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 BagelHole/DevOps-Security-Agent-Skills --skill mac-mini-llm-labgit clone --depth 1 https://github.com/BagelHole/DevOps-Security-Agent-SkillsWrote 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/bagelhole/devops-security-agent-skills/mac-mini-llm-lab)<a href="https://agentmods.dev/skills/bagelhole/devops-security-agent-skills/mac-mini-llm-lab"><img src="https://agentmods.dev/badge/skills/bagelhole/devops-security-agent-skills/mac-mini-llm-lab/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/bagelhole/devops-security-agent-skills/mac-mini-llm-lab"><img src="https://agentmods.dev/badge/skills/bagelhole/devops-security-agent-skills/mac-mini-llm-lab.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00045 | $0.02422 |
| Opus 5 | $0.00023 | $0.01211 |
| Sonnet 5 | $0.00009 | $0.00484 |
| Haiku 4.5 | $0.00005 | $0.00242 |
Grade D, and why
mac-mini-llm-lab scanned grade D with 3 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Asks for rootmediumPrivilege escalation
A mod that escalates privileges can change anything on the machine, not only the project.
sudo pmset -a disablesleep 1 Downloads and executes remote codehighSupply chain
curl | sh runs whatever the server returns today, which is not necessarily what it returned when this was reviewed.
curl -s http://localhost:11434/api/ps | python3 -m json.tool Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)" How it starts
The opening of the file, as written. The whole thing — 334 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Mac mini LLM Lab
Turn a Mac mini into a low-noise, always-on local AI appliance.
When to Use This Skill
Use this skill when:
- Setting up a dedicated local LLM inference server
- Building a private AI development environment
- Need always-on model serving without cloud costs
- Running models that require Apple Silicon unified memory (32-192GB)
- Creating a home lab AI server for a small team
Prerequisites
- Mac mini with Apple Silicon (M2/M3/M4, 16GB+ unified memory recommended)
- macOS Sonoma 14+ or Sequoia 15+
- Ethernet connection (recommended over Wi-Fi)
- UPS for power protection (optional but recommended)
Initial System Setup
# Update macOS
softwareupdate --install --all
# Install Xcode command-line tools
xcode-select --install
# Install Homebrew
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
# Core packages
brew install tmux htop btop wget jq git neovim
# Python environment (for MLX and custom scripts)
brew install [email protected] uv
# Monitoring
brew install prometheus node_exporter
Ollama Setup
# Install Ollama
brew install ollama
# Pull models based on your RAM
# 16GB Mac mini:
ollama pull llama3.1:8b
ollama pull nomic-embed-text
ollama pull codellama:7b
# 32GB Mac mini:
ollama pull llama3.1:8b
ollama pull qwen2.5:14b
ollama pull deepseek-coder-v2:16b
ollama pull nomic-embed-text
# 64GB+ Mac mini:
ollama pull llama3.1:70b
ollama pull qwen2.5:32b
ollama pull codellama:34b
# Verify Metal acceleration
ollama run llama3.1:8b --verbose
# Look for: "metal" in output
MLX Framework (Apple Silicon Native)
MLX runs models natively on Apple Silicon with excellent performance:
# Install MLX
uv pip install mlx mlx-lm
# Run a model
python3 -c "
from mlx_lm import load, generate
model, tokenizer = load('mlx-community/Llama-3.1-8B-Instruct-4bit')
response = generate(model, tokenizer, prompt='Explain Docker in 3 sentences', max_tokens=200)
print(response)
"
# MLX server (OpenAI-compatible API)
uv pip install mlx-lm[server]
mlx_lm.server --model mlx-community/Llama-3.1-8B-Instruct-4bit --port 8080
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.
- 8d ago First seen · 334 lines · 45 tokens per session scan D 8c753fb7ef12
mac-mini-llm-lab is a skill published in the GitHub repository BagelHole/DevOps-Security-Agent-Skills (1,084 stars, last pushed 3mo ago), licensed MIT. It adds 45 tokens to every session and 2,422 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it D with 3 findings (asks for root, downloads and executes remote code, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
spark-environment-setup
Set up a working ML training/inference environment on NVIDIA DGX Spark (GB10, aarch64, CUDA 13). Use when installing PyTorch/Unsloth/TRL/vLLM on DGX Spark, hitting libcudart or wheel-ABI errors on aarch64, or choosing between NGC containers and bare pip installs.
spark-memory-thermal-ops
Manage unified memory and thermals during long-running ML jobs on NVIDIA DGX Spark. Use when planning memory headroom for a training run on GB10, when a job OOMs on unified memory, or when monitoring temperature and power during multi-hour training.
spark-training-gotchas
Preflight and diagnose the ten known failure modes for ML training on NVIDIA DGX Spark. Use when a training run on DGX Spark fails to start, OOMs below the 128GB limit, slows down mid-run, or before any multi-hour training job on GB10.
amc-run-video-calibration
Calibrates pre-recorded cam.mp4 datasets through the AutoMagicCalib REST API. Use for user-supplied local MP4s; route live RTSP streams to amc-run-rtsp-calibration.
amc-run-rtsp-calibration
Calibrate a new dataset from live RTSP camera streams via the AutoMagicCalib REST API. Use when the user provides RTSP URLs or asks to calibrate live cameras; VIOS records clips, AMC ingests them, then runs calibration.
deepstream-generate-pipeline
Build DeepStream GStreamer pipelines interactively. Use when the user asks about pipelines for video/image inference, detection, tracking, or streaming — including natural phrases like 'pipeline to infer on image', 'run inference on video', 'detect objects in stream', 'save inference output', 'deepstream pipeline'…