Improving HCLS AI reasoning with open-source agent skills

Foundation model-based AI agents frequently misapply healthcare and life sciences decision frameworks, even when trained on the relevant guidelines. These agents may cite the correct framework but still apply evidence categories incorrectly, omit crucial population frequency thresholds, or invent computational predictor scores. While the models possess factual knowledge, they lack the structured reasoning procedures typically cultivated over years of practitioner training.

The resulting silent failures manifest in variant interpretation, claims adjudication, clinical trial design, and imaging analysis, with potentially significant regulatory and patient safety implications.

To address this methodology gap, we present a collection of 38 open-source agent skills spanning 11 HCLS domains. These skills, when combined with agentic AI services, can measurably improve performance. In head-to-head comparisons, agents equipped with these skills win 70–86 percent of the time, with the strongest effect observed in critical thinking (78–85 percent win rate).

The skills are provided as structured markdown documents (SKILL.md) that encode domain decision procedures into a format consumable by AI agents during inference through progressive disclosure. The collection covers skills for genomics, drug discovery, claims operations, medical imaging, and more.

Skills are categorized into reasoning skills and pipeline skills. Reasoning skills encode methodology and decision frameworks, while pipeline skills provide tool-specific commands, validated parameters, and code templates. The dual taxonomy equips agents with both the judgment to make correct decisions and the technical precision to execute them.

Unlike Retrieval Augmented Generation approaches, skills encode decision procedures and error conditions themselves. They are not fine-tuned models but structured prompts that activate contextually based on trigger patterns in the user's query. Skills are auditable, portable, and easy to maintain, as every decision criterion is human-readable in markdown format. They work across multiple services without customization and can be quickly updated in response to policy changes or new experimental criteria.