Mental Health Analysis Tool
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About this skill
Problem Addressed
The tool targets engineering workflows where a video or image needs to produce a reusable mental health reference instead of ad hoc notes. It accepts local files, image/video inputs, or network URLs, then organizes emotion, stress, social interaction, and behavior signals into structured reports and history queries under the PSYCHOLOGY_ANALYSIS scenario.
How It Works
The main flow is: provide video input, run the analysis script, and receive a structured report. Local files can be passed via --input, while remote videos can be submitted through --url. Analysis can focus on general/emotion/anxiety/depression/stress/other using --analysis-type, and verbosity can be set with --detail to basic/standard/json. History listing uses --list, which should read from the cloud API rather than local memory or manually aggregated records. The main entry point is scripts/psychology_analysis.py, and references/api_doc.md documents fields and error codes.
Boundaries
Treat outputs as reference signals for employee care, campus screening, or home wellness monitoring, not as professional diagnosis. Input should be clear and context-appropriate, with video files limited to mp4/avi/mov and 10MB. Risk flags and recommendations should be interpreted with individual context, and professional help should be sought when intervention is needed.
Use Cases
- HR care managers review employee-provided short video URLs, identify stress and anxiety signals, and produce structured follow-up reports for wellbeing cases.
- Campus psychology teachers input student-submitted 10MB mp4 videos, screen for social withdrawal or reduced eye contact, and export structured report links for follow-up.
- Engineers debug the analysis pipeline, run local mov files with --analysis-type stress, and inspect the returned JSON fields for stress assessment output.
- Health coordinators query history reports, call --list to retrieve cloud-hosted records, and convert the latest psychology analysis list into a Markdown table.
Best For
- Product managers running employee care programs: organize short-video analysis into risk level, emotion trends, and recommendation lists.
- Platform engineers building campus screening: wire video input, report output, and history queries into a service flow.
- Multimodal algorithm engineers: validate structured JSON returned for anxiety, stress, and social behavior analysis types.
- Family wellness assistant developers: generate reference reports from videos under 10MB and mark professional-help boundaries.
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