Lets Scroll

lets-scroll

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. A manual asset path (Step 1.7) swaps the render calls for a prompt + file handoff: the user generates every still and clip in tools of their choice and drops the files into the work folder; everything downstream (frame extraction, encode, engine, QA) is identical. 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 1.8 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 1.8, command in Step 2. Absence just removes the option.
6. Manual asset path (Step 1.7) — if the user will render the assets themselves, skip items 1, 2 and 5 entirely (no Monid/Higgsfield/Codex needed); only ffmpeg/ffprobe (item 3) and, for the optional knockout, PIL (item 4) matter.
7. 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 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. Camera style — ALWAYS ask; it's the film's personality, not a technical detail. Ask by feel (AskUserQuestion in Claude Code; a plain question elsewhere) and record the answer as CAMERA. The options map to the Step 4 architectures — Step 4 then implements the choice, it never re-decides it:

   • "Fly through the world" — the camera dives into each scene, pulls up and out, and hops across the miniature world to the next; angles change constantly, big expressive aerial moves (this is the flagship-demo look). → Architecture B. Recommend as the default for diorama/miniature art directions.
   • "One continuous walkthrough" — a single forward flight that glides through each scene straight into the next, never pulling back; expressive but always-forward moves per scene (camera grammar table). → Architecture A. Recommend as the default for grounded/photoreal art directions.
   • "Locked isometric glide" — the camera keeps one fixed angle for the whole film, Emons-style; the world slides past/toward it, no rotation, no reveals. → Architecture A + the locked-iso clause in every leg prompt (prompts.md). State the trade-off in one line each (B reverses direction at seams — charming in miniature, jarring in realism; locked-iso is the calmest and cheapest to re-roll; walkthrough sits between).

5. The journey (sections) — start with the LEVEL OF ANIMATION; always ask it as a structured choice (AskUserQuestion in Claude Code; a plain question elsewhere). Three sizes, 6 is the maximum — record the count as N:

   • 2 scenes — a teaser: opener + hero/CTA. 2 stills, 3 clips (2 dives + 1 connector). Fastest and cheapest; the right first run for testing the pipeline end-to-end.
   • 4 scenes — a short journey. 4 stills, 7 clips.
   • 6 scenes — the full film. 6 stills, 11 clips. The most the scroll pacing stays comfortable with. Then propose that many scenes derived from the subject's own value chain and let the user edit. Boba example at N=6: 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.

6. 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.

7. Asset source — automatic or manual. ALWAYS ask; it decides who renders. Two options (AskUserQuestion in Claude Code; a plain either/or elsewhere): "How do you want to produce the stills and clips — should I generate them, or do you want the prompts to render in tools of your choice?" Record as ASSET_SOURCE.

   • Automatic (default) — the skill renders everything itself: stills via Higgsfield gpt_image_2 (or Codex image_gen), the video chain via Monid with Higgsfield as fallback biller. Item 8 prices this path.
   • Manual — the skill writes every prompt to a file (still_<name>.txt at Step 2, dive_<name>.txt at Step 4, conn_<i>.txt at Step 5) plus the exact conditioning frames each clip must start/end on; the user copy-pastes the prompts into tools of their choice (any image tool for the stills; a start/end-frame-capable video tool for the clips) and drops the finished files into $WORK. The skill validates what comes back (count, dimensions, aspect, duration, seam frames) and continues the same downstream pipeline — only the render calls change. State the one hard requirement up front: the seamless rule still applies, so their video tool must accept a first/start frame (both architectures) and, for architecture B connectors, a last/end frame — if it can't, steer CAMERA to architecture A or have them pick a tool that can (Steps 4–5).

8. Budget — engines shown by cost, decided before anything renders. On the manual path (item 7) there is nothing to bill here — the spend happens in the user's own tools — so skip the tier/backend picks below; just state the workload they're signing up to produce (N stills + (2N−1) clips [×2 if mobile], plus re-roll headroom) and get a go. On the automatic path: 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.

   • Backend — Monid is the DEFAULT biller for the chain (Step 0.1; wiring in pipeline.md → Monid backend). Same Seedance 2.0, per-clip USD instead of credits. Token-priced width × height × 24 × seconds / 1024 at $7/1M (480p/720p) or $7.7/1M (1080p) — measured: 1080p 8s dive ≈ $2.99, 5s connector ≈ $1.87; 720p ≈ $1.21 / $0.76; 480p ≈ $0.28 / $0.35. An N=6 desktop chain ≈ $27 at 1080p / ~$11 at 720p vs Higgsfield Plus-monthly ≈ $32 / $16 — ~15% cheaper per clip, parity with Plus-annual; structurally better for one-off builds (pay-per-use, no monthly expiry). On Monid the Draft/previz tier is simply the same endpoint at 480p — no model swap, so previz→final stays one-model by construction. State monid balance against the estimate; fall back to Higgsfield credits (per-model tiers above) when the user prefers their subscription, the balance is short, or the model must be kling3_0 (Higgsfield-only). It's the same underlying model (seedance_2_0 ≙ Monid's seedance-2.0), so finishing a stranded chain on the other biller is a reasonable rescue — but the serving stacks differ and cross-provider seam character is untested: eyeball the first rescued seam before rendering the rest, same as any model swap.

   • Stills source (only offer if the Codex CLI is present, Step 0.5): 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 — already carries the solid <bg> + palette>.
Render a wide 3:2 landscape image (≥1536 px). The background stays plain solid <bg>
across the whole frame — empty backdrop: no sky, no clouds, no horizon, no gradient.
Centered composition, nothing essential at the far edges. No text, no letters, no logos.
Subject: <what is in THIS diorama>.

Self-contained on purpose: aspect, background lock and palette live in the prompt text, because on the manual path each still may be rendered in a different tool or session (full template + rationale in references/prompts.md).

• Manual stills path (ASSET_SOURCE = manual, Step 1.7) — skip the generate commands below. Write the same prompt files ($WORK/still_<name>.txt, identical preamble, from the prompts.md templates) and hand them over with the spec: one image per file, 3:2 landscape, ≥1536 px wide, solid <bg> background, no text — any image tool the user likes. They save the results as $WORK/still_<name>.png and tell you when the set is in. Validate before continuing: all N present, each opens, aspect ≈ 3:2 (a few % tolerance), width ≥ ~1200 px (ffprobe or PIL). Then run the same cohesion review as below; off-style stills go back to the user for a re-roll (suggest re-running the same prompt with an approved sibling as style reference). The one-source rule applies here too — don't mix manual stills with CLI-rendered ones in one build.

• 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 1.8 — 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.' \
    < /dev/null

  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 — and keep the < /dev/null: parallel codex exec calls sharing a script's stdin hang waiting for input (Gotchas). 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 (implements the Step 1.4 choice)

How the camera moves between scenes is the single biggest quality lever. The user already chose the style at the interview (CAMERA, Step 1.4): fly-through → B, walkthrough → A, locked-iso → A + the locked-iso leg clause (prompts.md). If the interview somehow skipped it, ask now — never silently pick for them. The two shapes, and the grammar that colors them:

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.)

One more architecture-A-only candidate, worth knowing because it is by far the cheapest probe: minimax_hailuo (Hailuo-2.3, ~6 credits per 768p/6s clip vs 22–72 for the roster). Verified 2026-07: --start-image + prompt frame-locks (output frame 0 ≡ input, PSNR 33 dB) and a forward-glide prompt was obeyed, gently. Constraints: the 2.3 variant rejects end_image (no connectors → arch A only), output aspect follows the input image (hand it a 16:9 canvas, not a bare 3:2 still), motion runs subtler than seedance, and don't pass --resolution (the CLI mis-types the enum; the 768 default works — 1080 supports 6s only). One clip ≠ a chain: qualify a leg-to-leg handoff before betting a full build on it.

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, rendered through Monid by default (per-clip USD — next section) with Higgsfield credits as the fallback biller (Step 0.1/1.8); 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. kling3_0 and seedance_2_0_mini exist only on the Higgsfield side.
• The pipeline scripts take the model as $VMODEL with per-model flags already cased out (references/pipeline.md).

Monid backend — the DEFAULT chain biller (qualified 2026-07-25)

Monid's bytedance /v1/video/seedance-2.0 passed both paid probes on 2026-07-25 and is the default way this skill renders the chain, for both architectures — it is the roster's seedance_2_0 served pay-per-USD (wiring in pipeline.md → "Monid backend"; Higgsfield renders the chain only as the fallback biller or for Higgsfield-only models):

• Leg probe (prompt + first_frame image): output frame 0 ≡ input still (PSNR 31.6 dB), forward-glide prompt obeyed, billed the advertised cell ($0.279 / 480p 4s).
• Connector probe (prompt + first_frame + last_frame): start locked (31.6 dB); the end lands close but not pixel-perfect (27.5 dB, same composition, prop-level drift) — the exact end-image behavior Seedance shows on Higgsfield, covered by the engine's seam crossfade and by using the next dive's ACTUAL first frame as the end-image (Step 5 law, unchanged).

The I/O contract differs from the Higgsfield CLI — three rules:

1. Images go by URL, never inline. content items are {"type":"image_url","image_url":{"url":…},"role":"first_frame"|"last_frame"}; base64 data URLs are rejected ("Must be a public https:// URL or an asset://<id> reference"). Local frames travel through Monid's free workspace file system: sfs /put → curl -T the bytes → sfs /cat returns a signed public URL to paste into the body ($0, explicitly built for this).
2. Pass ratio explicitly (16:9, or 9:16 for the mobile chain) — the adaptive default follows the input image's aspect instead.
3. Bill-check every clip: cost is token-priced (w × h × 24 × sec / 1024 at $7–7.7/1M); read cost.value off each run.

History that shaped these rules (still true as of 2026-07-25): the seedance endpoints were text-to-video-only until late July 2026 — re-inspect before each build; the catalog moves in both directions. minimax /v1/video_generation (Hailuo-2.3) remains disqualified: sending prompt + first_frame_image together silently drops the image (unrelated t2v output, wrong price cell); image-only frame-locks (33 dB) but has no camera control.

Qualification protocol for any new/changed Monid endpoint (each probe is one cheap 480p clip): (1) prompt + first-frame from a real still — frame 0 must match the input to codec noise (PSNR ≳ 30 dB) and cost.value must match the advertised cell; (2) for connector duty, add a last_frame from a different still — the end must land on that composition (Seedance-style near-miss is fine, the crossfade covers it). Pass → pay-per-clip tier (arch A if start-only; full roster if start+end).

Manual rendering path (ASSET_SOURCE = manual — Step 1.7)

Skip the model roster and the Monid backend above; the architecture choice (A vs B) still stands — it decides which clips exist and which frames chain them. The frame-lock rule is unchanged, so confirm the user's video tool accepts a first/start frame (both architectures) and, for B's connectors, a last/end frame. If it can't take an end frame, either re-confirm CAMERA as architecture A with the user (no connectors) or have them use a tool that can (e.g. Kling's start/end-frame mode) — never ship unseamed connectors. Per clip:

1. Write the same prompt file as the automatic path ($WORK/dive_<name>.txt / conn_<i>.txt) and hand it over with the conditioning image(s) it assumes: arch A leg i → the previous leg's actual last frame; B dive → the scene's solid-bg still; B connector → the extracted last_/first_ pair (Step 5).
2. Spec for the user: 16:9 landscape (9:16 on the mobile chain), ~8 s dives/legs, ~5 s connectors, highest quality their tool offers, no audio. Results saved as $WORK/dive_<name>.mp4 / $WORK/conn_<i>.mp4.
3. When a batch lands, validate each file before chaining: plays, right aspect, duration ≈ spec — and frame 0 must match the handed-over start frame (extract it and compare). A clip whose tool ignored the start image can't hold its seam; send it back for re-generation, don't crossfade over it.

Arch A stays logically sequential even though rendering is manual — each leg's start frame is extracted from the previous leg's returned file. Everything downstream (boundary-frame extraction, encode, engine, QA) is identical to the automatic path.

Every manual handoff — stills, dives, connectors — is ALWAYS presented as a spec table, never a prose list: prompt file, conditioning frame(s), the exact output filename, and a status column you keep current (pending / rendered / accepted) as files arrive and pass validation. The user works through it offline, in any order, and the table is the contract for what you're still waiting on. The connector table in Step 5 is the canonical example.

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 — which is exactly what the "fly through the world" interview answer opts into; the reversal reads as intentional there. If the user picked B against a grounded/photoreal direction, say why it will read as a stutter and confirm before rendering.