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AC ovirt-mcp

Manage oVirt/RHV virtualization infrastructure via MCP. Provides 186 tools for VMs, hosts, clusters, networks, storage, templates, snapshots, disks, events, RBAC, quotas, and more. Use when the user asks to manage oVirt virtual machines, create/delete/modify VMs, check host status, manage storage domains, handle templates, configure networks, manage user permissions, view events/alerts, or any oVirt/RHV infrastructure operations. Triggers on "oVirt", "RHV", "virtual machine", "VM management", "storage domain", "cluster", "host management".

ClawHub Agent Skills author: Joey v0.1.0 MIT-0 17 files body ≈ 1 888 tokens Open the sourceclawhub.ai↗ analyzed 4 d ago

As a process C 62/100 · Has gaps — weak spots: result and completion, running it twice, progress reporting

GeneratorGitHubInfrastructureAI and agentstype 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
97/100
safety, quality, tests
Safety 60%
100
Quality 40%
93
Run on models
none yet
Process rating
C
62/100
Has gaps
Result and completion w 14
0
Progress reporting w 2
0
Running it twice w 4
30
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 · 0

    ✓ No critical or high findings

    Files scanned: 17. 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 62/100

    • 0Result and completion. Does not say what the result is
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 27 mutating operations with no state check
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 6 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1888 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

    • +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
    • +5Description quotes 5 example trigger phrases
    • +3Description length 545: enough signal without eating the budget
    • +4Structure: 20 headings
    • +3Step-by-step instructions: 6 items
    • +4Has examples (14 code blocks)
    • +4Reference files are cited in the instructions (14 of 14)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 93.

    External checks

    ClawHub: suspicious
    This skill is not deceptive, but it gives an AI agent broad oVirt/RHV infrastructure control without enough scoping or safety guidance.
    LLM: suspicious (high) · VirusTotal: · 28 May 2026