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AD minimax-monitor

Use when (1) user says "mmx 仪表盘启动" / "mmx monitor" / "MiniMax 配额查询" / "打开 minimax 监控" and wants a real-time dashboard. (2) user wants to check how much of their MiniMax Token Plan quota is left (4h / 24h / weekly windows for M3 / M2.7 / video / music / image models). (3) user wants to test MiniMax inference latency (TTFT / P50 / burst) by clicking "开始速率测试" - explicitly opt-in, NOT background.

ClawHub Agent Skills author: 王继鹏 v1.7.0 MIT-0 8 files body ≈ 2 146 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process D 43/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureInfrastructureData and analyticsSales and CRMtype 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
93/100
safety, quality, tests
Safety 60%
98
Quality 40%
85
Run on models
none yet
Process rating
D
43/100
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 · 2

    ✓ No critical or high findings

    Medium and low: 2
    • low Exfiltration read-dotenv README_zh.md:94
      Reads a .env file
      cp .env.example .env
    • low Exfiltration read-dotenv README.md:88
      Reads a .env file
      cp .env.example .env

    Files scanned: 8. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "permissions"

    Process rating: all ten parameters 43/100

    • 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. 5 mutating operations with no state check
    • 60Tools and files. Uses tools (web, node) that frontmatter does not declare
    • 100Steps. 47 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2146 tokens
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low The response is described with custom markup (5 tags): a typed call is more reliable

    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
    • -2localhost URLs: will not work for another user
    • -236 emoji in the instructions: noise for the model
    • +2Single-language instructions
    • +5Description quotes 5 example trigger phrases
    • +3Description length 395: enough signal without eating the budget
    • +4Structure: 15 headings
    • +3Step-by-step instructions: 47 items
    • +4Has examples (2 code blocks)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.

    External checks

    ClawHub: suspicious
    The skill is a coherent MiniMax quota dashboard, but it automatically reads local API credentials and calls MiniMax on page load despite saying this only happens after user confirmation.
    LLM: suspicious (high) · VirusTotal: · 9 Jul 2026