Agent skill · Testing & QA

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 pi

gooseworks-aigithub.com/gooseworks-aiGitHub ↗
claude-codecodexcursorships scriptsMIT
Install
npx skills add gooseworks-ai/goose-skills --skill render-model-comparison-grid --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 6
SKILL.md size: 3 KB
Bundled scripts: yes
Path: skills/ads/capabilities/render-model-comparison-grid/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 1,091
Language: Python

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# 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`.

What's inside
Steps it walks through
  1. Run
  2. Contract
Ships with 5 files
  • scripts/build_composition.py
  • scripts/config.example.json
  • scripts/render_seekable_hyperframe.py
  • skill.meta.json
  • tests/smoke-test.md
More from goose-skills
All skills →
About this skill
What does the render-model-comparison-grid skill do?

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 pi

How do I install it?

Run `npx skills add gooseworks-ai/goose-skills --skill render-model-comparison-grid --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From gooseworks-ai/goose-skills, a repository with 1,091 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

Keep going