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…
Assemble the FREE steps of the flat-vector-explainer video format — a flat-illustration creator-character walks a countable N-step product routine, one step per beat, and Remotion composites every chip/numeral/tagline/slate/CTA as an animated DOM overlay ON TOP of the Kling i2v
$ npx -y skills add gooseworks-ai/goose-skills --skill render-flat-vector-explainer --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/render-flat-vector-explainerContext preview
The summary Claude sees to decide when to auto-load this skill.
Assemble the FREE steps of the flat-vector-explainer video format — a flat-illustration creator-character walks a countable N-step product routine, one step per beat, and Remotion composites every chip/numeral/tagline/slate/CTA as an animated DOM overlay ON TOP of the Kling i2v
name: render-flat-vector-explainer description: Assemble the FREE steps of the flat-vector-explainer video format — a flat-illustration creator-character walks a countable N-step product routine, one step per beat, and Remotion composites every chip/numeral/tagline/slate/CTA as an animated DOM overlay ON TOP of the Kling i2v character clips (text is NEVER baked into a keyframe — i2v warps type), the closing 'N products' grid is a PIL composite of the REAL product photos (not AI), full-sentence VO drives word-by-word burned captions over a VO-forward music bed, and the ~50s animated silent master is re-cut to a 30s deliverable FROM the animated master (never a static intermediate). Documentation-grade — ships config.example.json + PIPELINE.md + a README of the free assembly; the paid gen steps (keyframes, Kling i2v, VO, music) are separate capabilities the recipe orchestrates. Use for the flat-vector-explainer format. status: active
Assembles a flat-vector product-routine explainer: one illustrated creator-character walks through a countable N-step routine (e.g. collagen -> serum -> eye cream -> hair), one step per beat, each beat carrying a large corner numeral, a labelled chip + one-line tagline, and the step's real product photo, closing on an "N products" grid + brand CTA. It reads as a premium DTC explainer (Spotify/Anchor flat-vector lineage), not UGC.
This capability is **documentation-grade**. The content-goose molecule is a documented recipe, not a runnable end-to-end app, so this capability ships the **config schema** (`scripts/config.example.json`), the **field-to-script map** (`scripts/PIPELINE.md`), and a **README** (`scripts/README.md`) describing the FREE assembly steps the agent runs by hand with ffmpeg + Remotion + PIL. The paid generative steps are separate capabilities the recipe orchestrates and gates.
1. **Motion layer != text layer.** Animate a **text-stripped clean plate** with Kling i2v (subtle motion, style-preserving negative, cfg 0.5), then composite every chip / numeral / tagline / slate / CTA as an **animated Remotion DOM overlay** on top. Baking text into the keyframe before i2v warps the type and forfeits the ability to retime/restyle it — this separation is the format's whole credibility. 2. **Real assets != AI assets.** The per-step product photo and the closing "N products" grid are **real product webps composited with PIL** (AI duplicates SKUs in a grid). Only the character vignettes and stylized backgrounds are generative.
The agent runs these deterministic, $0 steps by hand — see `scripts/README.md` for the ffmpeg/Remotion/PIL detail:
The recipe orchestrates and gates these; they are not part of this capability:
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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