Generate Teal Shiny apps from ADaM datasets and push to GitHub.
npx skills add https://github.com/Appsilon/mediforce --skill build-teal-app
Please help me install this Agent Skill. Skill: build-teal-app Source: https://github.com/Appsilon/mediforce/tree/main/apps/tealflow/plugins/tealflow/skills/build-teal-app Command: npx skills add https://github.com/Appsilon/mediforce --skill build-teal-app
This skill enables an autonomous agent to assemble and deploy a Teal Shiny app from ADaM datasets using TealFlowMCP tools, removing manual steps.
Provide the ADaM datasets path and GitHub repository, then run the Teal app generation workflow to produce app.R and data.R and push to GitHub.
High-intent search queries and answers about installing and using this skill.
To automate Teal Shiny app generation from ADaM datasets, provide the absolute dataset paths and a GitHub repository. The workflow autonomously discovers data, selects compatible modules, generates app.R and data.R, configures renv, and pushes the validated code to GitHub.
Generating reproducible Teal apps requires absolute paths to ADaM datasets and a target GitHub repository. The process autonomously sets up the renv environment, validates startup, and produces a renv.lock file alongside the app.R and data.R scripts for full reproducibility.
Yes, automatically pushing generated Teal apps to GitHub after validation is supported. The workflow handles end-to-end validation, including renv setup and startup verification with failure retries, before pushing the publish-ready outputs directly to your specified GitHub repository.
Yes, the Teal app generation workflow requires absolute paths for ADaM datasets. Providing absolute paths allows the autonomous agent to correctly discover datasets, perform module compatibility checks, and generate the necessary TealFlowMCP tools for your Shiny application.
Automated Teal app scaffolding performs module compatibility checks on ADaM data to ensure selected modules function correctly with your specific datasets. This validation step prevents runtime errors and guarantees the generated Shiny app executes successfully before pushing to GitHub.
AppsilonGenerate a Shiny-based teal app from ADaM datasets and mock TLG shells.
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