SKILLEMALL.ai

AD smyx-pet-toy-interaction-activity-analysis

Triggers when a user provides a pet toy area video URL or file for analysis; supports local video uploads or network URLs to call server-side APIs for toy interaction behavior recognition, tracking interaction frequency, duration, and toy preference per pet, generating daily activity curves and trend comparisons to detect declining activity that may indicate illness or depression. Application scenarios: smart pet toys, pet wellness monitoring. | 当用户提供玩具区域视频URL或文件时,触发本技能进行互动行为分析;支持通过上传本地视频或网络视频URL,调用服务端API进行玩具互动识别,记录宠物与各类玩具互动频次、时长、偏好,生成活跃度曲线,活跃度下降提示疾病或抑郁风险。应用场景:智能宠物玩具、宠物健康管理。

ClawHub Agent Skills author: smyx-sunjinhui v1.0.1 MIT-0 30 files body ≈ 1 585 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process D 41/100 · Unfinished process — weak spots: steps, result and completion, inputs and preconditions

AnalyzerInfrastructureMedia and videotype and topics are labelled automatically from the skill text
JSON
Technical rating
A
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
81
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: smyx-pet-toy-interaction-activity-analysis (ClawHub)

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: 30. 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
    • 25Steps. 1 steps
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1585 tokens
    • 100Running it twice. No mutating operations
    • low 11 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)
    • +3No numbered steps or checklist
    • +3Output format is not stated: the model decides each time
    • -258 emoji in the instructions: noise for the model
    • -32 of 4 scripts are never mentioned in SKILL.md
    • +2Single-language instructions
    • +3Description length 581: enough signal without eating the budget
    • +4Structure: 21 headings
    • +4Has examples (3 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)
    • +1License stated

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

    External checks

    ClawHub: suspicious
    The skill largely matches its pet-video cloud analysis purpose, but it silently creates or reuses an account identity and stores/sends authentication tokens and report history data.
    LLM: suspicious (high) · VirusTotal: · 27 Jun 2026