SKILLEMALL.ai
GitHub ↗ RU Sign in

AD skill-vetter-guide

Guide for vetting third-party OpenClaw skills before installation using the Skill Vetter security protocol. Use when installing any third-party skill, auditing existing skills, enforcing vet-before-install SOP, performing periodic security audits of installed skills, or setting up Skill Vetter on a new OpenClaw instance. Triggers on: install a skill, vet this skill, audit skills, skill security, review skill safety, set up skill vetter, skills security audit.

ClawHub Agent Skills author: VibeSparkingAI v1.1.0 MIT-0 9 files body ≈ 1 082 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process D 49/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, failures and branches

AnalyzerInfrastructuretype 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
A
95/100
safety, quality, tests
Safety 60%
99
Quality 40%
88
Run on models
none yet
Process rating
D
49/100
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

How to improve

    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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Risky intent intent-offensive-security SKILL.md:57
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      | `sudo`/elevated permissions | Privilege escalation |

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

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 49/100

    • 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
    • 30Running it twice. 2 mutating operations with no state check
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 100Steps. 11 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1082 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
    • +1No license
    • +2Single-language instructions
    • +3Description length 463: enough signal without eating the budget
    • +4Structure: 13 headings
    • +3Step-by-step instructions: 11 items
    • +4Has examples (3 code blocks)
    • +4Reference files are cited in the instructions (3 of 6)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.

    External checks

    ClawHub: suspicious
    This is a legitimate security-vetting guide, but some templates can persistently change agent behavior or schedule recurring audits without enough confirmation and rollback detail.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026