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Auto Luhmann Numberer

Community
twhsi
auto-luhmann-numberer

Assign, validate, normalize, and explain Luhmann/Zettelkasten-style project-note codes for Chinese book manuscripts, Obsidian Markdown folders, Mandala-Grid section files, HyperCard Markdown stacks, and project-note JSON to EPUB workflows. Use when Codex needs to scan folders for 部/章/節/項/目 numbering, generate stable card ids, insert branch codes without renumbering existing notes, map letter-coded branches to Obsidian Mandala sections, preserve K### keyword index cards, sanitize private catalogs into public examples, or prepare card IDs for EPUB and HyperCard output.

Overview

Publishertwhsi
Repositoryskills
Skill nameauto-luhmann-numberer
Stars
259
Forks
37
Bundled files
10
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.

  • 10 bundled files

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

  • Open source

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

Installation

Install the Auto Luhmann Numberer 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/twhsi/skills/tree/main/skills/auto-luhmann-numberer
  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/twhsi/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/auto-luhmann-numberer .claude/skills/auto-luhmann-numberer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Auto Luhmann Numberer 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 Auto Luhmann Numberer 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 Auto Luhmann Numberer 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.

自動魯曼編號機

Use this skill to manage stable writing-system addresses for book manuscripts and card boxes. Prefer the active book outline as the topic-number seed, then let Luhmann-style branches grow inside the outline without renumbering existing notes. When publishing the skill or sharing outputs, reduce real catalogs to a small public example.

Core Model

Use four separate namespaces:

  • book_topic_code: pure numeric book addresses, at most five levels: 部、章、節、項、目. Examples: 1, 1.1, 1.1.3, 1.1.3.2.1.
  • luhmann_branch_code: letter or mixed suffixes that grow from a book address. Examples: 1.1.a, 1.1.E, 1.1.c3b, 1.1.d.1.
  • mandala_section_code: local Obsidian Mandala-Grid section comments inside one Markdown file. Examples: <!--section: 1-->, <!--section: 1.7-->, <!--section: 1.8.8-->.
  • keyword_index_code: index-card ids such as K001, kept separate from topic codes.

Never merge these namespaces. A K001 card is not a chapter. A local section: 1.7 is not the book's 1.7 chapter unless the source explicitly says so.

Default Preferences

Read references/public-numbering-rules.json when you need the numbering taste, output schema, public-case policy, or Mandala section shape. The bundled example is intentionally minimal and safe to publish.

Important defaults:

  • Use book chapter numbers as the ZKII-like topic seed.
  • Preserve existing codes; do not renumber old cards just because a new card appears.
  • Use letter branches such as 1.1.a to 1.1.g for lateral inserts under a chapter.
  • Keep compact variants such as 1.1.c1, 1.1.c3a, and 1.1.c3b when they already exist.
  • Treat letter-coded Markdown files with mandala: true or <!--section: ...--> markers as Mandala containers.
  • Do not publish complete manuscript catalogs, absolute local paths, or private source titles unless the user explicitly asks for a private artifact.

Workflow

  1. Identify the source surface:

    • Folder of Markdown files.
    • Project-note JSON.
    • HyperCard Markdown stack.
    • EPUB catalog/build script.
    • One new card that needs a code.
  2. Build or load a catalog:

    • Use scripts/extract_book_codes.mjs <folder> to scan Markdown/TXT/JSON files and write a relative-path catalog.
    • Use an existing catalog JSON if provided.
    • Keep every entry's code, title, kind, source_path, depth, and any off_prefix_codes.
  3. Classify each code:

    • Pure numeric code: 部/章/節/項/目 by dot depth.
    • Code with letters: luhmann_branch.
    • K###: keyword_index.
    • <!--section: ...-->: local Mandala section.
  4. Validate:

    • Flag missing chapter numbers, duplicate codes, codes deeper than five numeric levels, and off-prefix codes.
    • Flag likely local Mandala sections so they do not pollute book-level numbering.
    • Treat off-prefix codes as "待校正或跨章引用", not immediate errors.
  5. Assign a new code:

    • If the note belongs to an existing pure numeric chapter, place it under the closest chapter.
    • If it is a lateral insert, use a Luhmann branch suffix instead of renumbering neighbors.
    • If it belongs inside a Mandala file, assign a display code like 1.1.E|1.7.
    • If it is an index concept, use the next K###.
  6. Explain the decision:

    • Return the assigned code, code kind, parent code, previous/next neighbors, source assumption, and warnings.

Common Requests

  • "Scan this folder and list every 部、章、節、項、目 code."
  • "Find missing chapter numbers and off-prefix card ids."
  • "Give this new note a Luhmann number without renumbering old cards."
  • "Convert Mandala section addresses into display ids for HyperCard."
  • "Prepare project-note JSON codes for EPUB export."

Output Shape

For a folder scan, produce both human and machine output when useful:

json
{
  "summary": {
    "chapters": ["1.1"],
    "missing": [],
    "warnings": []
  },
  "catalog": [
    {
      "code": "1.1.3",
      "title": "努力:先吃青蛙",
      "kind": "節",
      "source_path": "examples/1.1-fast-day-plan.md"
    }
  ]
}

For a single new card, return:

json
{
  "assigned_code": "1.1.E|1.7",
  "code_kind": "mandala_section",
  "parent_code": "1.1.E",
  "section_code": "1.7",
  "reason": "The note belongs inside the existing Mandala branch file for non-linear numbering.",
  "warnings": []
}

Public Sharing Rule

Before making a repository public, remove or replace:

  • Complete book catalogs that reveal the manuscript structure.
  • Absolute local paths.
  • Raw source excerpts or private chapter titles.
  • EPUB files generated from private catalogs.

Keep only references/book-chapter-card-catalog.* as a public FAST day-plan example unless the user asks for a private build.

Bundled Resources

  • references/public-numbering-rules.json: namespace rules, public-case policy, and example output contract.
  • references/book-chapter-card-catalog.json: public example catalog for 1.1 FAST 日計劃.
  • references/book-chapter-card-catalog.md: human-readable public example catalog.
  • scripts/extract_book_codes.mjs: scan a supplied folder and regenerate a relative-path catalog.
  • scripts/validate_luhmann_catalog.py: validate catalog JSON for duplicates, depth, malformed Mandala sections, and off-prefix codes.
  • scripts/build_skill_guide_epub.py: build the teaching EPUB in assets/.
  • assets/自動魯曼編號機_使用手冊_公開版_2026-06-17_03.epub: public teaching ebook.

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 Auto Luhmann Numberer AI skill do?

Assign, validate, normalize, and explain Luhmann/Zettelkasten-style project-note codes for Chinese book manuscripts, Obsidian Markdown folders, Mandala-Grid section files, HyperCard Markdown stacks, and project-note JSON to EPUB workflows. Use when Codex needs to scan folders for 部/章/節/項/目 numbering, generate stable card ids, insert branch codes without renumbering existing notes, map letter-coded branches to Obsidian Mandala sections, preserve K### keyword index cards, sanitize private catalogs into public examples, or prepare card IDs for EPUB and HyperCard output.

Why use Auto Luhmann Numberer on TypingMind?

Because you install it once and use it with any model. Auto Luhmann Numberer 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 Auto Luhmann Numberer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/twhsi/skills/tree/main/skills/auto-luhmann-numberer. 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 Auto Luhmann Numberer?

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 Auto Luhmann Numberer?

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

Is the Auto Luhmann Numberer AI skill free?

It is published on GitHub by twhsi. 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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