SKILLEMALL.ai

BD smyx-child-bedtime-soothing-analysis

Through a fixed camera (with infrared night vision) and microphone in the child's bedroom, the system analyzes pre-sleep and night-time video and audio to detect pre-sleep crying (continuous crying, calling 'Mama'), fear-of-the-dark expressions (curling up, looking around), and nightmare awakenings (sudden sitting up, trembling, screaming). | 通过儿童卧室的固定摄像头(红外夜视)及麦克风,分析儿童睡前及夜间视频,检测睡前哭闹(持续性哭声、呼喊'妈妈')、怕黑表现(身体蜷缩、四处张望)、噩梦惊醒(突然坐起、颤抖、尖叫)等行为。当检测到上述情绪不安时,自动触发安抚动作:开启小夜灯(柔光)、播放预先录制的妈妈讲故事音频或轻柔摇篮曲。

ClawHub Agent Skills author: smyx-skills v1.0.11 MIT-0 30 files body ≈ 1 997 tokens Open the sourceclawhub.ai analyzed 2 d ago

Through a fixed camera (with infrared night vision) and microphone in the child's bedroom, the system analyzes pre-sleep and night-time video and audio to…

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

AnalyzerMedia and videoSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
B
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
69
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

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

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 (web, python) that frontmatter does not declare
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1997 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
  • -266 emoji in the instructions: noise for the model
  • -32 of 4 scripts are never mentioned in SKILL.md
  • +2Single-language instructions
  • +3Description length 489: 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: 69.

External checks

ClawHub: suspicious
The skill has a coherent child-bedtime video analysis purpose, but it also silently creates/reuses identities, stores tokens locally, queries cloud history, and defaults to private HTTP service endpoints without enough user-facing control or disclosure.
LLM: suspicious (high) · 6 Sept 2026