SKILLEMALL.ai

AD smyx-pet-scratch-frequency-intensity-analysis

Triggers when a user provides a cat scratch post area video URL or file for analysis; supports local video uploads or network URLs to call server-side APIs for scratch behavior recognition, analyzing scratch frequency, single-session duration, and intensity (estimated via vibration amplitude), outputting standardized observation data on stress level and claw health (without diagnosing diseases or prescribing behavior correction). Application scenarios: smart scratch post, multi-cat household stress management. Development reason: stress-induced abnormal scratch, early signs of behavioral issues. | 当用户提供猫抓板区域的视频URL或文件时,触发本技能进行抓挠行为分析;支持通过上传本地视频或网络视频URL,调用服务端API进行抓挠动作识别,分析抓挠频率、单次持续时间、力度(通过振动幅度估算),评估宠物压力水平和爪子健康状况,输出标准化观察结果(不诊断疾病、不提供行为矫正建议)。应用场景:智能猫抓板、宠物行为监测、多猫家庭压力管理。

ClawHub Agent Skills author: smyx-sunjinhui v1.0.8 MIT-0 30 files body ≈ 1 396 tokens Open the sourceclawhub.ai analyzed 4 d ago

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

AnalyzerMedia and videoInfrastructuretype 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

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 1396 tokens
    • 100Running it twice. No mutating operations

    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
    • -256 emoji in the instructions: noise for the model
    • -32 of 4 scripts are never mentioned in SKILL.md
    • +2Single-language instructions
    • +3Description length 773: enough signal without eating the budget
    • +4Structure: 19 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 can perform pet scratch analysis, but it silently links activity to cloud identities, uploads media, retrieves history, and stores authentication data locally without clear user-facing control.
    LLM: suspicious (high) · 29 Aug 2026