scroll-world

scroll-world

Produces a landing page where scroll drives a camera: it dives from outside a scene into its interior, then flies out and into the next scene, continuously, with no visible cuts. The visuals are AI-generated — stills via Higgsfield (or Codex), the video chain via Monid by default (pay-per-clip Seedance 2.0; Higgsfield credits as fallback) — and the page just scrubs pre-rendered video by scroll position. This is the same technique behind Apple's scroll-through product pages — the camera genuinely moves, scroll only drives time.

What you generate: N scene stills → N "dive-in" camera clips → N-1 "connector" clips that join consecutive scenes seamlessly → a portable scrub engine that plays the whole chain as one flight.

The one rule that makes or breaks it: seams must be frame-identical. Read The seamless chain before generating any connector. Getting this wrong is the single most common failure and produces a visible "pop" between scenes.

Do not assume a frontend framework. The scrub engine in references/scrub-engine.js is self-contained vanilla JS (it builds its own DOM + injects its own CSS into a container you give it), so it drops into plain HTML, Next.js, Vue, a Python-served page, anything. The value of this skill is the Higgsfield pipeline, the prompts, and the seam method — not the framework.

Step 0 — Bootstrap

1. Monid CLI — the default video-chain backend. Check monid --version, monid keys list (active key) and monid balance — the chain is billed per clip in USD (Step 1.7 has the numbers; a 1080p N=6 chain ≈ $27). If the CLI is missing or the balance can't cover the chain, say so and fall back to rendering the chain on Higgsfield credits instead — same model, same pipeline, different biller (Step 4 → Monid backend).
2. Higgsfield CLI — still required even on the Monid path: it renders the scene stills (gpt_image_2) and is the home of the kling3_0 NSFW fallback and the fallback chain. If higgsfield is not on $PATH, install per the higgsfield-generate skill. If higgsfield workspace list fails auth, ask the user to run higgsfield auth login (interactive OAuth — you cannot run it) and, if needed, higgsfield workspace set <id>. Confirm credits cover the stills (~N image gens) — plus (2N-1) video gens if the chain falls back here.
3. ffmpeg / ffprobe on $PATH (frame extraction + encoding).
4. An image tool for background knockout if you want floating scenes: PIL (python3 -c "import PIL"), or cwebp/sips. Optional — see Step 3.
5. (Optional) Codex CLI — if codex is on $PATH (≥ 0.125) and codex login status reports a ChatGPT login, the scene stills can be generated through Codex's built-in image_gen (the same gpt-image-2 model) billed to the user's ChatGPT subscription instead of Higgsfield credits — offer it at Step 1.7, command in Step 2. Absence just removes the option.
6. Caveats: macOS ships bash 3.2 (no declare -A); don't use associative arrays in scripts. Higgsfield generations take 3–8 min each — always run them detached (background) and poll, never a foreground blocking call. Reference-by-job-UUID is rejected by media flags — pass local file paths to --image/--start-image/--end-image. Video models differ in accepted params (e.g. Kling has no --resolution) and in whether they support start/end-image conditioning at all — before batching, confirm the chosen model's schema with higgsfield model get <job_type> and see the Step 4 model table.