SKILLEMALL.ai

BF smyx-infant-cry-cause-classification-analysis

Using the built-in microphone of a baby monitor or smart camera to capture infant cry audio, AI acoustic analysis extracts cry features such as frequency, pitch, rhythm, and duration, and classifies the possible causes behind the cry (hunger, sleepiness, pain/discomfort, boredom/need for comfort, fear, etc.), outputting the most likely cause and its confidence. | 通过婴儿监护器或智能摄像头的内置麦克风采集婴儿哭声音频,利用AI声学分析技术提取哭声的频率、音调、节奏、持续时间等特征,分类识别婴儿哭声背后的可能原因(饥饿、困倦、疼痛/不适、无聊/需要安抚、恐惧等),输出最可能的原因类别及置信度。系统实时监测哭声,当检测到哭声时自动分析并在父母手机APP上推送结果(如'宝宝可能是饿了,建议喂奶')。

ClawHub Agent Skills author: smyx-skills v1.0.10 MIT-0 30 files body ≈ 1 573 tokens Open the sourceclawhub.ai analyzed 2 d ago

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

AnalyzerInfrastructuretype 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
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

  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 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 1573 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 534: 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 appears to run a cloud-backed generic media analysis flow under an infant-cry label, with silent identity creation and local token persistence that users should review before installing.
LLM: suspicious (high) · 25 Aug 2026