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FC mac-mini-llm-lab

Configure a Mac mini as a reliable local LLM server with remote access, observability, and power-safe operation. Use when building an always-on private AI inference server on Apple Silicon.

Not recommendedcritical or high security findings · low grade F
sickn33/agentic-awesome-skills Agent Skills author: sickn33 MIT 1 file body ≈ 2 273 tokens Open the sourcegithub.com↗ analyzed 5 d ago

Configure a Mac mini as a reliable local LLM server with remote access, observability, and power-safe operation.

As a process C 58/100 · Has gaps — weak spots: result and completion, when it triggers, running it twice

IntegrationDockerInfrastructuretype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
F
37/100
safety, quality, tests
Safety 60%
9
Quality 40%
80
Run on models
none yet
Process rating
C
58/100
Has gaps
Result and completion w 14
0
When it triggers w 12
20
Running it twice w 4
30
the three weakest of ten parameters · all ten

The same skill appears in 2 more places: agentic-awesome-skills, agentic-awesome-skills

What is at stake

The skill contains fragments that, in the wrong hands, cost money or data. Below: what the installer risks and what the author should do.

Dangerous commands
If you install

The skill contains commands that delete files, rewrite disks or execute code fetched from the network. The agent may run them without asking if it believes the instructions require it.

For the author

Replace destructive commands with safe equivalents that ask for confirmation, scope them to one folder, and stop piping curl into a shell: pin a version and a checksum.

How to improve

  1. Address the high-severity findings: each costs 18 safety points. If one is a false positive, add the rule id to guard.allow in spec.yaml.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 6

  • high Dangerous commands cmd-persistence SKILL.md:120
    Persistence mechanism (cron / launchd / scheduled task / autorun registry)
    <!-- ~/Library/LaunchAgents/com.ollama.serve.plist -->
  • high Dangerous commands cmd-persistence SKILL.md:157
    Persistence mechanism (cron / launchd / scheduled task / autorun registry)
    launchctl load ~/Library/LaunchAgents/com.ollama.serve.plist
  • high Dangerous commands cmd-persistence SKILL.md:163
    Persistence mechanism (cron / launchd / scheduled task / autorun registry)
    launchctl unload ~/Library/LaunchAgents/com.ollama.serve.plist
  • high Dangerous commands cmd-persistence SKILL.md:210
    Persistence mechanism (cron / launchd / scheduled task / autorun registry)
    sudo launchctl unload /System/Library/LaunchDaemons/ssh.plist
  • high Dangerous commands cmd-persistence SKILL.md:211
    Persistence mechanism (cron / launchd / scheduled task / autorun registry)
    sudo launchctl load /System/Library/LaunchDaemons/ssh.plist
Medium and low: 1
  • low Dangerous commands cmd-cron-mention SKILL.md:282
    Mentions editing / listing crontab
    # Add to crontab: crontab -e

Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • note frontmatter-key unknown frontmatter key "risk"
  • note frontmatter-key unknown frontmatter key "source"
  • note frontmatter-key unknown frontmatter key "source_repo"
  • note frontmatter-key unknown frontmatter key "source_type"
  • note frontmatter-key unknown frontmatter key "date_added"
  • note frontmatter-key unknown frontmatter key "license_source"

Process rating: all ten parameters 58/100

  • 0Result and completion. Does not say what the result is
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 1 mutating operations with no state check
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (bash) that frontmatter does not declare
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Steps. 14 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2273 tokens
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 16 top-level sections: this looks like several domains in one skill

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • -2localhost URLs: will not work for another user
  • +2Single-language instructions
  • +3Description length 189: enough signal without eating the budget
  • +4Structure: 21 headings
  • +3Step-by-step instructions: 14 items
  • +4Has examples (13 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 80.