Improving HCLS AI reasoning with open-source agent skills

AI agents built on foundation models (FMs) often misapply healthcare and life sciences (HCLS) decision frameworks, even when they've seen the guidelines in training and in the system prompt. Ask an agent to classify a TP53 missense variant using ACMG/AMP criteria. It will cite the correct framework but misapply evidence categories, skip population frequency thresholds, or hallucinate computational predictor scores. The model knows facts but lacks the structured reasoning procedures that domain practitioners internalize over years of training. The gap produces silent failures across variant interpretation, claims adjudication, clinical trial design, and imaging analysis. Outputs look correct but apply wrong criteria, with regulatory and patient safety consequences.

In this post, we share a collection of 38 open source agent skills spanning 11 HCLS domains that help close this methodology gap. We walk through installation and show how to use them across agentic AI services. We share our evaluation results to demonstrate measurable improvement across drug discovery, healthcare operations, and medical imaging workflows. Agents equipped with these skills win 70–86 percent of head-to-head comparisons against the same agents without skills, varying agent harness setup. The strongest effect is on critical thinking (78–85 percent win rate, d = 0.65–1.03). We also show you how you can customize, extend, and create your own agent skills for your specific use case.

Agent skills in the HCLS Agent Skills collection are structured markdown documents (SKILL.md) that encode domain decision procedures into a format AI agents can consume at inference time through progressive disclosure. Following the Agent Skills open standard, each skill declares triggers, dependencies, and metadata in YAML frontmatter. The content that follows contains decision frameworks, parameter tables, code patterns, and validation criteria. The collection covers 38 skills across 11 HCLS domains including genomics, drug discovery, claims operations, and medical imaging. Refer to the full skill catalog for the complete list organized by domain. All are released under the MIT-0 license.

Skills in this repository are sorted as either reasoning or pipeline skills. Reasoning skills encode methodology and decision frameworks that guide how the agent thinks. For example, the genomic-variant-interpretation skill encodes the full ACMG/AMP classification framework such as evidence categories, population frequency thresholds, and computational predictor cutoffs. Pipeline skills encode tool-specific commands, validated parameters, and code templates that produce runnable artifacts. The variant-calling skill provides GATK4 HaplotypeCaller commands with correct annotation groups, VQSR tranche sensitivity targets, and Mutect2 tumor-normal configurations.

This dual taxonomy gives agents both the judgment to make correct decisions and the technical precision to execute them. Unlike Retrieval Augmented Generation (RAG), which retrieves limited passages from indexed documents to augment the response generation, skills encode the decision procedure and error conditions itself. Skills are not fine-tuning either. They're structured prompts that activate contextually based on trigger patterns in the user's query.

Three properties make skills distinct from other approaches to domain specialization. Skills are auditable, portable, and straightforward to maintain. Every decision criterion is human-readable in markdown format, not hidden in model weights. A skill works across over 20 services (Amazon Bedrock AgentCore, AWS Strands Agents SDK, Kiro, Amazon Quick Desktop, Claude Code, OpenAI Codex and more) without customization to each. Annual medical policy changes or new experiment criteria can be reflected quickly by editing a text file, not retraining a model.

Now that you understand what skills contain, let's set them up.

To follow along with the examples in this post, you need one of the supported services from AWS: Kiro or Kiro CLI for interactive skill use and multi-agent orchestration, the AWS Strands Agents SDK with Amazon Bedrock foundation model access, AgentCore harness, a capability of Amazon Bedrock AgentCore, with an existing agent implementation, or Quick Desktop for GUI-based skill management. You can also use a coding agent harness of your choice such as Claude Code or OpenAI Codex. You also need Python 3.10+ with uv, and Git for cloning the repository.

For Kiro, the install.sh script installs both skills and a pre-configured agent that equips them. The agent handles skill routing automatically, so you don't need to invoke individual skills by name. Run ./install.sh --target kiro, then switch to the agent in Kiro CLI with /agent hcls. For multi-agent mode, run ./install.sh --target kiro --mode multiagent and use /agent hcls-multiagent.

For AgentCore, follow Skills to add agent skills to an AgentCore-hosted agent. AgentCore provides managed hosting, auto scaling, security boundaries, and observability capabilities.

For Amazon Quick Desktop, run ./install.sh --target quick-desktop to see the full instructions for adding skills in the graphical interface. Alternatively, follow the instructions in Skills in the Amazon Quick documentation.

With skills installed, we demonstrate three deployment patterns: the simplest single-agent approach in Quick Desktop, multi-agent orchestration in Kiro CLI that addresses context engineering challenges, and production deployment with Strands SDK on Amazon Bedrock AgentCore. We then show three sample use cases that highlight the measurable difference skills make in real HCLS workflows.

With skills installed, Quick Desktop's agent gains structured HCLS domain reasoning without additional configuration. When you ask a domain question, the agent automatically activates relevant skills based on trigger patterns in your query. For example, asking "What is the RAF impact of coding E11.9 instead of E11.42?" triggers the risk-adjustment skill and the agent responds with specific HCC mappings, hierarchy resolution, and quantified RAF deltas rather than a generic suggestion to "review documentation." Skills are activated selectively. Only the relevant skill is triggered for the response, helping keep output focused and accurate. The following video shows a skill dynamically loaded for the question in Amazon Quick Desktop.

Loading all 38 skills into a single agent context consumes ~80K tokens. This is workable with large-context models, but it creates a context engineering challenge. The agent must select the right subset from 38 available skills on every query and irrelevant skill content competes for attention. An alternative is explicit skill invocation (for example, /risk-adjustment), but this requires you to know which skill to invoke before asking your question, which is exactly the expertise gap skills are meant to bridge.

Kiro CLI's multi-agent architecture solves both problems. A lightweight coordinator agent (no skills loaded) routes queries to eight domain specialists, each loading only its relevant skills (approximately 15K tokens per specialist). The coordinator handles intent classification while the specialists handle domain reasoning. The specialization can be defined as described in the following table.

The multi-agent configuration is defined in JSON agent files. Refer to the coordinator agent config for the routing logic, and a specialist agent config for an example of how domain skills are attached to a specialist. The following video shows multiagent and dynamic skill activation answering a complex drug repurposing question while working in a code base in Kiro CLI.

After your skill-equipped agent works locally, you can move it to production. Amazon Bedrock AgentCore provides an alternative path to inject skills into hosted agents. In addition to embedding them in the Strands agent code, you can configure skills at the environment level so they're