Today, with the rapid development of AI technology, large language models have been able to answer various complex questions. However, a new problem gradually emerged:AI can generate vast amounts of information, but it doesn’t necessarily make it easy for people to understand.
When faced with technical architecture, system design, process analysis or comparison of multiple solutions, traditional chat-style answers often require users to read large paragraphs of text, repeatedly sort out the logic, or even draw their own flow charts to truly understand.
Is there a way for AI to not only answer questions, but also turn the answers directly into a clear, intuitive, and easy-to-read visual document?
Open source projects on GitHub Answer me with HTML It is designed to solve this problem.
Project address:https://github.com/QingYunA/answer-me-with-html
The core idea is simple: let an AI agent answer complex questions with a page of HTML, rather than filling a chat window with text.
1. Project Introduction: From chat answers to visual explanations
Answer me with HTML is a Skill for AI Agent. After the user raises a question, the Agent generates a concise draft of the Markdown content, and then converts it into a complete HTML page through the project's own CLI tool.
The final generated page can contain:
- Structured text and descriptions
- Table vs. Multiple Plan Comparison
- Flowcharts and Sequence Diagrams
- code snippet
- Timeline and architectural relationships
- Interactive annotations and decision options
For example, users can directly ask the AI Agent:
Explain how the TCP three-way handshake works.
Traditional AI might output a text that walks through SYN, SYN-ACK, and ACK.
After using Answer me with HTML, the Agent can generate HTML pages containing communication sequence diagrams, status changes, and key field descriptions.
Users no longer need to extract logic from long texts themselves, but can quickly understand the entire process through graphics and structured content.
2. Core design: Why not let AI write HTML directly?
This is the most noteworthy design idea of the entire project.
Normally, if we want AI to output a web page, we will directly ask the model to generate HTML, CSS, or even SVG graphics.
Although this approach is feasible, there are obvious problems.
The model not only requires organizing the content, but also requires writing a lot of page structure, style code, and graphics coordinates. Really valuable knowledge explanations are instead occupied by a large amount of repetitive front-end code.
Answer me with HTML adopts a more reasonable division of labor:
AI is responsible for content and CLI is responsible for presentation.
The overall process is as follows:
- Users ask complex questions.
- AI Agent analyzes questions and organizes answers.
- Agent outputs a Markdown draft.
- The CLI parses special components and chart syntax in Markdown.
- The CLI automates page layout, styling, and graphics drawing.
- Finally, a separate HTML file is generated.
This approach decouples content creation from page rendering, allowing the model to focus on the information that really matters.
The project README gives its own benchmark results: under certain test conditions, directly generating HTML requires an average of about 4,893 output tokens, while using the project to generate a Markdown draft averages about 612 tokens.
This means that in this test, the number of model output tokens was reduced to about one-eighth of the original number.
It should be noted that these data come from the project's own testing and do not mean that all models, tasks and environments can achieve the same performance improvement.
3. Core functions: not just an HTML generator
Answer me with HTML is not simply about converting Markdown into a web page. It focuses more on how to choose appropriate visual expressions based on different information types.
1. Automatic layout and chart generation
Projects support a variety of chart types, including:
| chart type | applicable scenarios |
|---|---|
| Flow | System architecture, business processes, decision branches |
| Sequence | Communication process between multiple systems |
| ER | Database entities and relationships |
| Tree | Directory structure, module hierarchy |
| Timeline | Historical events, project phases |
| Limits | Indicators and constraints |
| Annot | Text analysis and sentence-by-sentence annotation |
| KV | Key information display |
For example, when explaining a complex back-end system, AI can use a flow chart to show the complete path of a request from the client to the system, through the gateway, business services, cache and database.
Compared with plain text, this expression is more suitable for analyzing dependencies between modules.
2. Multiple themes and dark and light color modes
There are a variety of visual themes built into the project:
- Blueprint: Engineering design style, suitable for architectural diagrams and technical processes.
- Shadcn: adopts modern card-style design.
- Paper: Suitable for long reading and document display.
Supports both light and dark modes.
Users can choose an appropriate theme based on the type of content to make the page both readable and maintain a good visual experience.
3. Single file HTML, supports offline reading
This is a very useful feature.
The HTML pages generated by the project can be saved independently and do not rely on CDN or external font resources.
This means:
- The page can be opened in a local browser.
- Can be shared with others as a file.
- No need to deploy a server.
- Can be read offline.
This format has high practical value for technical documentation, internal knowledge sharing and project analysis.
4. Page interaction and feedback
The generated pages are not completely static.
Users can comment on different content on the page and answer decision-making questions raised by the Agent.
The page collects this feedback and generates a Markdown message for the user to copy back to the AI Agent.
This makes the HTML page not only a display carrier of answers, but also a medium for further discussion between humans and AI.
5. Explain video generation
In addition to HTML pages, the project also supports the generation of explanatory videos.
Users can ask the Agent to:
Explain the TCP three-way handshake in a way similar to 3Blue1Brown.
The Agent generates corresponding content drafts and storyboards, which are then transformed by the CLI into pages with step-by-step animation, narration, and chapter navigation.
This approach is particularly suitable for explaining abstract concepts, communication protocols, and complex processes.
4. Technical implementation: Markdown-driven rendering architecture
From the design of the project, its core architecture can be summarized into three parts.
The first layer: AI Agent
Responsible for understanding user issues and generating structured Markdown content.
Markdown contains not only ordinary text, but also special syntax defined by the project, such as flowcharts, sequence diagrams, and layout properties.
Second layer: CLI rendering engine
Project comes with am The CLI is responsible for converting Markdown drafts into HTML.
What it handles includes:
- Markdown parsing
- Page template selection
- layout calculation
- Chart drawing
- themes and styles
- HTML output
Project use marked Parse Markdown and use @dagrejs/dagre Make a flowchart layout.
Among them, the layout calculation of graphics is completed by the program instead of requiring the model to manually calculate SVG coordinates.
This reduces common layout errors when the model generates graphics.
The third layer: HTML page
The final output is a standalone HTML file.
The page not only contains the final explanation, but also embeds the Markdown source that generated it.
Users can regain the original content through the functions in the page, making it easy to modify, reuse and further edit.
The overall architecture embodies an idea worth learning from:
Combine the semantic understanding that large models are good at with the deterministic computation that programs are good at.
5. How to install and use?
The project supports a variety of AI Agents, including Claude Code, Codex, Cursor, OpenCode, etc.
Method 1: Use unified installation command
npx skills add QingYunA/answer-me-with-htmlDuring the installation process, you can select the Agent that needs to be connected.
Note that using this project requires Node.js 20 or higher.
Method 2: Claude Code plug-in installation
Execute in Claude Code:
/plugin marketplace add QingYunA/answer-me-with-html
/plugin install answer-me-with-html@answer-me-with-htmlOnce the installation is complete, you can ask questions directly to the Agent.
For example:
解释 Redis 和 Memcached 的区别，
生成一个可视化 HTML 页面。Or:
分析当前代码仓库的模块结构，
用流程图展示模块之间的依赖关系。Agent will generate corresponding pages based on the questions.
6. What application scenarios is it suitable for?
The value of Answer me with HTML isn't limited to developers.
technical learning
For abstract concepts such as TCP, HTTP, database indexes, and operating systems, the cost of understanding can be reduced through diagrams and structured pages.
Code warehouse analysis
For large projects, module trees, call diagrams and architecture descriptions can be generated to help developers quickly understand the project.
Technical plan review
When comparing Redis vs. Memcached, different databases, or different architectural options, you can organize the pros, cons, limitations, and final conclusions into a single page.
project planning
Integrate task breakdown, execution process, key nodes and decision-making items into a visual plan that is easy to discuss.
Knowledge sharing
Convert AI answers into stand-alone HTML documents that can be shared directly with team members or used as the basis for technical blogs and learning materials.
7. Value and limitations of the project
The most valuable thing about Answer me with HTML is not that it generates HTML, but that it rethinks the way AI outputs content.
The traditional chat model emphasizes continuous dialogue, while this project emphasizes the organization, presentation and reading experience of answers.
It reduces the model's consumption of repetitive front-end code through a clear division of labor between content and rendering, and transforms complex information into a more understandable visual structure.
Of course, it also has certain usage thresholds and limitations:
- A supported AI Agent needs to be installed and configured.
- Requires Node.js environment.
- Page performance depends on the quality of content generated by Agent.
- For simple questions, generating HTML may not be necessary.
- Complex charts still require reasonable data and semantic design.
Therefore, it is more suitable for complex questions, technical analysis and knowledge explanation, rather than replacing all ordinary chat answers.
8. Summary: The next step for AI is not just to answer questions
Answer me with HTML shows an interesting AI application direction:
Let AI not only generate answers, but also proactively choose expressions that are more suitable for human understanding.
In an era where information is becoming increasingly abundant, the truly important capability is not just to generate more content, but to help users understand the content faster.
Markdown is responsible for expressing ideas, CLI is responsible for completing deterministic layout and rendering, and HTML becomes the ultimate knowledge carrier.
This design allows AI Agent to move further from a simple text generation tool to a knowledge organization and visual explanation tool.
For developers, Answer me with HTML is not only an open source project worth trying, but also provides a design concept worth thinking about:
Instead of letting a large model repeat the work that the program can do reliably, let the model focus on the parts that really require intelligence.
Project address:
https://github.com/QingYunA/answer-me-with-html
If you often use AI for technical learning, code analysis or solution design, you might as well try this project to turn complex problems from a paragraph of text into a page that can actually be read.