r-advanced

Package advanced R/Shiny implementation patterns into reusable templates from commit history and tests.

6|Updated Apr 8, 2026
One-click install
npx skills add https://github.com/blankuzr/R-Skills --skill r-advanced
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: r-advanced
Source: https://github.com/blankuzr/R-Skills/tree/main/gpt/skills/r-advanced
Command: npx skills add https://github.com/blankuzr/R-Skills --skill r-advanced

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

Advanced R and Shiny implementation patterns to turn recurring friction into reusable templates, grounded in commit history, tests, logs, and current package documentation.

Core Features & Use Cases

  • Deterministic patterns for Shiny state management, module testing, data engineering, and reusable modeling or reporting helpers.
  • Evidence-driven guidance drawn from repo history and references to seed durable workbenches.
  • Reference-ready templates for model builders, registry usage, and publication-ready outputs across analytic workflows.

Quick Start

Run scripts/r_repo_commit_scan.py <repo-path> to seed the evidence map and begin reusing patterns.

Frequently Asked Questions about r-advanced

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I extract reusable Shiny patterns from commit history and tests?▼

You can extract reusable Shiny patterns by scanning your repository's commit history, logs, and package documentation to identify recurring friction points and generalize them into a cohesive, registry-driven workbench template.

What's the best way to build a deterministic registry for Shiny module state management?▼

Building a deterministic registry for Shiny state management involves applying a registry-driven approach with helper scripts and tests, turning recurring data engineering and module testing friction into durable, reference-ready templates.

How do I package advanced R Shiny workflows into reusable model builders and data registries?▼

Packaging advanced R Shiny workflows into reusable components requires grounding implementation patterns in commit history and current package documentation, then applying them across model builders, data registries, and workflow guardrails.

Can I use commit history to generate publication-ready outputs and reporting helpers in R?▼

Yes, you can use commit history to generate publication-ready outputs by seeding an evidence map that guides the creation of reference-ready templates and reusable reporting helpers across your analytic workflows.

Why does my Shiny app need workflow guardrails and how do I implement them deterministically?▼

Shiny apps need workflow guardrails to prevent recurring implementation friction, and you can implement them deterministically by applying a registry-driven pattern with helper scripts, references, and tests.

Does r-advanced require external dependencies to package R Shiny workbench patterns?▼

The r-advanced Skill operates with no external dependencies, relying solely on its internal scripts and references to package advanced R and Shiny implementation patterns into a reusable, registry-driven workbench.