SKILLEMALL.ai
GitHub ↗ RU Sign in

AD ezviz-multimodal-analysis

萤石多模态理解技能。通过设备抓图 + 智能体分析接口，实现对摄像头画面的 AI 理解分析。 Use when: 需要对监控画面进行智能分析、场景识别、行为理解、物体检测等多模态 AI 分析任务。 ⚠️ 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 ≈ 3 989 tokens Open the sourcegithub.com↗ analyzed 2 d ago

萤石多模态理解技能。通过设备抓图 + 智能体分析接口，实现对摄像头画面的 AI 理解分析。 Use when: 需要对监控画面进行智能分析、场景识别、行为理解、物体检测等多模态 AI 分析任务。 ⚠️ 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 41/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerAI 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
90/100
safety, quality, tests
Safety 60%
98
Quality 40%
77
Run on models
none yet
Process rating
D
41/100
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
This is a copy of a skill from another catalog; the rating counts the canonical one: ezviz-multimodal-analysis (LeoYeAI/openclaw-master-skills)

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 Secrets in code secret-password-literal references/ezviz-agent-api.md:108
      Hard-coded password / key literal (may be an example) (quoted — discussed, not commanded)
      --header 'accessToken: at.7…s6n******' \
      quoted
    • low Exfiltration read-dotenv SKILL.md:647
      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 41/100

    • 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
    • 40Consistency. Frontmatter name (ezviz-multimodal-analysis) differs from the folder (hsa-test3)
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 100Steps. 100 steps
    • 100Execution cost. Instruction body is 3989 tokens
    • 100Running it twice. No mutating operations
    • low 21 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
    • -262 emoji in the instructions: noise for the model
    • -41 reference files, but SKILL.md never points to them: the model will not open them
    • +1No license
    • +2Single-language instructions
    • +3Description length 159: enough signal without eating the budget
    • +4Structure: 48 headings
    • +3Step-by-step instructions: 100 items
    • +4Has examples (32 code blocks)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.