SKILLEMALL.ai

BD phone-detection-alert

玩手机检测告警技能。通过摄像头抓图→AI 分析→TTS 语音→设备播放的完整流程。Use when: 需要监控玩手机行为并自动告警、课堂/考场/会议室纪律监控、通过萤石摄像头进行 AI 行为检测。

ClawHub Agent Skills author: EzvizOpenTeam v1.0.10 MIT-0 4 files body ≈ 1 323 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

AnalyzerInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
B
83/100
safety, quality, tests
Safety 60%
93
Quality 40%
68
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

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Secrets in code medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The files contain someone else's key or token. If it is live, your agent will call third-party services under a stranger's identity; if it was revoked, the skill's scripts simply fail. Such a key often arrives with the author's whole workspace, personal data included.

For the author

The key is visible to everyone who downloaded the skill and has likely been copied by catalog-scanning bots already. Revoke it now, check bills and access logs, then reissue.

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 · 3

    ✓ No critical or high findings

    Medium and low: 3
    • medium Secrets in code secret-labelled-token references/ezviz-api-docs.md:54
      Labelled token / key literal (vendor format unknown — verify it is not a live credential)
      appKey=9mqi…srl&appSecret=096e…776
    • low Secrets in code secret-password-literal references/ezviz-api-docs.md:138
      Hard-coded password / key literal (may be an example)
      accessToken=at.1…929&deviceSerial=427734888&channelNo=1
    • low Secrets in code secret-password-literal references/ezviz-api-docs.md:227
      Hard-coded password / key literal (may be an example) (detector / deny-list definition)
      --header 'accessToken: at.3…rfi' \
      detector

    Files scanned: 4. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • warning frontmatter-yaml SKILL.md: the frontmatter is not valid YAML (YAML parse error: Nested mappings are not allowed in compact mappings at line 2, column 14: description: 玩手机检测告警技能。通过摄像头抓图→AI 分析→TTS 语音→设备播放的完整流程。Use when: 需要监控玩手机行为并自动告警、… ^ ); fields were read line by line. The usual cause is a colon inside an unquoted value

    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
    • 30Running it twice. 1 mutating operations with no state check
    • 40Consistency. Frontmatter name (phone-detection-alert) differs from the folder (ezviz-open-capture)
    • 100Tools and files. No external tools needed
    • 100Steps. 52 steps
    • 100Execution cost. Instruction body is 1323 tokens
    • low 10 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)
    • +3Description length 98: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • -230 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
    • +4Structure: 13 headings
    • +3Step-by-step instructions: 52 items
    • +4Has examples (8 code blocks)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 68.

    External checks

    ClawHub: suspicious
    This skill matches its stated camera-monitoring purpose, but it needs review because it handles surveillance images and device audio alerts with some unsafe edges.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026