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FacundoDuranDev
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presentation-development

Build Jupyter notebook UI panels for the Spatial-Iteration-Engine presentation layer.

Updated Feb 4, 2026
Search Tags:#diagnostics#presentation#jupyter-notebook#ipywidgets#engine-integration#notebook-ui
One-click install
npx skills add https://github.com/FacundoDuranDev/Spatial-Iteration-Engine --skill presentation-development
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: presentation-development
Source: https://github.com/FacundoDuranDev/Spatial-Iteration-Engine/tree/main/.claude/skills/presentation-development
Command: npx skills add https://github.com/FacundoDuranDev/Spatial-Iteration-Engine --skill presentation-development

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Facilitates adding, modifying, or debugging Jupyter notebook UI panels, control widgets, diagnostics panels, and example notebooks for the presentation layer of the engine.

Core Features & Use Cases

  • Provides a cohesive set of components and patterns for building notebook-based interfaces (e.g., build_general_control_panel, build_control_panel, build_diagnostics_panel) that wire into the engine via the notebook API.
  • Encourages a clear separation of presentation from pipeline logic by returning a dict of widgets and guarding imports when used in notebooks.
  • Offers examples and notebooks that demonstrate complete control panels, diagnostics, and factory functions to instantiate a ready-to-run StreamEngine for notebook exploration.

Quick Start

Install ipywidgets and IPython, then import the notebook UI builders and render a live engine panel.

Frequently Asked Questions about presentation-development

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

FAQPage Schema
How do I build interactive Jupyter notebook control panels for my engine?▼

You build interactive Jupyter notebook control panels using UI builders like build_control_panel that wire into the engine via the notebook API, returning a dict of widgets for programmatic use.

What is the best way to separate notebook UI presentation from pipeline logic?▼

Separating notebook UI presentation from pipeline logic is achieved by returning a dict of widgets and using guarded imports, ensuring the interface remains distinct from the core engine processing.

How do I create diagnostics panels in ipywidgets for engine exploration?▼

Create diagnostics panels in ipywidgets using the build_diagnostics_panel builder pattern, which connects directly to the engine to provide live feedback for notebook exploration.

Why do I need to use the stop-modify-restart pattern when debugging notebook UI panels?▼

The stop-modify-restart pattern enforces safe presentation practices during debugging by preventing runtime conflicts when modifying and re-rendering live notebook UI panels.

Can I instantiate a ready-to-run engine directly inside a Jupyter notebook?▼

Yes, you can instantiate a ready-to-run StreamEngine directly inside a Jupyter notebook using provided factory functions that connect the engine to your interactive presentation panels.

Do I need to install ipywidgets to use notebook UI builders?▼

Yes, installing ipywidgets and IPython is required before importing the notebook UI builders to render live engine control panels and diagnostics interfaces.

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