Train and evaluate AI agents on DIPG medical safety with OpenEnv.
npx skills add https://github.com/surfiniaburger/med-safety-gym --skill dipg-safety-env
Please help me install this Agent Skill. Skill: dipg-safety-env Source: https://github.com/surfiniaburger/med-safety-gym/tree/main Command: npx skills add https://github.com/surfiniaburger/med-safety-gym --skill dipg-safety-env
This Skill provides a safe training environment for AI agents to learn and demonstrate reliability in handling medical information related to Diffuse Intrinsic Pontine Glioma (DIPG).
Run the server and provide a medical context and question to the DIPG Safety Gym for an AI agent to process.
High-intent search queries and answers about installing and using this skill.
To train AI agents for medical safety in DIPG, use this Skill with the OpenEnv framework to provide clinical context and questions, scoring agents on fact-based responses and safe abstention. It evaluates reliability in handling high-stakes medical information.
Safe abstention in medical AI reinforcement learning trains agents to withhold answers when clinical context is insufficient. This Skill enforces strict guidelines to ensure agents avoid providing unsafe or unsupported information regarding DIPG.
Yes, you need the OpenEnv framework and uv to run this DIPG safety environment. The Skill functions as a gym server requiring OpenEnv to process clinical contexts and evaluate AI agent responses.
AI agents detect conflicting information in clinical contexts by evaluating provided data against strict safety guidelines. This Skill trains agents to identify and report inconsistencies while adhering to fact-based response protocols.
Yes, this Skill uses RLVR-inspired scoring to evaluate AI agents on medical safety. While focused on DIPG, the mechanisms for conflict detection, safe abstention, and fact avoidance apply to general medical context evaluation.
A limitation of this DIPG safety environment is its strict requirement for OpenEnv and uv. Agents must operate within provided clinical contexts and are heavily penalized for unsupported information, limiting unsupervised exploration.
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