Render Model Comparison Grid

OrganizationPopular

gooseworks-ai

render-model-comparison-grid

Render a 'model comparison grid' video from a config — a fal-style "same prompt, N contenders" showcase — a dark real-DOM stage where per beat a monospace prompt fades in centered, docks to a small top strip, then a labeled 2-4 panel grid (static images OR muted video clips, mixable per cell) staggers in and holds for comparison, plus a minimal end card — frame-stepped via Playwright (video cells are frame-seeked deterministically) and encoded with FFmpeg. Deterministic assembly, FREE (cell media comes from create-image-fal / create-video-fal, music from create-music-elevenlabs), text stays pixel-crisp. Use for the model-comparison-grid format.

Overview

Publisher	gooseworks-ai
Repository	`goose-skills`
Skill name	`render-model-comparison-grid`
Stars	1.2K
Forks	208
Bundled files	5
License	MIT
Links	Skill source Repository Homepage

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.
5 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by gooseworks-ai on GitHub. Read the source before you install it.

Installation

Install the Render Model Comparison Grid AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/gooseworks-ai/goose-skills/tree/main/skills/ads/capabilities/render-model-comparison-grid

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

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/gooseworks-ai/goose-skills.git /tmp/goose-skills
mkdir -p .claude/skills
cp -r /tmp/goose-skills/skills/ads/capabilities/render-model-comparison-grid .claude/skills/render-model-comparison-grid

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Render Model Comparison Grid 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 Render Model Comparison Grid 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 Render Model Comparison Grid 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.

render-model-comparison-grid

Render the 'model comparison grid' format from a config. The signature of this format is a "Same prompt. N models." gauntlet: a dark stage where, per beat, a PROMPT eyebrow + the (condensed) prompt fades in centered in monospace and holds readable ~0.8s, then docks to a small top strip while a grid of 2-4 labeled panels staggers in (0.15s apart) and holds for side-by-side comparison. A persistent model/variant label sits under each panel; column order is identical on every beat. Ends on a minimal end card (headline + column names only — no meta-stats line).

The grid is media-agnostic per cell: any cell is a static image or a muted video clip (i2v outputs, screen recordings), mixable within one beat. Video cells loop during the hold and are frame-seeked deterministically (the renderer awaits each seek), so the render never depends on wall-clock playback timing.

The renderer itself is FREE/deterministic (Playwright frame-step + FFmpeg). The paid inputs are separate capabilities: the cell images come from create-image-fal, the cell clips from create-video-fal, and the music bed from create-music-elevenlabs. Prompt text and labels are real DOM — never AI-rendered.

Default shape: 5 beats × 4.5s + 2.5s end card = 25.0s @ 1280×720/30fps, all configurable from one config.json.

Run

build_composition.py --config config.json --output hyperframe.html ; render_seekable_hyperframe.py hyperframe.html master-silent.mp4 --fps 30 --width 1280 --height 720 — dark stage, staggered grid, deterministic, $0. The config schema is documented at the top of scripts/build_composition.py; scripts/config.example.json IS the shipped worked example (re-point the cell paths at your own media).

build_composition.py validates every cell path and the column count (2-4), infers each cell's media type from its extension (.png/.jpg/.jpeg/.webp → image; .mp4/.mov/.webm/.m4v → muted video), and emits a self-contained HTML that exposes window.mediaReady() + window.renderAt(t). render_seekable_hyperframe.py awaits both, so <video> cells seek to the right frame before each screenshot — never a frozen first frame.

Contract

Deterministic + FREE (Playwright frame-step + FFmpeg); no paid calls in this capability.
Columns = panels-per-beat (2-4); every beat supplies exactly that many cells, same order.
The template recipe (DB) supplies the config; cell images/clips + music are separate capabilities.
State is computed entirely in renderAt(t) — never CSS animation-delay/transitions (Playwright scrubbing traps delayed animations in pre-state).
Video cells must decode in the render Chromium (H.264 yes, ProRes no — transcode .mov ProRes to H.264 first). An images-only grid has no decode dependency.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

scripts/build_composition.py10 KB
scripts/config.example.json4 KB
scripts/render_seekable_hyperframe.py3 KB
skill.meta.json1 KB
tests/smoke-test.md1 KB

Frequently asked questions

What does the Render Model Comparison Grid AI skill do?

Why use Render Model Comparison Grid on TypingMind?

Because you install it once and use it with any model. Render Model Comparison Grid 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 Render Model Comparison Grid in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/gooseworks-ai/goose-skills/tree/main/skills/ads/capabilities/render-model-comparison-grid. 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 Render Model Comparison Grid?

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 Render Model Comparison Grid?

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

Is the Render Model Comparison Grid AI skill free?

Yes. It is published on GitHub by gooseworks-ai 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

render-model-comparison-grid

Run

Contract

Bundled files

Frequently asked questions

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