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Readme Beautifier

Community
hylarucoder
readme-beautifier

Reformats and restructures a README or similar Markdown project document without changing its factual content, voice, or scope, then summarizes formatting-only edits. Use when the user asks to beautify, tidy, organize, or professionally format a README(README 美化、排版、格式化、太乱了). Use document-audit skills to judge correctness or consistency, and hai-rewrite-doc when meaning or claims must change.

Overview

Publisherhylarucoder
Repositoryhai-stack
Skill namereadme-beautifier
Stars
284
Forks
15
Bundled files
1
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.

  • 1 bundled files

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

  • Open source

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

Installation

Install the Readme Beautifier 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.
  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/hylarucoder/hai-stack.git /tmp/hai-stack
mkdir -p .claude/skills
cp -r /tmp/hai-stack/skills/readme-beautifier .claude/skills/readme-beautifier
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Readme Beautifier 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 Readme Beautifier 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 Readme Beautifier 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.

Readme Beautifier

Overview

Take a README whose structure is messy or whose formatting is inconsistent, and deliver a version with clear structure, consistent formatting, and a professional look.

Core Principles

  • Content unchanged — nothing added, nothing removed: do not invent information, do not delete valid content; improve only at the structure and formatting level.
  • Faithful to the original meaning: preserve the author's intent and tone; do not rewrite for style.
  • Minimal change: prefer a small fix over a big one; prefer adjusting formatting over rewriting.
  • Respect the original; never force-fit: keep every reasonable choice the original already made (section order, whether to include badges / a TOC / spaces between Chinese and English), and do not proactively add what is missing — apply the checklist below under this principle instead of restating it per item.

Workflow

1. Read the README → understand what the project is and who it is for
2. Diagnose problems → check every item in the checklist below
3. Plan the changes → list what to change and why
4. Apply the beautification
5. Deliver the summary

Deliver steps 4 and 5 in the format defined under "Output" below.

Checklist

Ordered from highest to lowest priority. Fix an item only when the problem actually exists.

1. Heading hierarchy

  • Use h1 exactly once, for the project name.
  • Keep levels consecutive — never skip (h1 → h3 is wrong).
  • Align granularity across same-level headings (do not have one h2 be "Install" and another h2 be "How to mount config files with a Docker volume").

2. One-line description

  • If the original contains a one-line project description, place it directly under the project name; a blockquote or plain paragraph both work.
  • If none exists, report that as an out-of-scope content gap. Do not invent one.

3. Section structure

  • Give every section a single clear responsibility; do not mix topics.
  • A common sensible order: what it is → quick start → usage → configuration → directory layout → contributing → license.
  • Preserve empty sections and flag them as out-of-scope content gaps; deleting or filling them changes content.

4. List formatting

  • Keep item markers uniform within one list (all - or all *, never mixed).
  • Keep nesting indentation consistent (2 spaces or 4 spaces, never mixed).
  • Use 1. auto-numbering for ordered lists instead of manual numbers (avoids renumbering when items are inserted).
  • Surround lists with blank lines.

5. Code blocks

  • Tag every code block with a language (bash / yaml / ```text, etc.).
  • Wrap inline code in backticks: commands, file names, variable names, package names.
  • Strip unnecessary $ prefixes from command examples (unless input must be distinguished from output).
  • Put multi-line commands in code blocks, not inline code.

6. Tables

  • Align columns (pixel-perfect alignment is not required, but they should look tidy).
  • Make headers meaningful.
  • If a table has only two columns of short content, consider whether a list fits better.
  • If a list carries three or more parallel dimensions of information, consider whether a table fits better.

7. Links and references

  • Make link text meaningful (no "click here", no "link").
  • Check for obviously broken link formats ([text]() empty links, [text](TODO) placeholders).
  • Check that relative vs absolute paths are used sensibly.

8. Whitespace and separation

  • Put a blank line before headings.
  • Put a blank line between paragraphs.
  • Never allow more than two consecutive blank lines.
  • Spaces between Chinese and English: if the original mostly has them, fill in the gaps for consistency; if it mostly does not, follow the original.
  • Put a blank line between lists and paragraphs.
  • End the file with a single newline (no trailing blank lines, no missing newline).

9. Badges

  • Tidy badges only if the original has them: gather them right below the project name and above the one-line description.
  • Separate badges with spaces, no line breaks.

10. Table of contents (TOC)

  • Suggest adding a TOC when there are more than 6 sections, but do not add one unless the user also authorizes content additions.
  • If the original already has a TOC with broken anchors, repair it rather than delete it.

Do not

  • Do not add decorative elements: no emoji, no horizontal rules, no fancy ASCII art, unless the original already has them.
  • Do not change technical content: do not alter commands, configuration options, or the logic of code examples.
  • Do not translate: do not turn Chinese into English or the reverse.
  • Do not add content: if a "Contributing" section is missing, do not auto-create one — only mention in the summary that it could be added.
  • Do not rename the file: the output stays README.md, never something else.

Use a different skill when

This skill handles only formatting and structure, never whether the content is correct. Content-level problems are out of scope — this skill never changes meaning. When the user says "check my README", distinguish the intent:

  • The user wants to confirm the README matches the code / config / API, or whether it is stale → use hai-audit-docs.
  • The user wants an internal-consistency / stale-content audit of the docs themselves (internal contradictions, no code comparison) → use hai-audit-docs.
  • The user wants the content rewritten around the current conclusions because the doc drifted through rounds of discussion → use hai-rewrite-doc.
  • The user wants the layout beautified, the formatting unified, the structure straightened out → this skill.

Output

When the user targets a local file, edit that file and return its path plus a short summary; do not repeat a long README in chat. When the README was pasted without a writable target, return the full beautified content followed by the summary.

Use references/output-template.md for both file and pasted-content delivery shapes and for the already-clean case.

Do not duplicate its output schema here.

Edge cases

  • Very short README (< 10 lines): only fix formatting; do not pad the length.
  • Very long README (> 300 lines): fix structural problems first; fix only the most glaring formatting problems.
  • Multilingual README: beautify only the current file; do not touch other language versions.
  • README is already good: do not force changes; reply using the "check summary" format at the end of references/output-template.md.

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 Readme Beautifier AI skill do?

Reformats and restructures a README or similar Markdown project document without changing its factual content, voice, or scope, then summarizes formatting-only edits. Use when the user asks to beautify, tidy, organize, or professionally format a README(README 美化、排版、格式化、太乱了). Use document-audit skills to judge correctness or consistency, and hai-rewrite-doc when meaning or claims must change.

Why use Readme Beautifier on TypingMind?

Because you install it once and use it with any model. Readme Beautifier 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 Readme Beautifier in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/hylarucoder/hai-stack/tree/main/skills/readme-beautifier. 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 Readme Beautifier?

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 Readme Beautifier?

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

Is the Readme Beautifier AI skill free?

It is published on GitHub by hylarucoder. Check the repository for licensing terms. 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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