t1d-post-llm-safety-guardrails

Detect policy violations and enforce disclaimers in post-LLM T1D companion outputs.

Updated May 15, 2026
One-click install
npx skills add https://github.com/ruskibeats/t1d --skill t1d-post-llm-safety-guardrails
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: t1d-post-llm-safety-guardrails
Source: https://github.com/ruskibeats/t1d/tree/main/.pi/skills-archive/t1d-post-llm-safety-guardrails
Command: npx skills add https://github.com/ruskibeats/t1d --skill t1d-post-llm-safety-guardrails

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill enforces safety protocols in a T1D companion after the use of LLM (Language Learning Model) by implementing a 3-layer safety model.

Core Features & Use Cases

  • Safety Model Implementation: Enforce safety at three layers - pre-LLM, LLM system prompt, and post-LLM.
  • Policy Violation Detection: Identifies policy violations in dosing advice, treatment changes, and disclaimer enforcement.
  • Use Case: It ensures that LLM outputs in a T1D companion are safe before they are presented to the user.

Quick Start

Use the t1d-post-llm-safety-guardrails skill to apply post-LLM safety protocols in the T1D companion's AI chat pipeline.

Frequently Asked Questions about t1d-post-llm-safety-guardrails

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

FAQPage Schema
How do I enforce safety guardrails on LLM outputs for a Type 1 Diabetes companion app?

You can enforce LLM safety guardrails by applying post-processing checks to detect policy violations in dosing advice and treatment changes, ensuring mandatory disclaimers are appended before outputs reach the user.

What is post-LLM safety validation for AI output in healthcare applications?

Post-LLM safety validation is a post-processing step that screens generated text for maladaptive behavior or harm, enforcing regulatory compliance and policy violation detection after the LLM creates its response.

How do I prevent my LLM from giving unauthorized medical dosing advice in a T1D educational tool?

You prevent unauthorized dosing advice by running post-LLM safety checks that identify policy violations related to treatment changes and inject mandatory disclaimers into the final output.

Does this post-LLM safety approach work as a standalone check or does it require pre-LLM filtering?

This approach implements a 3-layer safety model including pre-LLM, LLM system prompt, and post-LLM checks, meaning it is designed to function as part of a comprehensive pipeline rather than a standalone filter.

What are the limitations of using post-processing checks for AI output validation in regulated medical tools?

Post-processing checks are limited to evaluating the final generated text and cannot retroactively alter the LLM's internal reasoning, meaning they function as a safeguard against policy violations rather than a guarantee of clinical accuracy.

Can I use post-LLM safety guardrails to ensure regulatory compliance for AI-generated diabetes treatment advice?

Yes, you can use post-LLM safety guardrails to ensure regulatory compliance by detecting policy violations in treatment change suggestions and enforcing mandatory disclaimers before outputs are presented to the user.