Generate an OpenMRS ESM microfrontend scaffold from the DHTI template.
npx skills add https://github.com/dermatologist/dhti --skill conch-generator
Please help me install this Agent Skill. Skill: conch-generator Source: https://github.com/dermatologist/dhti/tree/main/.agents/skills/conch-generator Command: npx skills add https://github.com/dermatologist/dhti --skill conch-generator
This skill helps AI agents scaffold a brand-new OpenMRS ESM microfrontend (DHTI conch) from this repository template, enabling rapid creation of DHTI-enabled healthcare apps.
Use the skill to generate a new OpenMRS ESM microfrontend from this template by following the instructions in SKILL.md and the Examples.
High-intent search queries and answers about installing and using this skill.
To scaffold an OpenMRS ESM microfrontend, generate a complete project structure from the DHTI template. This automatically configures the workspace layout, package naming, routing, and config-schema files to accelerate frontend development.
A DHTI conch is a newly scaffolded OpenMRS ESM microfrontend generated from a specific repository template. It enables the rapid creation of DHTI-enabled healthcare apps by wiring frontend modules to GenAI-powered backend services.
Yes, the scaffolding process supports GenAI-powered backend integration by wiring the new microfrontend to DHTI Elixir services. It also configures the necessary translation and configuration schemas for the workflow.
Generating an OpenMRS ESM module requires updating package.json, index.ts, config-schema.ts, and translation files. You must also adjust moduleName, featureName, and planning notes to fulfill the clinical feature workflow.
Yes, the generator guides environment setup and project scaffolding, including workspace layout and package naming. It also configures routing, translations, and plans to fulfill the specific clinical feature workflow.
Scaffolding from the DHTI template is specifically designed for OpenMRS ESM microfrontends and DHTI service integration. It focuses on initializing project structure and configuration, meaning you must manually implement the actual clinical feature logic afterward.
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