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Ubuntu AI — build a local AI platform on Ubuntu and Debian. Ubuntu AI across x86 desktops, ARM edge devices, Raspberry Pi, Jetson Orin, and cloud VMs. Heterogeneous Ubuntu AI cluster with NVIDIA CUDA, AMD ROCm, and CPU inference. LLM, image generation, and embeddings on Ubuntu. Debian AI with the same stack. Ubuntu AI本地推理平台。Ubuntu IA plataforma local.

Not recommendedcritical or high security findings
ClawHub Agent Skills author: Twin Geeks v1.0.1 MIT-0 2 files body ≈ 1 350 tokens Open the sourceclawhub.ai↗ analyzed 4 d ago

As a process D 48/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

GeneratorGitHubAI and agentsInfrastructuretype and topics are labelled automatically from the skill text
Runs in: OpenClaw 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) Goose OpenCode
JSON
Technical rating
C
70/100
safety, quality, tests
Safety 60%
70
Quality 40%
69
Run on models
none yet
Process rating
D
48/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

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.
  2. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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 · 5

  • high Dangerous commands cmd-pipe-to-shell SKILL.md:30
    Downloads and executes remote code from an unrecognised host (pipe to shell)
    curl -fsSL https://ollama.ai/install.sh | sh
Medium and low: 4
  • medium Dangerous commands cmd-privilege SKILL.md:64
    Privilege escalation / world-writable permissions
    sudo tee /etc/systemd/system/herd-router.service << 'EOF'
  • medium Dangerous commands cmd-privilege SKILL.md:84
    Privilege escalation / world-writable permissions
    sudo tee /etc/systemd/system/herd-node.service << 'EOF'
  • low Dangerous commands cmd-background-process SKILL.md:79
    Starts a background / autostarted process
    sudo systemctl enable --now herd-router
  • low Dangerous commands cmd-background-process SKILL.md:99
    Starts a background / autostarted process
    sudo systemctl enable --now herd-node

Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • note frontmatter-key unknown frontmatter key "homepage"

Process rating: all ten parameters 48/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 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 (web) that frontmatter does not declare
  • 100Steps. 14 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1350 tokens
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 13 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
  • +1No license
  • +2Single-language instructions
  • +3Description length 353: enough signal without eating the budget
  • +4Structure: 18 headings
  • +3Step-by-step instructions: 14 items
  • +4Has examples (12 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 69.

External checks

ClawHub: suspicious
The skill appears aligned with building a local Ubuntu AI service, but its setup opens a network port and creates persistent privileged services without enough safety guidance.
LLM: suspicious (high) · 28 May 2026