feat(benchmark): add Indoor Safety Hazards VLM suite (12 tests)

Expand HomeSec-Bench VLM Scene Analysis from 35 to 47 tests with a new
Indoor Safety Hazards category covering fire, electrical, trip/fall,
child safety, and blocked exit scenarios using AI-generated indoor
security camera frames.

New test scenarios:
- Stove smoke, candle near curtain, space heater near drapes, iron left on
- Overloaded power strip, frayed electrical cord
- Toys on stairs, wet floor
- Person fallen, items on high shelf
- Open cabinet with chemicals
- Cluttered/blocked exit

Total benchmark: 131 → 143 tests
VLM suite: 35 → 47 tests
Version: 2.0.0 → 2.1.0

Author: solderzzc

README.md

@@ -42,7 +42,7 @@ Each skill is a self-contained module with its own model, parameters, and [commu
 | **Detection** | [`yolo-detection-2026`](skills/detection/yolo-detection-2026/) | Real-time 80+ class detection — auto-accelerated via TensorRT / CoreML / OpenVINO / ONNX | ✅|
 | | [`dinov3-grounding`](skills/detection/dinov3-grounding/) | Open-vocabulary detection — describe what to find | 📐 |
 | | [`person-recognition`](skills/detection/person-recognition/) | Re-identify individuals across cameras | 📐 |
-| **Analysis** | [`home-security-benchmark`](skills/analysis/home-security-benchmark/) | [131-test evaluation suite](#-homesec-bench--how-secure-is-your-local-ai) for LLM & VLM security performance | ✅ |
+| **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 | ✅ |
 | | [`vlm-scene-analysis`](skills/analysis/vlm-scene-analysis/) | Describe what happened in recorded clips | 📐 |
 | | [`sam2-segmentation`](skills/analysis/sam2-segmentation/) | Click-to-segment with pixel-perfect masks | 📐 |
 | **Transformation** | [`depth-estimation`](skills/transformation/depth-estimation/) | Monocular depth maps with Depth Anything v2 | 📐 |
@@ -140,7 +140,7 @@ Camera → Frame Governor → detect.py (JSONL) → Aegis IPC → Live Overlay
 
 ## 📊 HomeSec-Bench — How Secure Is Your Local AI?
 
-**HomeSec-Bench** is a 131-test security benchmark that measures how well your local AI performs as a security guard. It tests what matters: Can it detect a person in fog? Classify a break-in vs. a delivery? Resist prompt injection? Route alerts correctly at 3 AM?
+**HomeSec-Bench** is a 143-test security benchmark that measures how well your local AI performs as a security guard. It tests what matters: Can it detect a person in fog? Classify a break-in vs. a delivery? Resist prompt injection? Route alerts correctly at 3 AM?
 
 Run it on your own hardware to know exactly where your setup stands.
 

skills/analysis/home-security-benchmark/SKILL.md

@@ -1,7 +1,7 @@
 ---
 name: Home Security AI Benchmark
 description: LLM & VLM evaluation suite for home security AI applications
-version: 2.0.0
+version: 2.1.0
 category: analysis
 runtime: node
 entry: scripts/run-benchmark.cjs
@@ -15,7 +15,7 @@ requirements:
 
 # Home Security AI Benchmark
 
-Comprehensive benchmark suite evaluating LLM and VLM models on **131 tests** across **16 suites** — context preprocessing, tool use, security classification, prompt injection resistance, alert routing, knowledge injection, VLM-to-alert triage, and scene analysis.
+Comprehensive benchmark suite evaluating LLM and VLM models on **143 tests** across **16 suites** — context preprocessing, tool use, security classification, prompt injection resistance, alert routing, knowledge injection, VLM-to-alert triage, and scene analysis.
 
 ## Setup
 
@@ -76,7 +76,7 @@ This skill includes a [`config.yaml`](config.yaml) that defines user-configurabl
 
 | Parameter | Type | Default | Description |
 |-----------|------|---------|-------------|
-| `mode` | select | `llm` | Which suites to run: `llm` (96 tests), `vlm` (35 tests), or `full` (131 tests) |
+| `mode` | select | `llm` | Which suites to run: `llm` (96 tests), `vlm` (47 tests), or `full` (143 tests) |
 | `noOpen` | boolean | `false` | Skip auto-opening the HTML report in browser |
 
 Platform parameters like `AEGIS_GATEWAY_URL` and `AEGIS_VLM_URL` are auto-injected by Aegis — they are **not** in `config.yaml`. See [Aegis Skill Platform Parameters](../../../docs/skill-params.md) for the full platform contract.
@@ -112,7 +112,7 @@ AEGIS_SKILL_PARAMS={}
 
 Human-readable output goes to **stderr** (visible in Aegis console tab).
 
-## Test Suites (131 Tests)
+## Test Suites (143 Tests)
 
 | Suite | Tests | Domain |
 |-------|-------|--------|
@@ -131,7 +131,7 @@ Human-readable output goes to **stderr** (visible in Aegis console tab).
 | Alert Routing & Subscription | 5 | Channel targeting, schedule CRUD |
 | Knowledge Injection to Dialog | 5 | KI-personalized responses |
 | VLM-to-Alert Triage | 5 | Urgency classification from VLM |
-| VLM Scene Analysis | 35 | Frame entity detection & description |
+| VLM Scene Analysis | 47 | Frame entity detection & description (outdoor + indoor safety) |
 
 ## Results
 
@@ -142,4 +142,4 @@ Results are saved to `~/.aegis-ai/benchmarks/` as JSON. An HTML report with cros
 - Node.js ≥ 18
 - `npm install` (for `openai` SDK dependency)
 - Running LLM server (llama-server, OpenAI API, or any OpenAI-compatible endpoint)
-- Optional: Running VLM server for scene analysis tests (35 tests)
+- Optional: Running VLM server for scene analysis tests (47 tests)