). The deployment agent should check for `package.json` and run `npm install` in the skill directory before launching.

Entry script: `scripts/run-benchmark.cjs`

### Verification

```bash
node scripts/run-benchmark.cjs --help
```

## Quick Start

### As an Aegis Skill (automatic)

When spawned by Aegis, all configuration is injected via environment variables. The benchmark discovers your VLM server automatically, generates an HTML report, and opens it when complete.

### Standalone

```bash
# Run all 40 tests
node scripts/run-benchmark.cjs --vlm http://localhost:5405

# Quick mode (2 tests per category = 10 total)
node scripts/run-benchmark.cjs --vlm http://localhost:5405 --mode quick

# Skip report auto-open
node scripts/run-benchmark.cjs --vlm http://localhost:5405 --no-open
```

## Configuration

### Environment Variables (set by Aegis)

The rest: https://github.com/SharpAI/DeepCamera/blob/HEAD/skills/analysis/homesafe-bench/SKILL.md