Turn Research into Visual Docs with the html Skill

Where this fits

You just finished a deep investigation into a new database indexing strategy. Your notes are scattered across markdown files, chat logs, and a whiteboard photo. Now your team needs a clear, visual explanation they can open in a browser without a build step. The html skill from mathbullet/skills turns that raw research into a polished, self-contained HTML document—complete with a consistent design system, math rendering, and Japanese-friendly typography.

Why agents benefit

• Generates standalone HTML artifacts – The agent produces a single .html file plus linked CSS/JS assets that render directly in any browser. No Node, no bundler, no preview server required.
• Applies a consistent visual design system – Before writing, the agent checks design-system/component-samples.html and uses document.css and math-copy.js, so every output matches the same typography, spacing, and interactive math-copy behavior.
• Follows deterministic file conventions – Files are named {yyyymmdd}-{kebab-case}.html, and existing files keep their names on update. This makes output predictable for downstream automation or CI checks.
• Composes with writing-quality skills – The skill recommends pairing with explain for terminology and structure, japanese-tech-writing for Japanese prose norms, and cognitive-rhythm-writing for long-form readability. The agent can invoke these when context demands.
• Respects higher-level constraints – If an orchestrating skill specifies output paths, metadata, or a viewer, the html skill defers to those rules, making it safe to embed in larger agent workflows.

Practical scenarios

Documenting a system architecture decision

Your agent researches a microservices migration and produces an HTML page with comparison tables, sequence diagrams (via embedded SVG), and callout boxes—all styled with the shared design system. The file lands in your repo with a date-stamped name, ready for code review.

Creating a technical tutorial for a Japanese-speaking team

Combine with japanese-tech-writing to generate a step-by-step guide that uses proper である調/ですます調 consistency, technical term glossaries, and code blocks with copy buttons. The output is a single HTML file your team can open offline.

Building an interactive math explanation

The skill automatically includes math-copy.js, so formulas rendered in MathML or KaTeX become one-click copyable. Your agent can turn a research note about gradient descent into a visual explainer with annotated equations.

Add it to your agent workflow

Install the skill globally or per-project with:

npx skills add mathbullet/skills --skill html

Then instruct your agent: “Research X and create an HTML explanation document using the html skill.” The agent will check the design system, write the content, and output a dated .html file in the current working directory. If you have a preferred output folder, specify it in the prompt.

Compare before adopting

Before committing, evaluate the skill on:

• Quality signals – Does the generated HTML actually look polished? Check the component samples in the repo to see if the design system matches your taste.
• Maintenance freshness – The repo has 119 stars and recent activity; verify the last commit date and whether issues are addressed.
• Alternatives – Other skills generate PDFs or Markdown. If you need browser-native interactivity, HTML wins; if you need print-ready output, look elsewhere.
• Workflow fit – Does your agent already have output conventions? The skill’s deference to higher-level rules makes it adaptable, but confirm it aligns with your pipeline.

Why it is worth tracking

With 119 stars and a focused design system, html signals a maturing niche: agents producing human-readable artifacts, not just text. Its explicit composition with writing skills suggests a thoughtful ecosystem approach. Track it if you frequently need visual, self-contained documentation—and revisit when your agent’s output needs to be shared with stakeholders who expect design consistency. For more agent skills and community patterns, see OpenAgentSkill. Evaluate it in a small task first; if the output feels right, fold it into your standard research-to-documentation pipeline.