SKILLEMALL.ai

AD smyx-separation-anxiety-relief-analysis

AI-powered pet separation anxiety detection & relief when the owner leaves home. Real-time monitoring via smart camera detects typical anxiety signs—continuous vocalization, pacing, scratching doors/windows, destructive chewing. When anxiety reaches preset thresholds, the system auto-triggers comfort actions (play owner's pre-recorded voice, dispense treats via smart feeder, activate interactive toys) to reduce anxiety and destructive behavior, improving pet welfare. Scenarios: pet households (especially office workers / frequent travelers), pet boarding centers. | 通过智能家居摄像头(宠物摄像头)实时监测主人离家后宠物的行为,检测持续性发声(哀嚎、嚎叫)、来回踱步、抓挠门窗或破坏家具等分离焦虑典型表现。当焦虑行为达到预设阈值时,自动触发安抚动作,包括播放主人预录的安抚语音、联动智能零食机投掷零食、或启动互动玩具(如自动逗猫棒),减轻宠物独处时的焦虑,减少破坏行为,提升宠物福利。应用场景:宠物家庭(尤其上班族、经常出差的主人)、宠物寄养中心。

ClawHub Agent Skills author: smyx-sunjinhui v1.0.9 MIT-0 30 files body ≈ 1 735 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process D 35/100 · Unfinished process — weak spots: steps, result and completion, when it triggers

AnalyzerInfrastructuretype 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
35/100
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 35/100

    • 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
    • 25Steps. 1 steps
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1735 tokens
    • 100Running it twice. No mutating operations
    • low 13 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
    • -284 emoji in the instructions: noise for the model
    • -32 of 4 scripts are never mentioned in SKILL.md
    • +2Single-language instructions
    • +3Description length 763: enough signal without eating the budget
    • +4Structure: 23 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
    This skill is a cloud pet-video analysis tool, but it automatically manages identity, stores tokens locally, and sends media plus account-linked data to remote services with limited user control.
    LLM: suspicious (high) · 23 Aug 2026