Vital Skills: Upskilling Genie Code with Healthcare and Life Sciences skills

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Summary

What: Vital Skills skills are open-source Agent Skills that give coding agents healthcare and life sciences (HLS) domain expertise. In particular, Genie Code, optimized for the full-featured Databricks platform, further benefits from the HLS-specific guidance to apply domain tools and best practices.

Problem: General agents write plausible code but lack domain discipline. They pick clinical thresholds silently and opt for generic statistical methods instead of specialized ones that are de rigueur in the domain.

Results: On BiomniBench (an assessment tool for large language model (LLM) agents used in biomedical research), Genie Code with Vital Skills scored higher on in-scope tasks that the skills supported compared to Genie Code alone. Overall, we found that Vital Skills improved consistency, accuracy and rigor. Fewer steps and checks were missed. There were fewer retries and the tools were more likely to follow the industry standard.

Code with AI on a unified, governed platform Genie Code , our coding agent optimized for Databricks, allows you to chat instead of code to perform your data and AI tasks . Critically, Genie Code’s integration with Unity Catalog ensures that the agent understands your enterprise’s data and follows the same security and governance rules as the rest of the Databricks platform. Thus, Genie Code significantly outperforms a leading coding agent by more than twice on real-world data tasks such as exploratory data analysis, machine/deep learning, creating dashboards, agents, apps, declarative automation bundles, Lakeflow jobs and scheduling them. However, Genie Code and other general AI coding assistants lack domain specificity and assume generic methods and clinical thresholds that do not meet healthcare and life sciences (HLS) standards or best practices. Databricks Vital Skills bridges this gap by providing packaged playbooks built in the open Agent Skills standard. They equip AI coding agents with industry knowledge so agents know which questions to ask, which steps are non-negotiable, and which tested tools to use. Vital Skills is designed for Genie Code to make the best use of the unified Databricks ecosystem so generated code is not disconnected from your governed data, and you're not left to stand up serving, GPUs, governance, and scheduling yourself. You get a system, not just a sandbox. With the Databricks ecosystem, you: Are grounded in all your data assets. The quality of the answer rises with the reach of the data foundation underneath it. Are governed end to end. Data, skills, and models with conversational access are all governed, tracked and secured. Get a comprehensive platform. Model Serving, GPUs, and Apps are part of the platform, with no separate software or service to buy and wire together. Have open choice. Use any cloud, any model, any framework. Design for production, not just play. The same work in prototype can be promoted into scheduled Lakeflow pipelines and jobs with real throughput.

We tested Genie Code with and without Vital Skills on BiomniBench , a public benchmark of biomedical data-analysis tasks graded against expert-written rubrics . Genie Code alone rarely failed on the obvious parts. It parsed documents and generated charts with ease. However, it sometimes fell short on domain-specific methodology and knowledge and was prone to overassumptions. With Vital Skills, Genie Code scored higher on the relevant tasks and had no negative impact on tasks that were out of scope of Vital Skills.

Average BiomniBench score (0–100), Genie Code with and without Vital Skills. 3 runs per task, graded blind by an LLM judge.

Skills built by healthcare and life sciences experts, for healthcare and life sciences Each skill started with a field engineer who had solved the problem several times with customers. We held on to three principles: Ask before assuming. When a decision is the analyst’s to make, e.g. a clinical threshold, the skill pauses, provides options, and lets the user choose. Trust-tested tools, not improvised code. For sensitive work, Genie Code runs tested tools and code rather than writing new logic each time. Prove it. Every skill was run side by side against Genie Code without it, on the same tasks.

A catalog that spans the healthcare and life sciences value chain Vital Skills puts the know-how of a biostatistician, privacy officer, bioinformatician, or actuary directly in the hands of every analyst, helping teams move faster before engaging additional expertise. The Databricks Healthcare and Life Sciences field team has built skills across the industry, covering key steps in data processing, analysis, model life cycle management, evaluation, report and slide generation. These skills can be used directly, or further customized to your needs and to leverage your deep expertise in your discipline. We expect this starter set of skills to grow over time and welcome contributions to the open source repository . Skill What it does Bioinformatics bulk-rnaseq Find top differentially expressed genes from bulk RNA-seq counts. open-weight-models Package, register, validate & deploy open-weight HLS models on Databricks, e.g. TEDDY and Geneformer. pathway-enrichment-analysis Pathway enrichment for RNA-seq (ORA or GSEA). sc-rnaseq Analyze single-cell RNA-seq data from .h5ad files. Clinical cohort-builder Build a reproducible, feasibility-checked patient cohort from coded + free-text clinical data. rwe-cohortstudy Comparative effectiveness (cohort study) with propensity adjustment (matching, IPW). semantic-builder Build a semantic layer like Metric Views using 64 common measures from EHR or claims so they are dashboard or agent ready phi-deidentifier HIPAA Safe-Harbor de-identify a Unity Catalog table (k-anonymity + governed view). Others presentation-builder Build presentation-ready slides report-writer Move your analysis methods and results to a report accelerators Route to a relevant HLS accelerator or library with the right tools for your task skill-eval Benchmark a skill: paired runs with/without it, MLflow genai.evaluate scoring, win/loss + failure taxonomy.

Seamlessly get from data to results to a finished product, all on Databricks Go from data to a finished report or agent or dashboard seamlessly on Databricks. For example, our semantic-builder skill defines and computes commonly used...