Pet Eating Speed Detection and Slow-Feed Analysis
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Please install @user_bb47e3e3/pet-eating-speed-slow-feed-analysis into your AI assistant by following https://skillhub.cn/install/skillhub.md.
About this skill
Problem
When a pet eats too quickly, gulps, or shows an abnormal eating duration, visual inspection alone is hard to audit. This skill targets video analysis and turns the eating process near the bowl into structured signals: start/end timing, estimated eating speed, slow-feed suggestions, and queryable history.
How It Works
Input can be a local video path or a public URL, typically mp4, avi, or mov, with a maximum around 10MB, and it should cover a complete eating session. The built-in script calls a server-side API to analyze the video, extract fields, and generate a report. Users do not provide identity parameters; cloud reports are linked internally. Running --list reads the cloud history list and renders it as a Markdown table.
Boundaries
The output is health guidance, not diagnosis or treatment. Interventions such as slow-feed ramps or voice alerts are executed by the device; this skill only produces analysis and recommendations. Thresholds must be interpreted with breed, weight, and individual variation. Historical reports must come from the cloud API, not local records or manually compiled memory.
Use Cases
- A pet care team receives bowl videos and needs to judge overfast eating while producing auditable slow-feed guidance.
- When maintaining remote pet monitoring devices, use a video URL to output slow-feed risks and a report link.
- Review historical eating reports for a date range by fetching cloud data via `--list` and formatting a table.
- A veterinary assistant records local video analysis as health reference, noting it is not diagnosis and weight matters.
Best For
- Pet health data operations staff who need to convert bowl videos into auditable eating-speed reports.
- Engineers maintaining pet monitoring APIs who need JSON details and report field checks from scripts.
- Technical support teams deploying slow-feed devices who need to verify risk notes and recommendations.
- Clinic assistants who need health-reference notes for abnormal eating, not diagnostic conclusions.
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