AD smyx-vocal-emotion-classification-analysis
Triggers when a user provides a pet vocalization audio/video URL or file for analysis; extracts acoustic features such as frequency, duration, interval, and harmonic structure via AI audio analysis, and classifies the vocalization into 6+ emotion categories (howling, growling, excitement, loneliness, fear, whining/coaxing) with confidence scores. Helps owners understand pet emotional states, improve human-pet interaction, and detect potential stress or health issues early. Application: daily companionship (smart camera / collar), boarding center mood monitoring, vet clinic calming assessment, behavior training assistance. Does NOT provide medical or behavior-modification advice — only outputs audio-based emotion classification results with confidence. | 当用户提供宠物（犬/猫）叫声音频或视频URL/文件时，触发本技能进行叫声情绪深度分类分析；利用AI音频分析技术提取频率、时长、间隔、谐波结构等声学特征，将叫声分类为哀嚎、低吼、兴奋、孤独、恐惧、撒娇等6种以上情绪类别，并输出置信度；帮助宠物主人理解宠物情绪状态，改善人宠互动，及时发现潜在压力或健康问题。应用场景：宠物家庭日常陪伴（智能摄像头/项圈）、寄养中心情绪监测、宠物医院安抚评估、行为训练辅助。仅输出基于音频的情绪分类结果及置信度，不提供医疗或行为矫正建议。
As a process D 41/100 · Unfinished process — weak spots: steps, result and completion, inputs and preconditions
How to improve
- 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 41/100
- 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
- 25Steps. 1 steps
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1496 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)
- +3Description length 997: 120–800 characters recommended
- +3No numbered steps or checklist
- +3Output format is not stated: the model decides each time
- -257 emoji in the instructions: noise for the model
- -32 of 4 scripts are never mentioned in SKILL.md
- +2Single-language instructions
- +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: 78.