April 2025 (2025-04) monthly summary for stanford-crfm/helm: Key feature delivered was SHC Benchmark Dataset Expansion and Prompt Refinement. This included adding privacy- and proxy-focused SHC benchmark datasets and refining prompt instructions to ensure consistent 'A'/'B' responses across SHC scenarios, expanding HELM's capability to evaluate biomedical text understanding. Major bugs fixed: none reported this month. Overall impact and accomplishments: Strengthened benchmarking coverage for privacy-sensitive biomedical NLP, enabling more robust evaluation, faster iteration on prompts, and clearer signals for production readiness. Technologies/skills demonstrated: data curation of benchmark datasets, prompt engineering, version-controlled collaboration (Git), and reproducible benchmarking workflows.