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AC ezviz-open-video

Ezviz camera video streaming skill. Generates PC and mobile links for live and playback streams. Use when: Need to view camera live feed, playback recordings, generate preview links, remote monitoring. ⚠️ Security: Requires EZVIZ_APP_KEY and EZVIZ_APP_SECRET environment variables with minimal permissions.

ClawHub Agent Skills author: EzvizOpenTeam v1.0.1 MIT-0 6 files body ≈ 3 257 tokens Open the sourceclawhub.ai↗ analyzed 4 d ago

As a process C 51/100 · Has gaps — weak spots: inputs and preconditions, failures and branches, consistency

GeneratorInfrastructureMedia and videotype 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
95/100
safety, quality, tests
Safety 60%
99
Quality 40%
88
Run on models
none yet
Process rating
C
51/100
Has gaps
Inputs and preconditions w 11
0
Failures and branches w 10
0
Progress reporting w 2
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:392
      Reads a .env file (documentation of a security skill)
      source .env
      security skill

    Files scanned: 6. 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 51/100

    • 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
    • 30Running it twice. 3 mutating operations with no state check
    • 40Consistency. Frontmatter name (ezviz-open-video) differs from the folder (ezviz-open-camera-video)
    • 50When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 100Steps. 51 steps
    • 100Execution cost. Instruction body is 3257 tokens
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • 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)
    • -4Absolute local paths (C:\Users, /home/…): not portable
    • -235 emoji in the instructions: noise for the model
    • +1No license
    • +2Single-language instructions
    • +3Description length 307: enough signal without eating the budget
    • +4Structure: 38 headings
    • +3Step-by-step instructions: 51 items
    • +3Output format is stated explicitly
    • +4Has examples (19 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.

    External checks

    ClawHub: suspicious
    The skill appears to do camera-link generation as advertised, but it handles private camera credentials in ways that can expose access tokens or secrets in logs, command history, and documentation examples.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026