SKILLEMALL.ai

AF smyx-pregnant-posture-fatigue-detection-analysis

Uses a fixed home camera to detect prolonged standing, bending, and related posture of a pregnant woman, track standing duration and bending frequency, and assess fatigue risk. It sends rest reminders via speaker or app when tiring behavior is found. Suitable for homes, prenatal schools, community health centers, and smart-home or pregnancy apps. For health reference only, not medical diagnosis. | 通过家庭固定摄像头识别孕妇久站、弯腰等姿态,统计连续站立时长和弯腰频次,评估孕期劳累风险。发现久站、频繁弯腰或疑似重体力活动时,生成休息提醒,并可通过智能音箱或手机App推送。适用于家庭、孕妇学校、社区健康中心,可接入智能家居或孕期管理应用;结果仅供健康参考,不替代医生诊断。

ClawHub Agent Skills author: smyx-sunjinhui v1.0.9 MIT-0 30 files body ≈ 1 684 tokens Open the sourceclawhub.ai analyzed 2 d ago

Uses a fixed home camera to detect prolonged standing, bending, and related posture of a pregnant woman, track standing duration and bending frequency, and…

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

AnalyzerSoftware developmenttype 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
    • 21Steps. 1 steps, 1 vague phrases
    • 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 1684 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 539: 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’s main purpose is understandable, but it handles sensitive pregnancy home-video through cloud services while silently creating or reusing identity state and stored tokens.
    LLM: suspicious (high) · 28 Aug 2026