visual-pass

What problem does it solve?

This Skill identifies opportunities within educational content (notebooks, tutorials, articles) to strategically inject visual assets, improving comprehension and engagement without directly creating the visuals.

Core Features & Use Cases

• Content Analysis: Scans text and code to detect specific signals indicating a need for visuals (e.g., descriptions of architecture, processes, data comparisons).
• Visual Type Identification: Recommends appropriate visual types (static diagrams, animations, interactive charts, before/after panels, etc.) based on content signals.
• Prioritized Injection Plan: Generates a ranked list of visual opportunities, respecting budget constraints and content type.
• Delegation: Creates detailed "visual intents" that specify what needs to be rendered and how, delegating the actual production to other tools or skills (e.g., inline-svg-architecture-diagrams, Manim MCP, ComfyUI MCP).
• Use Case: When writing a technical tutorial, this Skill can analyze the draft and produce a manifest detailing exactly where to add architecture diagrams, animations explaining complex processes, and before/after comparisons of code performance, along with precise instructions for each visual.

Quick Start

Analyze the provided Jupyter notebook 'data-processing-pipeline.ipynb' to identify visual asset injection points.