SKILLEMALL.ai

AF smyx-living-alone-rhythm-anomaly-analysis

Using a fixed camera in the living room or bedroom of a person living alone, the system continuously analyzes night video (typically 22:00-06:00) to detect lights-off time (when light sources turn off) and early-morning activity (human movement or body motion between 0-6 AM). It builds a personal historical baseline (e.g., average lights-off time and early-morning activity frequency over the past 7-14 days). | 通过家庭客厅或卧室固定摄像头,夜间(通常指22:00-6:00)连续分析视频,检测熄灯时间(光源关闭的时刻)、凌晨活动(0-6点期间的人体移动或肢体动作)。建立个人历史基线(如过去7-14天的平均熄灯时间和凌晨活动频率),当当前熄灯时间比基线延迟超过2小时,或凌晨活动频次显著增加(如超出基线2个标准差)时,输出'作息规律异常'提醒。

ClawHub Agent Skills author: smyx-skills v1.0.10 MIT-0 30 files body ≈ 1 764 tokens Open the sourceclawhub.ai analyzed 4 d ago

As a process F 32/100 · Will not run — weak spots: steps, result and completion, when it triggers

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
F
32/100
Will not run
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 32/100

    • 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
    • 30Running it twice. 1 mutating operations with no state check
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1764 tokens

    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
    • -255 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: 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 broadly matches its stated home night-video analysis purpose, but it handles very private footage and account tokens with unsafe transport, silent identity creation, and persistent local credential storage.
    LLM: suspicious (high) · 8 Sept 2026