AI Replies As HTML Pages: This Skill Cuts Token Consumption 8x
Chat-based tools get a pattern for presenting AI output as structured HTML rather than raw text, and the eightfold token reduction lowers API costs directly.
An open-source Skill named `answer-me-with-html` replaces Markdown replies from AI models with HTML visualizations. The model outputs a structured representation that the Skill converts locally into a rendered page: sequence diagrams, state diagrams, comparison tables, and reminder boxes appear at a glance. The same prompt and model produce a completely different presentation format than plain text.
Token consumption drops eightfold because the model only emits the structure and flow; HTML generation happens on the local side. Different questions yield different page structures and visual layouts. The Skill is available at answer-me-with-html.com.
Moving HTML rendering from the model to local code shifts token spend from presentation to substance, which makes the eightfold reduction plausible—the model no longer writes verbose Markdown or HTML tags.
The Skill demonstrates a general approach: AI tools can offload formatting to client-side processors, leaving the model to emit minimal structured data, a trade-off that could become a standard way to cut inference costs while improving output quality.