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BD llm-deploy-helper

Detect your hardware and get the perfect local LLM setup command in one line. Auto-detects RAM, VRAM, GPU, CPU — matches 15+ models against your hardware — generates ready Ollama + llama.cpp commands. No more guessing what fits.

ClawHub Hermes author: Maya Tao v1.0.0 MIT-0 9 files body ≈ 429 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

Detect your hardware and get the perfect local LLM setup command in one line.

As a process D 47/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerGitHubAI and agentsInfrastructuretype and topics are labelled automatically from the skill text
Runs in: OpenClaw Hermes Agent
JSON
Technical rating
B
78/100
safety, quality, tests
Safety 60%
86
Quality 40%
67
Run on models
none yet
Process rating
D
47/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Dangerous commands medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

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. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  2. For Hermes the description must be one sentence under 60 characters; move the conditions to a "When to Use" section.
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

✓ No critical or high findings

Medium and low: 6
  • medium Dangerous commands cmd-privilege README_EN.md:78
    Privilege escalation / world-writable permissions
    sudo cp ollama-llm.service /etc/systemd/system/
  • medium Dangerous commands cmd-privilege README.md:109
    Privilege escalation / world-writable permissions
    sudo cp ollama-llm.service /etc/systemd/system/
  • low Dangerous commands cmd-privilege llm_deploy_helper/cli.py:186
    Privilege escalation / world-writable permissions (string literal in code, not executed)
    console.print(f"[dim]  sudo cp {output} /etc/systemd/system/[/dim]")
    code literal
  • low Dangerous commands cmd-background-process llm_deploy_helper/cli.py:188
    Starts a background / autostarted process (string literal in code, not executed)
    console.print(f"[dim]  sudo systemctl enable --now {Path(output).stem}[/dim]")
    code literal
  • low Dangerous commands cmd-background-process README_EN.md:80
    Starts a background / autostarted process
    sudo systemctl enable --now ollama-llm
  • low Dangerous commands cmd-background-process README.md:111
    Starts a background / autostarted process
    sudo systemctl enable --now ollama-llm

Files scanned: 9. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-long-hermes description is 228 chars; the Hermes authoring standard requires ≤ 60 (one sentence, ending with a period)
  • warning description-no-when neither description nor a "## When to Use" section says when to use the skill

Process rating: all ten parameters 47/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 4 mutating operations with no state check
  • 60Consistency. The Hermes dialect needs category and tags
  • 100Tools and files. No external tools needed
  • 100Steps. 5 steps
  • 100Execution cost. Instruction body is 429 tokens

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
  • +2Single-language instructions
  • +3Description length 228: enough signal without eating the budget
  • +4Structure: 5 headings
  • +3Step-by-step instructions: 5 items
  • +4Has examples (2 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 67.

External checks

ClawHub: clean
This local LLM deployment helper is coherent and user-directed, but users should review generated Docker/systemd files before enabling a persistent service.
LLM: benign (high) · VirusTotal: · 18 Jun 2026