Home Security AI Benchmark

Testing & Quality

LLM & VLM evaluation suite for home security AI applications

QUICK START

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Source SKILL.md: https://github.com/SharpAI/DeepCamera/blob/HEAD/skills/analysis/home-security-benchmark/SKILL.md

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Home Security AI Benchmark

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

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

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 LLM gateway and VLM server automatically, generates an HTML report, and opens it when complete.

Standalone

# LLM-only (VLM tests skipped)
node scripts/run-benchmark.cjs

# With VLM tests (base URL without /v1 suffix)
node scripts/run-benchmark.cjs --vlm http://localhost:5405

# Custom LLM gateway
node scripts/run-benchmark.cjs --gateway http://localhost:5407

# Skip report auto-open
node scripts/run-benchmark.cjs --no-open

Configuration

Environment Variables (set by Aegis)

Variable	Default	Description
`AEGIS_GATEWAY_URL`	`http://localhost:5407`	LLM gateway (OpenAI-compatible)
`AEGIS_LLM_URL`	—	Direct llama-server LLM endpoint
`AEGIS_LLM_API_TYPE`	`openai`	LLM provider type (builtin, openai, etc.)
`AEGIS_LLM_MODEL`	—	LLM model name
`AEGIS_LLM_API_KEY`	—	API key for cloud LLM providers
`AEGIS_LLM_BASE_URL`	—	Cloud provider base URL (e.g. `https://api.openai.com/v1`)
`AEGIS_VLM_URL`	(disabled)	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: URLs should be base URLs (e.g. http://localhost:5405). The benchmark appends /v1/chat/completions automatically. Including a /v1 suffix is also accepted — it will be stripped to avoid double-pathing.

User Configuration (config.yaml)

This skill includes a config.yaml that defines user-configurable parameters. Aegis parses this at install time and renders a config panel in the UI. Values are delivered via AEGIS_SKILL_PARAMS.

Parameter	Type	Default	Description
`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 for the full platform contract.

CLI Arguments (standalone fallback)

Argument	Default	Description
`--gateway URL`	`http://localhost:5407`	LLM gateway
`--vlm URL`	(disabled)	VLM server base URL
`--out DIR`	`~/.aegis-ai/benchmarks`	Results directory
`--report`	(auto in skill mode)	Force report generation
`--no-open`	—	Don't auto-open report in browser

Protocol

Aegis → Skill (env vars)

AEGIS_GATEWAY_URL=http://localhost:5407
AEGIS_VLM_URL=http://localhost:5405
AEGIS_SKILL_ID=home-security-benchmark
AEGIS_SKILL_PARAMS={}

Skill → Aegis (stdout, JSON lines)

{"event": "ready", "model": "Qwen3.5-4B-Q4_1", "system": "Apple M3"}
{"event": "suite_start", "suite": "Context Preprocessing"}
{"event": "test_result", "suite": "...", "test": "...", "status": "pass", "timeMs": 123}
{"event": "suite_end", "suite": "...", "passed": 4, "failed": 0}
{"event": "complete", "passed": 126, "total": 131, "timeMs": 322000, "reportPath": "/path/to/report.html"}

Human-readable output goes to stderr (visible in Aegis console tab).

Test Suites (143 Tests)

Suite	Tests	Domain
Context Preprocessing	6	Conversation dedup accuracy
Topic Classification	4	Topic extraction & change detection
Knowledge Distillation	5	Fact extraction, slug matching
Event Deduplication	8	Security event classification
Tool Use	16	Tool selection & parameter extraction
Chat & JSON Compliance	11	Persona, memory, structured output
Security Classification	12	Threat level assessment
Narrative Synthesis	4	Multi-camera event summarization
Prompt Injection Resistance	4	Adversarial prompt defense
Multi-Turn Reasoning	4	Context resolution over turns
Error Recovery & Edge Cases	4	Graceful failure handling
Privacy & Compliance	3	PII handling, consent
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	47	Frame entity detection & description (outdoor + indoor safety)

Results

Results are saved to ~/.aegis-ai/benchmarks/ as JSON. An HTML report with cross-model comparison is auto-generated and opened in the browser after each run.

Requirements

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 (47 tests)