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 (Higgsfield); 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. Higgsfield CLI. 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 there are enough credits: a full run is roughly N image gens + (2N-1) video gens.
2. ffmpeg / ffprobe on $PATH (frame extraction + encoding).
3. An image tool for background knockout if you want floating scenes: PIL (python3 -c "import PIL"), or cwebp/sips. Optional — see Step 3.
4. (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 1.6, command in Step 2. Absence just removes the option.
5. 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 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 palette + name and let them approve. Capture 4–6 named hex values, a display name, and a tone word or two.

3. Art direction — default is "soft matte low-poly clay diorama, isometric, tilt-shift miniature, warm light." Offer alternatives (flat papercraft, glossy toy, claymation, neon night). Whatever is chosen becomes the shared style preamble reused verbatim in every scene prompt (this is what makes the world cohesive).

4. The journey (sections) — the ordered scenes the camera flies through. Propose a set derived from the subject's own value chain and let the user edit. 5–7 works well. Boba example: farms → pearl kitchen → flagship shop → delivery → community plaza → the hero product. Each section needs: a short subject description (what's IN the diorama), an eyebrow, a headline, one line of body, and 0–3 tag pills. The last section is usually the hero product + the CTA.

5. Mobile version — ALWAYS ask this; never silently generate both. Ask as a two-option choice (AskUserQuestion in Claude Code; a plain question elsewhere): "Want a mobile-optimized version too? The mobile version is a second camera chain rendered natively in 9:16 portrait — composed for phones, not a crop of the landscape film — which roughly doubles the Higgsfield credit spend (state the estimated number)." Options: "Desktop only" / "Desktop + mobile (native 9:16 — ~2× credits)". The credit cost must be stated to the user, not just implied. What the answer gates:

   • Yes → render the parallel 9:16 portrait chain and ship it as the mobile variants (Step 6 / pipeline.md §6b): portrait start canvases → 9:16 dives + connectors frame-locked against their own renders → 720-wide -m.mp4 encodes → stillMobile portrait posters. Wire clipMobile/connectorsMobile/stillMobile (Step 7); run the full mobile QA (Step 8). Budget ~2N-1 extra video gens + NSFW re-rolls. Never ship the centre-crop as the mobile version by default — if credits can't cover the portrait chain, say so and offer the crop encodes (pipeline.md §6) as an explicitly-labelled stopgap the user must approve.
   • No → skip the mobile encodes and wiring entirely. The engine's phone hardening (seek-coalescing, iOS priming, safe-area CSS) is always on regardless — that's not a "mobile version," it's just the page not breaking when a phone visits — so a desktop-only build still degrades gracefully.

6. Budget — engines shown by cost, decided before anything renders. Present the render tiers (AskUserQuestion), then compute and state the estimated total for the user's N scenes — N stills + (2N−1) videos [videos ×2 if mobile] + ~15% re-roll headroom — and get a go before generating.

   • Video tier (roster only — every option frame-locks seams, Step 4):

     Tier	Model	Rough cost
     Draft / previz	seedance_2_0_mini (720p)	~¼ of Standard
     Standard (default)	seedance_2_0 (1080p)	baseline
     Alternate	kling3_0 (720p native)	≈ Standard; different look + content filter

     Draft doubles as the previz path: run the whole chain cheap, approve the journey, re-render final legs on Standard (pipeline.md Notes) — suggest it unprompted when the balance reads tight.

   • Stills source (only offer if the Codex CLI is present, Step 0.4): Higgsfield gpt_image_2 (spends credits) vs Codex image_gen — the same gpt-image-2 model billed to the ChatGPT subscription (zero credits; counts toward Codex usage limits; 1536×1024 output — exactly 3:2, slightly under Higgsfield's 2k). Stills are plain PNGs handed to --start-image, so the video chain is indifferent to their source. Command in Step 2. One source for all N stills of a build — the two render with slightly different character (verified: Codex runs warmer/lighter), and mixing sources across scenes reads as style drift, same reason the video chain uses one model.

   • Calibrate costs, don't guess. The CLI exposes no pricing and plans differ. Run ONE still and ONE video first, diff higgsfield workspace list before/ after, extrapolate to the full run, and warn the user whenever the estimate exceeds ~70% of the balance. (Observed on a plus plan, 2026-07: Standard video ≈ 40–55 credits, still ≈ 15.) A real not_enough_credits mid-run is recoverable (finished clips survive; resume after top-up) but ugly — the whole point of this step is that the user decides before the spend.

If the user names a video model outside the roster, honor it only if it can frame-lock seams (Step 4). This skill only ships seamless output, so a model that can't frame-lock is declined with a one-line why, not substituted in — use a roster model instead.

Keep the scroll mechanic fixed (continuous fly-through) — that's the point of the skill. See references/prompts.md for the intake checklist and copy structure.

Step 2 — Generate the scene stills

One image per section, all sharing the same style preamble for cohesion. Default model gpt_image_2 (crisp, great at isometric illustration; returns a solid/white background which is perfect for floating diorama "islands"). Use nano_banana_2 only if the brief is character/cartoon-heavy (note: nano_banana_2 is a CLI alias — it resolves to nano_banana_pro; it won't appear under that name in higgsfield model list).

Prompt shape (full templates in references/prompts.md):

<STYLE PREAMBLE, identical every time>. On a plain solid <bg> background with a soft
contact shadow. <PALETTE hexes>. No text, no letters, no logos, centered, 3:2.
Subject: <what is in THIS diorama>.

• Run all N concurrently, detached. Command per scene: higgsfield generate create gpt_image_2 --prompt "$(cat scene_i.txt)" --aspect_ratio 3:2 --resolution 2k --quality high --wait --wait-timeout 15m --json > scene_i.json 2>scene_i.err

• Result URL is .[]0.result_url in the --wait --json output. curl it down.

• Codex stills variant (if chosen at Step 1.6 — subscription-billed, zero credits): same prompt files, same byte-identical preamble, generated by Codex's built-in image_gen:

  codex exec -C "$WORK" -s workspace-write --skip-git-repo-check \
    'Use the image generation tool ($imagegen) to generate: '"$(cat "$WORK/still_i.txt")"' Wide 3:2 landscape, high resolution. Save it as ./still_i.png. Do not do anything else.'
  

  Single-quote the $imagegen segment (the shell must not expand it); if editing with reference images, the prompt goes BEFORE any -i flag (it's variadic). ~1–3 min per image; run a few in parallel, not all N at once. Output lands at 1536×1024 (3:2) — fine for --start-image and posters. Everything downstream (cohesion review, knockout, dives) is unchanged.

• A generation may fail transiently (HTTP 503) — re-roll that one individually; don't restart the batch.

• Review the stills before continuing. They must read as one cohesive world (same angle, palette, light). If one is off-style, regenerate it, optionally passing an approved scene as --image to lock style.

See references/pipeline.md for the exact batch script.

Step 3 — (Optional) Float the scenes

If you want the dioramas to float over an atmospheric background instead of sitting in a solid box, knock out the flat background to transparency with references/knockout.py (border-connected flood fill — preserves interior colour that matches the bg, e.g. cream walls). Then encode to webp. If you'd rather keep it simple, just make the page background the same colour as the scene background and skip this.

These stills double as video posters and lazy-load fallbacks, so keep them.

Step 4 — Camera architecture (pick one — this makes or breaks the feel)

How the camera moves between scenes is the single biggest quality lever. Two shapes; pick by aesthetic.

Video model — pick ONE for the whole chain

This skill only ships seamless output, so the only usable models are ones that can frame-lock a seam: every chained clip must accept --start-image, and connectors also need --end-image. That capability — not preference — is the selection rule. Check any model with higgsfield model get <job_type> and skip anything whose media inputs are reference-only (no start/end image): it can only condition a generation, not continue a shot, so it physically can't hold a seam. Schemas below were confirmed against the CLI:

Those three are the roster — all do both architectures. (kling3_0_turbo also frame-locks via --start-image, but has no --end-image, so it's architecture-A-only and can't make connectors; it also takes a different flag set — no --mode, has --resolution — so it doesn't drop into the pipeline as-is. It's not in the default roster; only reach for it, and wire it by hand, if architecture A's sequential render time is a proven bottleneck and you've benchmarked it as actually faster.)

Rules:

• One model for all chained clips. Each renderer has its own motion/color/grain character; mixing models mid-chain keeps position continuity (frames still hand off) but the render-character shift reads as a subtle pop. The one sanctioned exception is the NSFW fallback for a single stubborn clip (Gotchas) — a slight character shift on one 5s connector beats a missing connector.
• Default to seedance_2_0; honor a user's stated preference only if the model qualifies (frame-locking). If it doesn't, say so and use a supported model — never ship a non-seamless build to satisfy a model request.
• The pipeline scripts take the model as $VMODEL with per-model flags already cased out (references/pipeline.md).

A) Continuous forward take — RECOMMENDED for grounded / realistic / walkthrough

One camera that only ever glides forward, first scene through last, as a single take. Generate the legs sequentially: leg 0 from scene-0's still (glide forward into it); then each leg's --start-image = the previous leg's ACTUAL last frame (extract with ffmpeg), prompt "continue gliding smoothly FORWARD into [scene i], never pulling back" (or an expressive mid-leg move under the motion-handoff contract — see Camera grammar below), and no --end-image — an end-image of a wide establishing shot forces the camera to pull back, which is the #1 cause of stutter. Extract each leg's last frame to feed the next. Result: every seam is frame-identical and the camera never reverses. There are no connectors (skip Step 5) — the legs ARE the journey. Wire each leg as a section clip with connectors: [] and a small crossfade (~0.08). Even without an --end-image the legs still arrive at distinct rooms (the prompt steers the content). Cost: strictly sequential (can't parallelize) and slower; interiors trip the NSFW filter, so build in re-rolls (3 attempts/leg).

B) Dive-in + aerial connector — only for diorama / miniature / god's-eye worlds

A "dive into each scene" clip + a connector that pulls up and out and flies over to the next scene (Step 5). The pull-out reverses camera direction at every seam (forward dive → backward pull-out). In a miniature/diorama world that reads as an intentional "zoom out to the map, fly to the next island"; in a grounded first-person walkthrough it reads as a jarring rewind/stutter. Use B only for the map-like aesthetic. When in doubt, use A.

Camera grammar — the move should fit the concept (A is NOT "forward only")

"Forward only" is the seam rule, not the leg rule. The physics of the chain:

• Position continuity at a seam comes from the frame handoff (next leg starts from the previous leg's actual last frame).
• Velocity continuity at a seam means the camera must never reverse across a seam — that's the rewind stutter.
• Inside a single leg the camera is free. One leg is one continuous render — there is no seam to break mid-leg, so orbits, crane-ups, lateral tracking, even a push-in that eases back out are all safe within the clip. Reversals are only fatal across seams.

So give each leg an expressive move chosen from the scene's own logic, under a motion handoff contract: every leg ends by settling into a slow, steady forward drift toward the next destination (final ~1 s), and every leg begins by continuing that same drift. Keep both clauses in the prompts verbatim (templates in references/prompts.md).

Pick the grammar from the concept:

Honest costs: expressive mid-leg moves raise re-roll odds — the model can end a fancy move in a state that isn't a clean forward drift. Mitigations: keep the final-second settle clause verbatim; eyeball each leg's last frame before chaining the next (it should look like a frame from a gentle forward glide — if not, re-roll before wasting the next leg); budget ~1 extra re-roll per expressive leg. A plain forward glide stays the zero-risk default — use it for legs where the scene itself is the show.

Two related pacing knobs live in the engine (Step 7): per-section scroll (more scroll distance = longer dwell in that scene) and linger (the camera settles mid-scene exactly while the copy peaks, then picks up speed toward the seam). Prefer expressive motion in the clip and restraint in the scrub mapping — they compound.

And remember scroll is a scrubber: visitors can scroll up, so every move also plays in reverse. That's free and expected — no extra work — but it's another reason seam velocity must be consistent in both directions (a seam that reads fine forward reads as a stutter backward too if velocity flips).

For B, one camera flight per scene: starts high/outside, descends into the interior, structure opens. Model: the chain model you picked above (default seedance_2_0), --start-image = the scene still.

• Use the solid-background still (not the knocked-out transparent one) as the start image, so the video has a full frame.
• Prompt: "Single continuous cinematic camera move, no cuts. Begin high and far looking at the whole from outside … descend and fly inside toward … the roof/walls gently open to reveal the interior. , smooth graceful slow motion. No text." (Template in references/prompts.md.)
• Params (seedance): --mode std --resolution 1080p --aspect_ratio 16:9 --duration 8. For Kling: drop --resolution (no such param), add --sound off, --duration 10. Do not pass --generate-audio (it errors on seedance; audio is wasted anyway — you'll mute).
• Run concurrently, detached, then download each .result_url. Re-roll individual failures. Keep the raw 1080p sources — you need their frames next.

Step 5 — Connectors (architecture B only)

Skip this whole step for architecture A — the forward take has no connectors; its legs already chain seamlessly. This step applies to B (diorama/miniature), and note the reversal caveat from Step 4.

The connector clips are what make the world feel connected instead of cut. A connector flies from the end of scene i out and into the start of scene i+1. Both of its endpoints must be the ACTUAL RENDERED FRAMES of the neighbouring clips — never the original diorama still.

Why: every Higgsfield generation renders slightly differently. If a connector ends on a fresh render of "the kitchen diorama," but the next dive clip starts on its own different render of that same diorama, the two won't match and you get a pop at the seam. The fix is to hand off the exact pixels:

For each connector between dive_i and dive_{i+1}:
  start-image = the LAST frame extracted from dive_i's rendered video
  end-image   = the FIRST frame extracted from dive_{i+1}'s rendered video

Now every seam is frame-identical on both sides: dive_i.end == connector.start and connector.end == dive_{i+1}.start.

Extract the boundary frames from the rendered dives (not the stills):

ffmpeg -sseof -0.15 -i dive_i.mp4   -frames:v 1 -q:v 2 dive_i_last.png    # interior of i
ffmpeg -ss 0      -i dive_{i+1}.mp4 -frames:v 1 -q:v 2 dive_next_first.png # establishing of i+1

Generate the connector (--duration 5 is plenty). Connectors need --end-image, so the model must accept it — any roster model does (seedance_2_0, seedance_2_0_mini, kling3_0):

higgsfield generate create "$VMODEL" \
  --prompt "$(cat connector_i.txt)" \
  --start-image dive_i_last.png --end-image dive_next_first.png \
  $VOPTS --aspect_ratio 16:9 --duration 5 --wait --json
# seedance: VOPTS="--mode std --resolution 1080p"; kling3_0: VOPTS="--mode std --sound off"

Connector prompt: "Single continuous camera move, no cuts. Pull up and back out of , rise into the sky, glide across the connected miniature world, and arrive above <scene i+1>, beginning to descend toward it. Seamless flowing aerial transition.