Scroll WorldSAFE

A skill that turn any brand into a scrollable 3D world landing page

The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
name: scroll-world
description: >
  Build an immersive scroll-scrubbed "fly through the world" landing page for any
  industry or brand using Higgsfield. As the visitor scrolls, a pre-rendered camera
  flies from outside each scene into its interior, then flows on to the next scene
  with NO cuts — one continuous connected flight (Emons-style isometric diorama world,
  or any art direction you pick). The skill interviews the user for the topic, the
  story beats/sections, and brand kit, then generates cohesive scenes + seamless camera
  clips with Higgsfield and wires a portable, framework-agnostic scroll-scrub engine.
  The video chain renders through Monid by default (Seedance 2.0, pay-per-clip
  USD — capability re-checked each build, see Step 4) with Higgsfield credits as
  the fallback biller. Use when the user wants a "3D world" /
  "browse-through-the-industry" hero, a scroll cinematic, a diorama landing, or to
  turn a business into a scrollable world.
allowed-tools: Bash, Read, Write, Edit, AskUserQuestion, Skill
---

# 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](#step-5--the-seamless-chain-the-critical-part) 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.

---

## Step 1 — Interview the user

The **subject is the user's to state — ask it as an open question in plain prose**, never a
fabricated multiple-choice. A made-up list of industries biases them and reads as you
deciding their business for them; let them answer in their own words (their real business,
a client's, or any idea). Reserve structured multiple-choice (`AskUserQuestion` in Claude
Code; a plain either/or question elsewhere) for the genuinely
enumerable, lower-stakes choices below — art direction, camera style, and brand-kit
approach — and even
there, signal they can go their own way ("Other"). Ask only what you can't sensibly
default. Cover:

1. **Subject** (ask openly, not multiple-choice) — "What should this world be about? Your
   business, a client's, or any idea — a word or a sentence is fine." Capture the
   industry/product + a one-line pitch (e.g. "a bubble tea company, from leaf to last
   sip"), and a brand name if they have one; otherwise you'll propose one below.
2. **Brand kit** — offer three paths, pick one:
   - Import from a URL: `higgsfield marketing-studio brand-kits fetch --url <site> --wait`
     (pulls name, colours, tone). Then read it back with `brand-kits list --json`.
   - The user hands you palette + name + tone directly.
   - You propose a pa

SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.

What the source does

Filesystem

none-observed

Network

declared (1 observation(s))

Shell

none-observed

Dependencies

pinned

Secrets in source

none-found

Findings (0)

No findings outside the package's declared scope.

Gates applied: no_behavioural_pass.

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