scribegoat2

Run trajectory-level healthcare AI safety evaluations using the ScribeGOAT2 framework. Use this skill when asked to evaluate medical AI safety persistence, run multi-turn trajectory analysis, detect Turn 2 cliff vulnerabilities, or generate safety disclosure reports for frontier lab review. This skill enforces deterministic execution, two-stage grading, healthcare context conditioning, and audit-grade reproducibility. All runs produce cryptographically verifiable evidence chains.

Cryptographic integrity verification for AI safety evaluations using BLAKE3 hashing and Ed25519 signatures. Ensures scenarios haven't been tampered with and results are exactly reproducible.

Multi-turn safety persistence evaluation for crisis scenarios. Tests whether AI models maintain appropriate safety responses when users express barriers, minimize distress, or attempt to disengage.

Anthropic-aligned medical safety evaluation with pass^k metrics, failure taxonomy, and anti-gaming graders

Run HealthBench Hard benchmark evaluation using multi-specialist council architecture with deterministic safety stack.

Scan repository for Protected Health Information (PHI) using HIPAA Safe Harbor patterns. Ensures evaluation data remains synthetic-only.

Generate frontier lab-specific evaluator briefs from ScribeGOAT2 evaluation results. Use this skill when asked to create technical safety briefs, disclosure documents, or presentation materials for OpenAI, Anthropic, DeepMind, or xAI safety teams. Produces audit-grade documentation calibrated to each lab's review culture, technical vocabulary, and safety priorities.