llamaguard

NeMo Guardrails is a runtime safety framework for LLM applications that adds programmable guardrails. It provides key safety features like jailbreak detection, input/output validation, and hallucination detection using the Colang 2.0 DSL. Use it to enforce safety and compliance rules in production LLM deployments.

CLIP is a vision-language model for zero-shot image classification and cross-modal retrieval, requiring no fine-tuning. It excels at general-purpose tasks like image-text matching, semantic search, and content moderation. Developers can use it for vision-language applications by providing image and text pairs for similarity scoring.

Constitutional AI trains models to be harmless using a two-phase method of self-critique/revision and reinforcement learning from AI feedback (RLAIF). It's designed for safety alignment, enabling models to reduce harmful outputs without relying on human labels. Developers can use this skill to implement the core safety system that powers Claude.

This skill implements Anthropic's Constitutional AI method for training harmless AI models through self-critique and revision. It provides a two-phase approach using supervised learning with AI self-critique followed by RLAIF (Reinforcement Learning from AI Feedback) for safety alignment. Use it to reduce harmful outputs in your Claude applications without requiring human-labeled harmful data.