Booth Loop Video logo

Booth Loop Video

Organization
microsoft
booth-loop-video

Use this skill whenever the user asks for a looping booth, kiosk, trade-show, lobby-screen, or silent background video — including requests phrased as "a video loop for our stand", "an animated explainer with no voiceover", "a motion graphic for the monitor", or "turn this pitch into a looping MP4". Generate and run a self-contained Python render script (Pillow + ffmpeg) that outputs a 1920x1080 30fps MP4. Do NOT use this skill for videos that need narration, live footage, or editing of an existing video file.

Overview

Publishermicrosoft
Repositorycat-agent-skills
Skill namebooth-loop-video
Stars
95
Forks
115
Bundled files
1
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.

  • 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 microsoft on GitHub. Read the source before you install it.

Installation

Install the Booth Loop Video 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/microsoft/cat-agent-skills/tree/main/submissions/booth-loop-video
  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/microsoft/cat-agent-skills.git /tmp/cat-agent-skills
mkdir -p .claude/skills
cp -r /tmp/cat-agent-skills/submissions/booth-loop-video .claude/skills/booth-loop-video
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Booth Loop Video 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 Booth Loop Video 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 Booth Loop Video 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.

Booth / kiosk loop video

Produce a silent, looping MP4 (default 1920x1080, 30 fps, 60-135 s) suitable for a conference stand, a lobby screen, a reception kiosk, a LinkedIn post, or an embedded slide in a deck.

The output is a rendered animation, not a slideshow export. Every frame is painted in Python, so layout, timing, and easing are fully under your control.

Execution model — do the work, don't hand out instructions

You have Python and a shell. Never tell the user to open an editor or run the script themselves. You:

  1. Write a single self-contained script at work/booth_video.py.
  2. Install dependencies if missing: pip install pillow imageio imageio-ffmpeg numpy (imageio-ffmpeg ships its own ffmpeg binary — no system install needed).
  3. Render preview PNGs and show them to the user.
  4. Only after previews look right, run the full render.
  5. Report the output path.

All paths are relative to the current working directory:

{CWD}/
  work/
    booth_video.py
    preview/scene_1.png ...
  output/
    booth_loop.mp4

Write intermediate frames to the system temp directory, not the working folder. Rendering thousands of PNGs into a cloud-synced folder (OneDrive, Dropbox, iCloud) will stall the render and thrash the sync client.

Design defaults

Use these unless the user supplies a brand palette. Ask for their colours if the video is customer-facing; don't invent a brand.

Canvas1920x1080, 30 fps
Duration60-135 s
Backgrounddeep navy #0A1628
Accents#0078D4 primary, #B4009E secondary
Cards#112244, 18 px corner radius, ~86% alpha
Typehumanist sans, generous whitespace
Motion0.5 s fade in, 0.3 s fade out per scene, smooth-step easing

Dark, low-saturation background with two saturated accents reads well on a bright show floor and survives poor monitor calibration.

Script architecture

One file. A render_frame(t, total) function that returns a Pillow Image for time t in seconds, and a scene table:

python
SCENES = [
    (0.0,  8.0,  scene_hook),
    (8.0,  22.0, scene_problem),
    (22.0, 40.0, scene_how_it_works),
    # ...
    (108.0, 120.0, scene_cta),
]

def render_frame(t, total):
    img = Image.new("RGBA", (W, H), BG)
    draw = ImageDraw.Draw(img)
    for start, end, fn in SCENES:
        if start <= t < end:
            fn(draw, img, t - start, end - start)
    return img

Time-relative scene functions (local_t, duration) make it trivial to reorder or retime scenes later without touching their internals.

Animation helpers — always include these

python
def ease_in_out(t):  return t * t * (3 - 2 * t)
def lerp(a, b, t):   return a + (b - a) * t
def clamp(v, lo, hi): return max(lo, min(hi, v))
def fade(t, start, end): return clamp((t - start) / (end - start + 1e-6), 0.0, 1.0)

Combine them: alpha = ease_in_out(fade(local_t, 0, 0.5)) * (1 - fade(local_t, dur - 0.3, dur)) gives a clean in/out envelope for any element.

Cross-platform font loading

Never hardcode a single font path — it will fail on another machine. Probe a candidate list per weight and fall back gracefully:

python
import os
from PIL import ImageFont

FONT_DIRS = [
    "C:/Windows/Fonts",                       # Windows
    "/usr/share/fonts/truetype/dejavu",       # Linux
    "/usr/share/fonts/truetype/liberation",   # Linux
    "/System/Library/Fonts/Supplemental",     # macOS
    "/Library/Fonts",                         # macOS
]

CANDIDATES = {
    "light":    ["segoeuil.ttf", "HelveticaNeue.ttc", "DejaVuSans-ExtraLight.ttf", "arial.ttf"],
    "regular":  ["segoeui.ttf", "Helvetica.ttc", "DejaVuSans.ttf", "LiberationSans-Regular.ttf", "arial.ttf"],
    "semibold": ["seguisb.ttf", "DejaVuSans-Bold.ttf", "LiberationSans-Bold.ttf", "arialbd.ttf"],
    "bold":     ["segoeuib.ttf", "DejaVuSans-Bold.ttf", "LiberationSans-Bold.ttf", "arialbd.ttf"],
    "mono":     ["consola.ttf", "Menlo.ttc", "DejaVuSansMono.ttf", "cour.ttf"],
}

def get_font(weight, size):
    for name in CANDIDATES.get(weight, CANDIDATES["regular"]):
        for d in FONT_DIRS:
            p = os.path.join(d, name)
            if os.path.exists(p):
                try:
                    return ImageFont.truetype(p, size)
                except OSError:
                    continue
    return ImageFont.load_default()

Print which font actually resolved on the first call. A silent fall back to load_default() produces a tiny bitmap font and a video that looks broken — you want to know before the full render, not after.

Z-order rule — the one that bites

Pillow paints in call order: later calls sit on top. Draw connectors before the things they connect.

For any hub-and-spoke, node-and-edge, or step-and-arrow layout, split into two passes:

python
# Pass 1 — background layer: every connector
for item in items:
    draw.line([hub_xy, item.xy], fill=LINE, width=2)

# Pass 2 — foreground layer: every node
draw_hub(draw, hub_xy)
for item in items:
    draw_card(draw, item.xy, item.label)

Interleaving the two passes draws lines across cards and through label text. This is the single most common defect in generated diagrams-in-motion, and it is invisible until you look at a rendered frame — which is why previews are mandatory.

Scene planning

Design for someone walking past at 3 m who gives you eight seconds.

  1. Hook (0-8 s) — one bold headline, one idea. No body copy. If a passer-by can't get the point from this scene alone, the video has already failed.
  2. Body scenes (8 s onward) — one concept per scene, 10-18 s each. Card layouts, animated counters, progress bars, typed-text reveals. Never more than ~25 words on screen at once.
  3. CTA (last 8-12 s) — what to do next, plus a name, booth number, or short URL.

End the CTA so it cuts cleanly back to the hook — the loop point should be invisible. Either fade fully to background colour, or make the first and last frames identical.

Contrast rules

  • Dark card on dark background is unreadable on a show floor. For any chat, answer, or quote UI, put the response on a white or near-white card with dark text.
  • Never place accent-coloured text on the accent-coloured fill.
  • Check contrast on a preview PNG, not in your head. Show-floor lighting and cheap panels both crush shadow detail.

Preview before rendering

Full renders take minutes. Preview takes seconds. Always:

python
if PREVIEW:
    os.makedirs("work/preview", exist_ok=True)
    for i, (start, end, _) in enumerate(SCENES, 1):
        mid = (start + end) / 2
        render_frame(mid, TOTAL).convert("RGB").save(f"work/preview/scene_{i}.png")
    raise SystemExit

Display every preview inline to the user with markdown image syntax and get confirmation before the full render. URL-encode any spaces in the path.

Render loop

python
import imageio, numpy as np

writer = imageio.get_writer(
    output_path, fps=FPS, codec="libx264",
    output_params=["-crf", "18", "-pix_fmt", "yuv420p"],
)
for idx in range(int(TOTAL * FPS)):
    t = idx / FPS
    writer.append_data(np.array(render_frame(t, TOTAL).convert("RGB")))
    if idx % max(1, int(TOTAL * FPS / 20)) == 0:
        print(f"{100 * idx / (TOTAL * FPS):.0f}%", flush=True)
writer.close()

-pix_fmt yuv420p is not optional — without it the file will not play in QuickTime, PowerPoint, or most hardware media players, even though VLC handles it fine.

Iteration

After the first render, offer to tweak and accept plain-language feedback ("slower orbit", "bigger headline", "green instead of magenta"). Edit the script, re-render the affected scene as a preview PNG first, then re-render the video. Never re-render the full video to check a colour change.

Quality checklist before delivering

  • Resolved a real TrueType font, not load_default()
  • No text overflowing a card boundary or the canvas
  • No label collisions in radial / orbit layouts
  • All connectors drawn before all nodes (z-order)
  • Readable contrast on every scene's preview PNG
  • Loop point is seamless — last frame flows into first
  • Encoded with yuv420p; plays outside VLC
  • Reasonable file size (< 200 MB for ~120 s)
  • Reported the absolute output path to the user

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 Booth Loop Video AI skill do?

Use this skill whenever the user asks for a looping booth, kiosk, trade-show, lobby-screen, or silent background video — including requests phrased as "a video loop for our stand", "an animated explainer with no voiceover", "a motion graphic for the monitor", or "turn this pitch into a looping MP4". Generate and run a self-contained Python render script (Pillow + ffmpeg) that outputs a 1920x1080 30fps MP4. Do NOT use this skill for videos that need narration, live footage, or editing of an existing video file.

Why use Booth Loop Video on TypingMind?

Because you install it once and use it with any model. Booth Loop Video 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 Booth Loop Video in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/microsoft/cat-agent-skills/tree/main/submissions/booth-loop-video. 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 Booth Loop Video?

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 Booth Loop Video?

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

Is the Booth Loop Video AI skill free?

Yes. It is published on GitHub by microsoft 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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