mental-health-analyzer_skill

This skill analyzes mental health data, identifies patterns, assesses status, and offers personalized, non-clinical guidance to improve well-being.

Shell

655

GitHub Stars

1

Bundled Files

2 months ago

Catalog Refreshed

4 months ago

First Indexed

Readme & install

Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.

Installation

Preview and clipboard use veilstrat where the catalogue uses aiagentskills.

npx veilstrat add skill huifer/wellally-health --skill mental-health-analyzer

SKILL.md25.2 KB

Overview

This skill analyzes mental health data to identify emotional patterns, assess risk levels, and generate personalized, non-diagnostic recommendations. It correlates psychological measures with sleep, exercise, nutrition, and chronic disease data to highlight actionable lifestyle factors. Outputs include trend summaries, crisis risk alerts, progress tracking, and an exportable report.

How this skill works

The analyzer reads structured trackers and daily mood logs, validates data completeness, and computes time-series trends for PHQ-9, GAD-7, PSQI and emotion entries. It applies rule-based risk scoring and statistical correlation methods to link sleep, activity, nutrition and chronic disease metrics with mental health indicators. Results are packaged into concise findings, prioritized alerts, and specific behavior-focused suggestions.

When to use it

Review symptom trends after multiple PHQ-9/GAD-7 assessments (recommended ≥3).
Investigate recurring emotional patterns or triggers from daily mood logs.
Monitor therapy progress and homework adherence over time.
Perform a crisis risk check when self-harm items or rapid worsening appear.
Correlate mental state with sleep, exercise, nutrition, or chronic disease data.

Best practices

Ensure data completeness: at least 3 assessments or 7+ daily entries for reliable trends.
Regularly sync sleep, fitness and nutrition trackers for accurate correlation analysis.
Treat outputs as decision support, not medical diagnosis or prescription.
Act immediately on high-risk alerts and connect users with professional care.
Aggregate long-range logs (weekly/monthly) for performance and clarity.

Example use cases

Generate a 3-month trend report to evaluate treatment effectiveness and PHQ-9/GAD-7 change rates.
Detect time-of-day or weekday patterns in mood to schedule coping strategies.
Run a crisis assessment after a concerning diary entry to determine risk tier and next steps.
Correlate poor sleep weeks with increased anxiety scores to prioritize sleep interventions.
Produce a therapy progress summary listing goals, percent complete, and recommended adjustments.

FAQ

No. It identifies patterns and risk levels but does not provide diagnoses or prescribe medication.

What triggers an immediate alert?

High-risk signals include a PHQ-9 item 9 score indicating frequent self-harm ideation, total risk score ≥10, hallucinations, or an active plan—these prompt urgent action recommendations.

What data is required for reliable analysis?

At minimum, three PHQ-9/GAD-7 assessments or seven days of mood diary entries; linking sleep, activity and nutrition improves correlation findings.