QingYunA/answer-me-with-html

What it solves

It eliminates the "wall of text" problem in AI agent responses by converting complex explanations into structured, visual HTML pages. It specifically solves the inefficiency of asking LLMs to write raw HTML/CSS/SVG, which is slow and token-expensive; instead, it allows the model to write a concise Markdown draft that a local CLI tool renders into a professional layout.

How it works

The system operates as an agent skill. The AI model writes a short, structured Markdown draft containing content and specific component tags (like flow, sequence, tree, or timeline). A bundled CLI tool then takes this draft and handles the heavy lifting of layout, theme application, and diagram coordinate calculation using libraries like dagre. This process reduces output tokens by roughly 7x and speeds up response time by 3.6x compared to direct HTML generation.

Who it’s for

Users of AI agents (such as Claude Code, Cursor, Codex, and OpenCode) who want highly visual, easy-to-digest summaries, architecture diagrams, and comparisons instead of long text blocks.

Highlights

• Token Efficiency: Dramatically reduces the number of output tokens required to create visual pages.
• Automated Layout: Uses code to compute panel placement and diagram coordinates, ensuring labels are never cut off.
• Visual Components: Includes specialized components for sequence diagrams, flow charts, timelines, and folder trees.
• 3b1b-style Videos: Can generate narrated explainer videos with synchronized animations and camera focus based on the same Markdown drafts.
• Writing Check: Integrates a rule set based on ASD-STE100 to flag wordy phrases and passive voice for better readability.
• Portable Output: Generates single-file HTML pages with no external dependencies that work offline.