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Live Panel

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ythx-101
live-panel

Build a terminal-style, always-running animated architecture diagram (a "live panel") from a JSON config and render it to an mp4. Use when the user wants a system, agent or org architecture diagram that looks like a monitoring dashboard in a terminal window - fixed layout, glowing packets on the wires, scrolling log, counters, bars that flip state - as a screen-recording-style video or a live web page, instead of a static picture or a step-by-step reveal. 中文:当用户要做"活的"架构图、agent 树或系统监控面板风格的动画(mp4 视频或循环网页)时使用——终端里一直在跑的监控面板外观,固定布局、线上流动的数据包、滚动日志、翻转的计数器和状态条;不是静态图,也不是分步演示。

Overview

Publisherythx-101
Repositorylive-panel-skill
Skill namelive-panel
Stars
692
Forks
92
Bundled files
22
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.

  • 22 bundled files

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

  • Open source

    Published by ythx-101 on GitHub. Read the source before you install it.

Installation

Install the Live Panel 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/ythx-101/live-panel-skill/tree/main
  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/ythx-101/live-panel-skill.git \
  .claude/skills/live-panel
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Live Panel 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 Live Panel 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 Live Panel 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.

live-panel

Turn a description of a system into an always-running architecture diagram: one fixed page that looks like a monitoring panel of a running system. Every frame is a complete, readable diagram; what moves is system state, not decoration. Two built-in themes: terminal-dark (the original look) and light-pastel (white background, rounded pastel boxes - for infographics, e.g. Xiaohongshu). Canvas presets: 4:5 (X, 1200x1500), 3:4 (Xiaohongshu, 1080x1440), 1:1 (1080x1080). Intended as the base for posts on X and Xiaohongshu.

Credit. The look and the motion idea come from an architecture-diagram clip by @thedelost (a Codex agent-tree panel, generated with GPT and screen-recorded as a web page): https://x.com/thedelost/status/2105398038026195279 . It reached many people through a quote-post by @slashui: https://x.com/slashui/status/2105850132365443528 . This skill is an independent re-implementation of the method (the motion grammar was learned by analysing that clip). examples/codex-agents/ is a recreation of the original picture and stays the original author's design. Keep this credit when you publish output made with the skill's Codex example.

When to use

  • The user asks for an architecture / agent-tree / org-chart / data-flow diagram that should feel alive (video, GIF-like loop, or live page).
  • The user has a description of a system with a few boxes, a flow between them, a few "on call" side triggers, a log and some numbers.
  • Not for: diagrams that need interaction, real telemetry, or step-by-step explanations (use a slide or a normal diagram tool).

Input

A single JSON config (schema: references/config-schema.md). It holds everything: canvas size, colours, boxes with lines of text, wires, packet paths, side-rail triggers, log wording, numbers. The template never needs editing. Start from the closest example: examples/codex-agents/config.json (terminal-dark, 4:5, recreation of @thedelost's picture), examples/airbnb/config.json (terminal-dark, Chinese content, figures from an interview) or examples/agent-architecture/config.json (light-pastel, 3:4, a static Xiaohongshu infographic made to move, original by 小红书 @林纾). Theme: theme.preset; canvas: canvas.preset. Positions are absolute canvas pixels, so another aspect ratio needs its own config.

Fixed procedure

  1. Collect content and where each number comes from. List boxes, who talks to whom, which things are "on call" triggers, what the log would say. Write the source (link or "simulated") next to every number. If a number has no real source, it is simulated: say so on screen (a footer line, or "(illustrative)" next to the counter). Never invent a figure and present it as real.
  2. Write the config. Copy an example. Place boxes on a pixel grid (x, y, w, h in canvas px); text lines flow inside boxes at one fixed line height. Put the credit/source line in credit. Read references/motion-grammar.md before choosing machines and periods.
  3. Render. python3 scripts/render.py --config my.json --out my.mp4 (needs Chrome/Chromium and ffmpeg on PATH, or --chrome / --ffmpeg; on macOS the browser is also found under /Applications/*.app). Linux and macOS only - on Windows run the scripts inside WSL. --html-out page.html also keeps the self-contained page, which runs live in any browser.
  4. Check by frames, not by trust. python3 scripts/check_frames.py --config my.json --out-dir frames --repeat samples ~120 time points, measures text overflow and overlaps from the DOM, exports PNGs and proves replay determinism. Open the PNGs and look: the checker catches geometry, not taste. Fix the config and repeat until it exits 0.

Motion rules in one screen

(Full text and rationale: references/motion-grammar.md.)

  • Layout never moves. No camera, no step-by-step reveal. Frame 0 is already a complete diagram.
  • Three tempos at once: fast (packets with trails on every wire, spinners, fast counters), medium (log scrolls with newest line bright and older grey; bars re-roll and flip colour and label past a threshold), slow (side-rail triggers light up one at a time in order, their arrow changes colour and carries a packet, advice is typed out, totals accumulate).
  • One truth everywhere. The number in the log is the number on the bar; the trigger lit in the rail is the one the log mentions; counters in the status bar equal counters in boxes. In this engine that holds by construction: log lines are generated from the same state machines as the boxes.
  • No real data? Say "illustrative". Fixed facts stay fixed; only the animation's own counters move, and they are labelled.

Determinism (why it renders the same every time)

The page exposes window.seek(t). Every visual is a pure function of t (and a fixed integer seed for the few "random" picks): no wall clock, no Math.random, no CSS animation. The renderer calls seek(i/fps) and screenshots; check_frames.py --repeat seeks away and back and compares bytes. Opened in a normal browser (no ?manual), the same function is driven by requestAnimationFrame and loops.

Files

  • assets/template.html - the generic page. scripts/render.py - mp4. scripts/check_frames.py - checks + PNGs. scripts/livepanel.py - shared helpers (Chrome over the DevTools pipe, stdlib only).
  • references/motion-grammar.md, references/config-schema.md.
  • examples/codex-agents/, examples/airbnb/, examples/agent-architecture/ - config, mp4 and screenshots.

Light theme notes

For infographics keep the original colours and structure; motion is gentler: small packets drift along every arrow, state-bound arrows turn the accent colour and carry two packets, active boxes get a glow, lists (steps, tools) light one row at a time from one shared cycle machine so that everything on screen refers to the same step. Use container:true on boxes that hold other boxes so the checker does not call nested boxes overlapping, and inside:true on icon text placed on a box.

Credit rule

Always put the original author and link of any picture you re-animate or recreate in the video footer (credit) and in the post. State when a figure is simulated.

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 Live Panel AI skill do?

Build a terminal-style, always-running animated architecture diagram (a "live panel") from a JSON config and render it to an mp4. Use when the user wants a system, agent or org architecture diagram that looks like a monitoring dashboard in a terminal window - fixed layout, glowing packets on the wires, scrolling log, counters, bars that flip state - as a screen-recording-style video or a live web page, instead of a static picture or a step-by-step reveal. 中文:当用户要做"活的"架构图、agent 树或系统监控面板风格的动画(mp4 视频或循环网页)时使用——终端里一直在跑的监控面板外观,固定布局、线上流动的数据包、滚动日志、翻转的计数器和状态条;不是静态图,也不是分步演示。

Why use Live Panel on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ythx-101/live-panel-skill/tree/main. 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 Live Panel?

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 Live Panel?

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

Is the Live Panel AI skill free?

It is published on GitHub by ythx-101. 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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