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AD claw-agent-cockpit

CLAW Agent 智控驾驶舱 - 专为 OpenClaw Coding Plan 订阅用户打造的一站式运维监控平台。功能包括：(1) API 额度监控与四级告警 (2) 自学习预测引擎（越用越准）(3) 每日用量趋势分析 (4) Token 用量透视 (5) Cron 定时任务管理 (6) 多 Agent 状态监控 (7) 订阅到期倒计时与一键续订。Deploy a full-featured cost & operations dashboard for OpenClaw Coding Plan subscribers. Use when setting up API quota monitoring, Agent status tracking, token usage analysis, Cron management, or subscription expiry tracking.

ClawHub Agent Skills author: mumuli2021 v1.2.7 MIT-0 10 files body ≈ 719 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

IntegrationAI and agentsInfrastructureSoftware developmenttype 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
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
84
Run on models
none yet
Process rating
D
46/100
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 · 0

    ✓ No critical or high findings

    Files scanned: 10. 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 46/100

    • 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
    • 60Tools and files. Uses tools (node) that frontmatter does not declare
    • 100Steps. 30 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 719 tokens
    • 100Running it twice. No mutating operations

    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
    • -230 emoji in the instructions: noise for the model
    • +1No license
    • +2Single-language instructions
    • +3Description length 403: enough signal without eating the budget
    • +4Structure: 21 headings
    • +3Step-by-step instructions: 30 items
    • +4Has examples (5 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.

    External checks

    ClawHub: suspicious
    This appears to be a legitimate operations dashboard, but it exposes unauthenticated controls that can change scheduled-task state and write local files.
    LLM: suspicious (medium) · VirusTotal: · 29 May 2026