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Html Doc

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owainlewis
html-doc

Generates a polished, static HTML reading view from an existing Markdown PRD or technical design. Use when a user asks to render, present, visualize, or make a PRD or design document easier for humans to read in a browser.

Overview

Publisherowainlewis
Repositoryblueprint
Skill namehtml-doc
Stars
419
Forks
73
Bundled files
8
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 8 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by owainlewis on GitHub. Read the source before you install it.

Installation

Install the Html Doc AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/owainlewis/blueprint/tree/main/skills/html-doc
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/owainlewis/blueprint.git /tmp/blueprint
mkdir -p .claude/skills
cp -r /tmp/blueprint/skills/html-doc .claude/skills/html-doc
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Html Doc in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Html Doc on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Html Doc is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

HTML document

Turn one complete Markdown PRD or technical design into one verified static HTML page. Keep Markdown canonical. Change presentation, not meaning.

Boundaries

  • Accept an existing Markdown file or exact inline Markdown.
  • Support only prd and design document kinds.
  • For REQUIREMENTS.md, pass --kind prd. For ARCHITECTURE.md or spec.md, pass --kind design.
  • Classify prd.md as prd and design.md as design. For other names, use the request or ask if the kind is unclear.
  • Do not write requirements, settle design choices, summarize missing content, or edit the source.
  • If the source is only a rough brief, use the appropriate authoring skill first.
  • Do not hand-edit generated HTML. Change the template or renderer, then regenerate.
  • Do not publish or host the result unless the user separately asks.

Requirements

Generation requires Python 3.11 or newer, Pandoc 3 or newer, Node.js 22.12 or newer, and npm. The skill directory contains a locked Mermaid dependency tree.

Before the first Mermaid render, run:

bash
npm ci --prefix <skill-directory>

Use scripts/html_doc.py from this skill directory for all generation and lifecycle operations.

Workflow

1. Read and classify the source

Read the complete source. Confirm it is no larger than 2 MiB. Determine the document kind from the request or the basename mappings above.

Treat content problems separately from presentation problems. If the Markdown contradicts itself, omits a required decision, or contains an invalid diagram, report that source problem. Do not silently repair its meaning.

2. Create a candidate

For a file source:

bash
python3 <skill-directory>/scripts/html_doc.py render <source.md> --kind <prd-or-design> --out <output.html>

--kind may be omitted only for exact prd.md and design.md basenames. --out may be omitted to create a sibling HTML file.

For exact inline Markdown, pipe it through standard input and provide both kind and output:

bash
python3 <skill-directory>/scripts/html_doc.py render --inline --kind <prd-or-design> --out <output.html>

The command returns JSON containing candidate, output, and token. It does not replace the final output. Keep those exact values for verification and finalization.

If an output lock already exists, do not delete it automatically. It may represent another pending render. Report its path. Use discard --force-stale only when the state is known to be abandoned.

3. Verify the candidate in a real browser

Open the candidate as a local file. Do not treat source inspection as browser proof.

Check all of these:

  • desktop at 1440 CSS pixels;
  • tablet at 768 CSS pixels;
  • mobile at 390 CSS pixels;
  • A4 and US Letter print preview;
  • table-of-contents links and section targets;
  • keyboard order from skip link through navigation and document links;
  • visible focus indicators;
  • one main landmark and a sensible heading outline;
  • unique DOM IDs;
  • horizontal overflow, clipped text, code, tables, and diagrams;
  • useful diagram alternatives and accessible disclosure controls;
  • console errors and failed resource requests;
  • operation with browser network access disabled.

Compare the canonical content inside main with the Markdown abstract syntax tree. Navigation, source metadata, and derived accessibility text are not canonical content. Each top-level source block must map to one ordered data-source-block node with its SHA-256 identity and nested content intact. A Mermaid block maps to one figure whose exact source appears once in its disclosure.

If a presentation defect comes from the shared renderer or styles, fix that source and regenerate. If it is a content defect, leave the source and candidate unchanged and report it.

4. Finalize or discard

Only after every check passes, atomically install the candidate:

bash
python3 <skill-directory>/scripts/html_doc.py finalize --out <output.html> --token <token>

If any check fails, remove only this pending candidate:

bash
python3 <skill-directory>/scripts/html_doc.py discard --out <output.html> --token <token>

Finalization performs one same-directory atomic replacement. It never deletes the previous output first. A failed render, check, or replacement must leave the previous verified output byte-for-byte unchanged.

Output standard

The final page must:

  • preserve source wording and order;
  • use canonical prd or design machine metadata;
  • record the source path and SHA-256 hash;
  • use semantic HTML, inline CSS, and no runtime JavaScript;
  • contain no external runtime assets;
  • use a restrained blue-grey visual system;
  • remain readable without color, a mouse, a wide screen, or network access;
  • include responsive and print styles;
  • embed validated Mermaid SVG as a base64 image and retain the exact source in a disclosure.

Return

Stop when the verified candidate is finalized. Report the output path, source hash, and browser checks performed.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Html Doc AI skill do?

Generates a polished, static HTML reading view from an existing Markdown PRD or technical design. Use when a user asks to render, present, visualize, or make a PRD or design document easier for humans to read in a browser.

Why use Html Doc on TypingMind?

Because you install it once and use it with any model. Html Doc is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Html Doc in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/owainlewis/blueprint/tree/main/skills/html-doc. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Html Doc?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Html Doc?

As many as you like. As long as a model supports skills, you can use Html Doc with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Html Doc AI skill free?

Yes. It is published on GitHub by owainlewis under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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