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name Home Security AI Benchmark
description LLM & VLM evaluation suite for home security AI applications
version 2.0.0
category analysis
runtime node
entry scripts/run-benchmark.cjs
install npm

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.

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 (35 tests), or full (131 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 (131 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 35 Frame entity detection & description

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 (35 tests)