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萤石开放平台云台设备控制技能。支持设备列表查询、设备状态查询、云台控制 (PTZ)、预置点管理等功能。 Use when: 需要控制萤石云台设备、调整摄像头角度、设置预置点。 ⚠️ 安全要求：必须设置 EZVIZ_APP_KEY 和 EZVIZ_APP_SECRET 环境变量，使用最小权限凭证。

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 5 files body ≈ 2 826 tokens Open the sourcegithub.com↗ analyzed 2 d ago

萤石开放平台云台设备控制技能。支持设备列表查询、设备状态查询、云台控制 (PTZ)、预置点管理等功能。 Use when: 需要控制萤石云台设备、调整摄像头角度、设置预置点。 ⚠️ 安全要求：必须设置 EZVIZAPPKEY 和 EZVIZAPPSECRET 环境变量，使用最小权限凭证。

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

IntegrationSoftware developmentAI 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
92/100
safety, quality, tests
Safety 60%
99
Quality 40%
81
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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Exfiltration read-dotenv SKILL.md:408
      Reads a .env file
      source .env

    Files scanned: 5. 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 (python) that frontmatter does not declare
    • 100Steps. 65 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2826 tokens
    • 100Running it twice. No mutating operations
    • low 18 top-level sections: this looks like several domains in one skill

    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
    • -4Absolute local paths (C:\Users, /home/…): not portable
    • -248 emoji in the instructions: noise for the model
    • +1No license
    • +2Single-language instructions
    • +3Description length 149: enough signal without eating the budget
    • +4Structure: 39 headings
    • +3Step-by-step instructions: 65 items
    • +4Has examples (19 code blocks)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 81.