Display logo

Display

Community
NatsuFox
display

Expose the organized knowledge base through a readable frontend experience. Use when a user wants to browse the knowledge base visually as a lightweight site instead of reading raw Markdown files directly. Supports building viewers for specific data paths (e.g., individual books) or the entire knowledge base.

Overview

PublisherNatsuFox
RepositoryTapestry
Skill namedisplay
Stars
64
Forks
5
Bundled files
4
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.

  • 4 bundled files

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

  • Open source

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

Installation

Install the Display 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/NatsuFox/Tapestry.git /tmp/Tapestry
mkdir -p .claude/skills
cp -r /tmp/Tapestry/skills/tapestry/display .claude/skills/display
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Display 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 Display 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 Display 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.

Tapestry Display

Publish a readable frontend for the knowledge base: $ARGUMENTS

When to use this skill

Use this skill when:

  • A user wants to browse the knowledge base visually
  • You need to generate a readable frontend for the organized content
  • The user asks to "view", "display", "publish", or "preview" the knowledge base or a specific book
  • A lightweight site presentation is preferred over raw Markdown files
  • The user wants a blog-like or research portal view of the content

Purpose

This skill acts as the presentation layer for the knowledge base.

It should:

  • scan the specified data directory hierarchy (or default to _data/books/)
  • preserve the topic and chapter structure defined by index.md
  • generate a readable frontend that feels closer to a blog, notebook, or research portal than to a file browser
  • support building viewers for specific books or the entire knowledge base

Workflow

  1. Resolve the data path from the argument:

    • If a specific path is provided (e.g., "markets-and-trading"), use _data/books/markets-and-trading
    • If no argument is provided, default to _data/books/ (entire knowledge base)
  2. Ensure the data directory exists and contains markdown files

  3. Publish the frontend bundle with the appropriate data path:

bash
# For a specific book
python display/_scripts/publish_viewer.py --data-path _data/books/markets-and-trading --force

# For the entire knowledge base (default)
python display/_scripts/publish_viewer.py --force
  1. IMPORTANT: The viewer is created at <data-path>/_viewer. Serve that generated directory directly:
bash
# Serve from the viewer directory (entire KB — default, no --data-path)
python -m http.server 8766 --directory _data/books/_viewer

# Serve from the viewer directory (specific book)
python -m http.server 8766 --directory _data/books/markets-and-trading/_viewer

Critical: When the goal is to display ALL knowledge bases, always build without --data-path and serve _data/books/_viewer. Serving from a topic-specific _viewer/ will show only that one topic. If you used --data-path by mistake, re-run publish_viewer.py --force (without --data-path), kill the old server, and restart from _data/books/_viewer.

  1. Report back with:
    • the data source path
    • the viewer output directory
    • the generated manifest path
    • the local preview URL if served

Rules

  • Treat the index.md hierarchy as the authoritative structural map.
  • Do not flatten the topic/chapter tree into a single undifferentiated list.
  • Preserve topic-level separation so semantically distant materials remain clearly separated.
  • Make documents readable first, but keep enough structural information visible for navigation.
  • If the knowledge base is sparse or incomplete, still generate the frontend and let empty sections remain honest rather than faking content.

Common Issues

Viewer Shows Only One Topic Instead of All Knowledge Bases

Symptom: The viewer loads but only displays one topic (e.g., dingyi-dex-weekly) even though multiple KB topics exist under _data/books/.

Root cause: publish_viewer.py was called with --data-path _data/books/<topic>, which scopes the viewer to just that topic. The generated _viewer/ inside the topic directory is then served instead of the full KB viewer.

Solution:

bash
# 1. Rebuild the full-KB viewer (no --data-path)
python display/_scripts/publish_viewer.py --force

# 2. Kill any old server processes
fuser -k <port>/tcp   # or: kill <PID>

# 3. Serve from the FULL books/_viewer, not a topic-specific one
python -m http.server <port> --directory _data/books/_viewer

Rule: To show ALL KB topics, always build and serve from _data/books/_viewer. Only use --data-path when intentionally scoping to a single book.

Data Path Not Found

The generated viewer expects data/knowledge-base.json under <data-path>/_viewer/. If you see a JSON parsing error like "Unexpected token '<'", it usually means _ui/ was served directly instead of the published _viewer/ output.

Solution: Re-run publish_viewer.py and serve <data-path>/_viewer.

Resources

  • display/_scripts/publish_viewer.py: scans _data/books/, copies frontend assets, and generates the JSON manifest.
  • _ui/: custom static frontend assets for the viewer.

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 Display AI skill do?

Expose the organized knowledge base through a readable frontend experience. Use when a user wants to browse the knowledge base visually as a lightweight site instead of reading raw Markdown files directly. Supports building viewers for specific data paths (e.g., individual books) or the entire knowledge base.

Why use Display on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/NatsuFox/Tapestry/tree/main/skills/tapestry/display. 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 Display?

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 Display?

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

Is the Display AI skill free?

Yes. It is published on GitHub by NatsuFox 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇