crisis-response-protocol

Verify crisis escalation, duty-to-warn, and mandatory reporting workflows in a mental health platform.

Updated May 11, 2026
One-click install
npx skills add https://github.com/thachrocky12345/local-agent-train-workstation --skill crisis-response-protocol-thachrocky12345
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: crisis-response-protocol
Source: https://github.com/thachrocky12345/local-agent-train-workstation/tree/main/.claude/skills/crisis-response-protocol
Command: npx skills add https://github.com/thachrocky12345/local-agent-train-workstation --skill crisis-response-protocol-thachrocky12345

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Mental health platforms must prove that severe risk screenings trigger proper escalation, notification, and follow-up, but these workflows are often scattered across backend models, signals, and frontend code with no single audit trail. This Skill systematically verifies each legal and clinical obligation against the actual codebase and database. ## Core Features & Use Cases - Crisis Detection Verification: Traces is_severe flags through Django models, signal handlers, and frontend crisis resource displays (988 Lifeline, Crisis Text Line). - Duty to Warn & Mandatory Reporting Audits: Checks Tarasoff obligation handling by state, threat assessment documentation, and child/elder abuse reporting workflows. - Follow-Up Compliance Queries: Uses Django ORM to find severe screenings lacking 48-hour follow-up appointments or clinical override records. - Use Case: Before a regulatory audit, run the protocol to produce a readiness matrix mapping detection, duty-to-warn, reporting, follow-up, and administrative obligations to their implementation status. ## Quick Start Ask the AI to run the crisis response protocol audit and generate a readiness report for the risk screening escalation workflow.

Frequently Asked Questions about crisis-response-protocol

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I verify a crisis escalation workflow in a Django app?

Trace the is_severe flag on the UserResponse model through signal handlers, notification code, and frontend crisis resource displays using targeted grep searches. Then run Django ORM queries to confirm severe screenings received follow-up appointments within 48 hours.

What is the Tarasoff duty to warn and how does it vary by state?

Tarasoff v. Regents of the University of California established that mental health professionals must warn identifiable potential victims of serious harm. About 29 states impose a mandatory duty, roughly 14 make it permissive, and a few like Texas and Georgia have no statute but may apply common law duties.

How do I find severe screenings with no follow-up using Django ORM?

Filter UserResponse objects where is_severe is True and created more than 48 hours ago, then check for an Appointment within 48 hours or a ClientScreeningIgnore record. Screenings with neither are flagged as follow-up violations.

Does this audit require database access or just source code?

Both are needed. Grep searches verify that escalation and notification code exists in the backend and frontend, while Django ORM queries against the database confirm that actual severe screenings received timely follow-up or documented clinical overrides.

What are the limitations of using a boolean flag for crisis detection?

A boolean is_severe flag records that a crisis was detected but does not track the escalation chain, making response actions hard to audit. The skill notes this gap and recommends checking for a dedicated crisis event model with timestamps, actors, and actions taken.