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ClawHub Agent Skills author: leonforcode v1.0.0 MIT-0 13 files body ≈ 4 921 tokens open source ↗ analyzed 32 h ago
ReferenceGitHubSoftware developmentInfrastructuretype 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
B
88/100
Overall score
Safety 60%
94
Quality 40%
78
Tests bonus
0

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): it is the main trigger signal.
  2. Add evals/evals.json with 4–6 real requests and expected answers: the full check will then use your cases instead of a model draft.
  3. Add a spec.yaml with triggers and assertions (skilltest init): the behaviour contract for CI.

Guard findings · 2

✓ No critical or high findings

Medium and low: 2
  • medium Dangerous commands cmd-privilege README.md:174
    Privilege escalation / world-writable permissions
    echo "13.107.253.39 cdn.playwright.dev" | sudo tee -a /etc/hosts
  • low Dangerous commands cmd-privilege scripts/install_browser.py:451
    Privilege escalation / world-writable permissions (string literal in code, not executed)
    print(f'    echo "{ip} {OFFICIAL_CDN}" | sudo tee -a /etc/hosts')
    code literal

Files scanned: 13. Evidence is masked. Grey chips explain why severity was lowered.

Lint

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process maturity 42/100

  • 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. 3 mutating operations with no state check
  • 40Consistency. Frontmatter name (github-assistant) differs from the folder (github-assistant-skill)
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 70Execution cost. Instruction body is 4921 tokens
  • 100Steps. 83 steps

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
  • -242 emoji in the instructions: noise for the model
  • +2Single-language instructions
  • +3Description length 152: enough signal without eating the budget
  • +4Structure: 37 headings
  • +3Step-by-step instructions: 83 items
  • +4Has examples (30 code blocks)
  • +4Reference files are cited in the instructions (1 of 1)
  • +3All 7 scripts are documented
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 78.

External checks

ClawHub: suspicious
This is a real GitHub assistant, but it asks for very broad GitHub authority and can perform high-impact actions without strong confirmation or scoping controls.
LLM: suspicious (high) · VirusTotal: · 29 May 2026