create-image-fal
Generate or edit an image via any FAL image model (nano-banana edit, gpt-image, flux, ...), ROUTED THROUGH THE fal-proxy so it bills the Ads agent. image_urls…
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,
$ npx -y skills add gooseworks-ai/goose-skills --skill render-model-comparison-grid --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/render-model-comparison-gridContext preview
The summary Claude sees to decide when to auto-load this skill.
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,
name: render-model-comparison-grid description: 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. status: active
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`.
build_composition.py --config config.json --output hyperframe.html ; render_seekable_hyperframe.py hyperframe.html master-silent.mp4 <duration> --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.
capabilities.
(Playwright scrubbing traps delayed animations in pre-state).
ProRes to H.264 first). An images-only grid has no decode dependency.
Put your AI agent on the growth team. Research customers and competitors, analyze what is working, create the next campaign, and learn from the result.
Repo: gooseworks-ai/goose-skills
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