AI Agent Workflow Collection

Generate an interactive Shiny-based clinical trial exploration app using the R teal framework from ADaM datasets and mock TLG shells. Use this skill whenever the user has ADaM datasets and wants to create an interactive teal app for data exploration, review, or QC. Also trigger when the user mentions 'teal app', 'interactive exploration', 'Shiny clinical app', 'teal modules', 'interactive TLGs', 'clinical data explorer', 'teal dashboard', or wants an interactive review tool for their ADaM data. This skill is an alternative/complement to the static TLG generation (adam-to-tlg) — it produces a live, filterable Shiny app instead of static outputs.

Generate production-quality Tables, Listings, and Figures (TLGs) from ADaM datasets using R code with gtsummary, gt, and ggplot2, guided by mock TLG shells. Use this skill whenever the user has ADaM datasets and mock TLG shells and wants to produce the actual TLG outputs. Also trigger when the user mentions 'generate TLGs', 'create tables from ADaM', 'produce clinical study report outputs', 'TLG programming', 'table programming', 'figure programming', or wants to implement the statistical analyses defined in mock shells. This skill is the fourth step in a Protocol->SAP->Metadata->TLG Shells->ADaM->TLG pipeline.

Discover available plugins and generate 1-N execution proposals for a workflow structure. Each proposal is a complete WorkflowDefinition with executor config on every step.

Generate workflow structure (steps, transitions, triggers) from a natural language description. Outputs partial WorkflowDefinition YAML — structural elements only, without execution config (executor, agent, plugin are added later).

Generate mock TLG (Table, Listing, and Figure) shells from structured clinical trial metadata JSON. Use this skill whenever the user has trial metadata (output from the trial-metadata-extractor skill) and wants to create mock shells, TLG shells, display shells, output shells, or mock-ups for clinical study reports. Also trigger when the user mentions 'TLG generation', 'mock shells', 'display shells', 'CSR tables', 'clinical study report outputs', 'table shells', 'listing shells', 'figure shells', or wants to plan the reporting package for a clinical trial. This skill is the second step in a Protocol->SAP->Metadata->TLG pipeline and requires metadata JSON as input -- if the user provides a Protocol/SAP instead, redirect them to the trial-metadata-extractor skill first.

Generate ADaM datasets from SDTM data using {admiral} R code, driven by mock TLG shells. Use this skill whenever the user has SDTM datasets and mock TLG shells (output from mock-tlg-generator) and wants to create ADaM datasets. Also trigger when the user mentions 'ADaM derivation', 'ADaM generation', 'SDTM to ADaM', 'admiral code', 'ADaM programming', 'ADaM spec', 'create ADaM datasets', 'generate ADaM', or wants to derive analysis datasets from SDTM. This skill is the third step in a Protocol->SAP->Metadata->TLG->ADaM pipeline. It requires mock TLG shells as primary input and SDTM datasets as data input.

Extract structured metadata from clinical trial Protocol and Statistical Analysis Plan (SAP) documents into a standardized JSON format. Use this skill whenever the user provides a Protocol, SAP, or both (as file paths or uploaded files) and wants to extract study design, endpoints, populations, visit schedules, analysis plans, or any structured trial metadata. Also trigger when the user mentions 'protocol review', 'SAP review', 'trial metadata', 'study design extraction', 'endpoint extraction', 'ADaM planning', 'TLG planning', or wants to prepare inputs for mock TLG shell generation. This skill is the first step in a Protocol->SAP->Metadata->TLG pipeline. Even if the user only provides one document (Protocol OR SAP), use this skill -- it handles partial inputs gracefully and flags what's missing.