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shiny-for-python

Guides building, styling, testing, and debugging Shiny for Python reactive web applications.

1.8k|134|Updated Jul 27, 2021
Search Tags:#Python#playwright#Plotly#Shiny#Dashboard#Reactive Programming#Web App
One-click install
npx skills add https://github.com/posit-dev/py-shiny --skill shiny-for-python
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: shiny-for-python
Source: https://github.com/posit-dev/py-shiny/tree/main/shiny/.agents/skills/shiny-for-python
Command: npx skills add https://github.com/posit-dev/py-shiny --skill shiny-for-python

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Writing Shiny for Python apps requires knowing the right reactive patterns, layout primitives, and rendering APIs; this Skill routes an AI agent to the correct reference documentation before it writes code, preventing hand-rolled workarounds like custom HTML tables, fake tabs, DOM manipulation, and blocking reactive work.

Core Features & Use Cases

  • Index of 20+ topic references: Covers reactivity (calc/effect/value/event/req/isolate), Express vs Core modes, modules, layouts, navigation, dynamic UI, theming, plots, data frames, files, chat, bookmarking, custom components, Playwright testing, debugging, and OpenTelemetry.
  • Dashboard workflow guidance: References for dashboard design, card toolbars, value boxes, accessible icons, interactive Plotly charts, and maps with visual QA checklists.
  • Use Case: Ask an agent to build an analytical dashboard with shared filters, KPI value boxes, and a Plotly chart; the agent reads the dashboard-design, layouts, and interactive-charts references and produces idiomatic Shiny code instead of ad-hoc HTML.

Quick Start

Ask the agent to build a Shiny for Python dashboard app with a sidebar filter, value boxes, and an interactive Plotly chart, and it will consult the linked references before writing the code.

Frequently Asked Questions about shiny-for-python

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

FAQPage Schema
How do I build a Shiny for Python dashboard app?▼

Start with ui.page_sidebar for shared filters, add a row of ui.value_box KPIs, then cards containing charts and a data table. Drive everything from one @reactive.calc that filters the data so all outputs stay consistent.

What is the difference between Shiny Express and Core mode?▼

Express mode treats top-level code as the UI with context-manager layout and no App() or server() function, while Core uses an explicit app_ui object plus a server(input, output, session) function. A file must use one mode or the other, never both.

How do I run slow computations without freezing a Shiny app?▼

Decorate an async function with @reactive.extended_task and invoke it from an effect, passing reactive values as arguments since the task cannot read them directly. Read the outcome with task.result() inside a reactive context and pair it with ui.input_task_button.

Can I test Shiny for Python apps with Playwright?▼

Yes, Shiny ships Playwright fixtures and controller classes for launching an app under pytest and asserting on UI elements. Enable test mode with SHINY_TESTMODE=1 to read input, output, and exported values via controller.AppTestValues.

Why is my Shiny reactive output not updating?▼

Outputs only re-run when a reactive source they read changes; reading values outside a reactive context or calling ui.update_* at server top level breaks the dependency. Wrap updates in @reactive.effect and guard missing inputs with req().

Does Shiny for Python support LLM chatbots?▼

Yes, ui.Chat provides a complete chat UI with streaming, markdown rendering, and cancellation. Pass a chatlas client like ChatOpenAI to auto-wire streaming responses and conversation history, or handle @chat.on_user_submit manually.

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