Gacha Ui

Gacha-UI: divergent UI design by parallel mockups + human-in-the-loop selection

One line: diverge on FORM, lock the TRUTH. Generate several genuinely-different throwaway HTML mockups built on real data, show them side-by-side, let the human point at the winner, distill that pick into hard constraints, and narrow each round until they say "that's the one" — then build it for real.

Why this works (not just taste): parallel prototyping measurably beats iterating one design, and the gain comes specifically from showing multiples side-by-side — not from merely making them. Three mechanisms you must preserve: side-by-side comparison, delayed commitment, and self/work separation. So "serve N and let the human pick" is the value, not decoration. It's Anthropic's parallelization (voting) → evaluator-optimizer loop, human as evaluator.

→ Evidence + citations: reference/why-parallel-prototyping.md (read only if you need the receipts).

The two non-negotiables

1. Diverge on form, lock the truth. Every round varies exactly ONE unsettled dimension. Everything already decided — plus real data, brand tokens, and user-approved elements — is held byte-for-byte identical across all N mockups. Divergence you can attribute; fidelity you never break.
2. The human points; you don't guess for them. There is no universal "good-looking" (expert agreement barely beats chance) and auto-judges miss aesthetic/brand fit. So you ALWAYS render and show; the user always makes the real pick. (You may optionally auto-cull obviously-broken/near-duplicate cards before showing — never auto-judge which design is better.)

> HARD-GATE — never skip the show. You MUST serve all N mockups and let the human pick. Do NOT collapse to one, pre-pick a "winner", or describe options in prose. Generating multiples only widens your own exploration; only showing them raises quality. Skipping the show nullifies the whole method.

Two modes

• Mode A — Refine existing UI (the common case): there's a component/page the user dislikes. Read it + screenshot it as the baseline. Round 1 diverges on the single most-unsettled dimension.
• Mode B — Design from scratch: no UI yet, look is open. Round 1 diverges on the broad archetype (e.g. console/HUD, bento, editorial, radial-dashboard, card-wall…). Later rounds narrow exactly like Mode A.

Decide the mode in Gate 0. Everything after is identical.

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The loop

Run these in order. It's re-entrant: rounds repeat B→E until converged.

Gate 0 — Should we even gacha? (simplicity-first)

• Judge scope. A one-line tweak or a fully-specified change → just do it, no gacha. Heavy parallel flow is for unsettled visual direction. (Models over-trigger heavy flows; this gate is the throttle.)
• Pin the single target block/component with clear boundaries.
• Pick the mode (A or B).
• Announce one line to the user so they see the plan (and that you considered not gacha-ing): "Using gacha-ui: I'll make N=__ throwaway mockups (mode __) of __, serve them so you pick side-by-side, then narrow each round." — or, for the no-gacha branch, "This is a one-liner / fully-specified, not a gacha case — doing it directly." Once you fan out you can't course-correct mid-flight, so the prep below must be solid first.

A — Project profile + real-data recon (do this fully or don't start)

The skill is stack-agnostic; establish the profile once, then reuse it every round. Detect from the repo; ask only what you can't infer.

• Stack & preview: framework (React/Vue/Svelte/static…), how it's served, how to take a screenshot. Default toolkit when nothing else fits: standalone HTML mockups + a static server + Playwright screenshots (see scripts/serve-and-shoot.js).
• Design tokens: pull the real brand colors / type scale / radius / spacing into ONE shared CSS-variable block (gacha/tokens.css). Copy it into each round dir so every mockup @imports ./tokens.css — a sibling path (../tokens.css) renders under file:// but 404s over the local server, so screenshots and the served gallery would silently disagree. Mockups never redefine values. (shadcn-style physical lock — divergence happens in layout, not in remembered hex codes.)
• Real data: the actual numbers, copy, labels, image aspect ratios. Structured list; text as its own items. Never lorem/placeholder — a winner chosen on fake content breaks on real content and the feedback is junk. If you don't have the real data yet, go get it (read the data files / API shape) before fanning out.
• Target viewport: ask which width is real — desktop, mobile, or both? Shoot at that width (the script's [width] arg; run it twice for both). A winner picked at a guessed width can break at the real one — same fake-context trap as fake data.
• Fidelity list: real data + tokens + any already-approved element. This is reasserted verbatim in every divergence prompt.

→ Full recon + fidelity rules: reference/lock-ledger.md (Recon section).

B — Diverge (parallel, voting variant)

• Diverge on the single most-unsettled dimension only (from the prior round's "What If"; in round 1, the archetype or the biggest open question).
• Generate N throwaway mockups that share the same data + tokens and differ structurally on that axis. Each mockup gets a distinct constraint card + prototype name (e.g. "Refined-restrained", "Vivid-friendly", "High-density expert") — and, on style-flavored axes, one named design language from reference/style-vocabulary.md as its exemplar (unique per card; at most one card in the known-AI-mode center listed there).
• Force real diversity — independent re-runs collapse to the same typical answer. Use distribution-level instructions ("be mutually distinct; include 1–2 non-mainstream/contrarian takes"), one ordinary-but-different persona per card, and a quick "list the option space first, then split" step. Then drop near-duplicates — checked blind: a fresh pass that hasn't seen the constraint cards describes each render's axis-stance in one sentence; collisions = true dupes.
• Isolate generation (structural, not phrasing). Map the option-space centrally → assign regions for max spread (claim both poles first, then tails, then interior) → generate each mockup in its own parallel subagent that sees only its constraint card + the fidelity list, never the other cards' output. One agent writing all N cross-contaminates (one design recolored); N agents on an identical prompt collapse to the mode. One-prompt-producing-N is the fallback, not the default.
• → Full diversity recipe + the divergence prompt: reference/diversity-recipe.md.
• Save to gacha/round-{r}/mockup-{i}.html.

C — Show & select (human-in-the-loop — never skip)

• Serve the live gallery so the user browses side-by-side in a real browser — this is the load-bearing step. Run scripts/serve-and-shoot.js : it writes a grid of live HTML iframes (not images) and starts a local server, printing the URLs. Screenshots are an optional by-product of the same command — they exist so you (the model) can look at each mockup to auto-cull broken/near-duplicate cards and sanity-check against feedback; they're skipped gracefully when Playwright is absent, and the side-by-side viewing never depends on them. Give the user the URLs (and the PNGs too if you shot them).
• Add a one-line "look at X, ignore Y this round" guide (e.g. "judge layout; colors/copy are identical across all, don't compare them").
• Ask for a pick / ranking / "winner + worst", not a 1–10 score. Pairwise/relative judgments are faster and far less biased than scores. Mixing is welcome ("A's density + B's palette").
• Randomize the presentation order; don't always put your favorite first (kills position bias). Ask them to say why before which.

D — Extract constraints → lock ledger

• Convert the pick + words into "verdict + actionable constraint", not raw quotes. Users are great at finding problems, poor at prescribing fixes — treat feedback as problem-location signal and infer the constraint. Use I Like / I Wish / What If:
• I Like → write into the lock ledger (now held fixed).
• I Wish → next round's narrowing constraint / exclusion.
• What If → next round's divergence axis.
• Ledger is per-dimension (palette / layout / type / spacing / motion / data-viz), each row recording value · which mockup it came from · the user phrase that approved it. Support element-level approval and cross-version merges (A's palette + B's layout).
• Echo back to the user: "locked this round: …; next round we diverge on: …."
• → Ledger template: reference/lock-ledger.md.

E — Converge check

Three questions each round: (1) did the user approve ("that's the one")? (2) stalled — 2 rounds with no new lock? → switch the divergence axis. (3) hit the round cap (5–6)? → force-finish. Any one true → stop gacha, go to F.

F — Land it (real stack, data-driven)

• Implement the locked direction in the real framework with the real data source, reusing the shared tokens. This is real work, not a copy-paste — wire data, state, interactions.
• Acceptance is end-state: does the final real-data UI match every line of the lock ledger? Don't relitigate individual mockups.

G — Refine (get the design right)

• Render → screenshot → visual-diff against the approved mockup → fix only the differing regions. Don't repaint the whole thing (changing A breaks B).
• For pointed fixes, feed the full current code + the user's annotations on the screenshot with "keep everything else; change only the marked spots."
• Structural change → spin a fresh variant round; pure visual nudge → local edit. Don't re-gacha a whole page to change one color.

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Knobs (defaults + why)

| Knob | Default | Why | |---|---|---| | N (mockups/round) | 5 early (wide), 3–4 late (narrow); adaptive — drop a notch when feedback converges | max-of-n: cheap + uncertain → diverge more; "clear majority → early stop". Models over-diverge — hard-code this rule. | | Divergence axis | exactly ONE most-unsettled dimension/round; rest locked verbatim | controlled variable = attributable choice + max info gain | | Fidelity list | real data + tokens (physically locked as shared CSS vars) + approved elements; reasserted every prompt; a card that fakes/edits a locked dim is rejected | real material → real feedback; lock by file, not by memory | | Diversity | isolated per-card generation (parallel subagents, own constraint card only) + max-spread regions (poles first) + distribution-level instruction + one ordinary-distinct persona per card + de-dup on the axis | collapse is typicality bias; re-running doesn't fix it — structure + explicit per-card constraints do | | Converge signal | approve OR stall(2) OR cap(5–6) | AutoGen dual-stop + stall counter + self-consistency early-stop | | Lock ledger | per-dimension rows: value · source mockup · approving phrase; element-level + cross-merge; echoed each round | evaluator-optimizer memory; also the best-of-N regularizer against over-fitting one round's feedback | | Persistence | each round in gacha/round-N/ (html + png + pick + ledger snapshot) | supports "go back to round k and re-fork" if the user reverses | | Models | plain divergence on a fast model; review/refine on the strongest | tier by task difficulty |

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Prompt skeletons

Inline skeletons below; full versions in the reference files.

① Diverge one round (one isolated generator per mockup — preferred; a single prompt producing all N only when subagents aren't available):

Make {N} throwaway HTML mockups of "{target}" for the user to pick side-by-side.

LOCK THE TRUTH (identical, verbatim, in every mockup — never modify):
- Real data (use as-is, NO lorem/placeholder): {structured real data}
- Brand tokens (@import the same CSS vars, never change values): {tokens path}
- Approved elements (keep exactly): {locked elements}
- Already-locked dims (copy verbatim, do NOT diverge this round): {ledger snapshot}

DIVERGE ON ONE AXIS ONLY:
- Vary only: {the single most-unsettled dimension}
- Everything else: per the lock list above.

FORCE DIVERSITY (distribution-level, not "give me N"):
- The {N} must be STRUCTURALLY different on the axis — not one design recolored.
- Each card = a distinct prototype name + one ordinary-but-different persona/lens
  (+ on style axes: one UNIQUE named design language from reference/style-vocabulary.md).
- First map the option-space, annotating each answer with the probability a typical designer
  would pick it; assign regions favoring the POLES + low-probability tails — at most one card
  in the known-AI-mode center. Don't let card 1 anchor the rest.
- Must NOT resemble these rejected/duplicate takes: {prior culls}

OUTPUT: one self-contained HTML file each, gacha/round-{r}/mockup-{i}.html, sharing the
same data + tokens; plus a one-line design intent. Self-check: any card that fakes data /
edits a locked dim / duplicates another ON THE AXIS (differs only in off-axis dims) → redo that card.

② Select & extract — see reference/lock-ledger.md (show protocol, I-Like/I-Wish/What-If, converge questions).

③ Land + refine:

Direction locked. Build the winner in {real stack} with real data.
MUST: match every lock-ledger line {ledger}; use real data source {source}; reuse shared tokens.
ACCEPT (end-state): final real-data UI matches the ledger.
REFINE: render → screenshot → visual-diff vs approved mockup → fix only differing regions
(no full repaint). Structural change → new variant round; visual nudge → local edit only.

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File layout this skill writes

gacha/
  tokens.css                # shared design tokens (the physical lock) — master copy
  data.json | data.md       # real data recon (or inline)
  ledger.md                 # the running lock ledger (source of truth between rounds)
  round-1/  tokens.css  mockup-1..N.html + .png + pick.md   # tokens.css COPIED in so @import './tokens.css' works over http too
  round-2/  ...

(Put under a scratch/ignored dir, or /tmp, if the repo shouldn't carry mockups.)

Boundaries vs other skills

(Names below refer to common companion skills; if your agent doesn't have them, the division of labor still holds — do that job as a plain task instead.)

• frontend-design makes ONE high-quality draft from scratch. gacha-ui makes MANY, with the human in the loop, and converges. The load-bearing pivot: did the user ask to COMPARE / explore options? Yes → gacha-ui ("this looks off", "show me directions"). No — they want one good page → frontend-design. A single mockup inside gacha may borrow frontend-design's craft.
• brainstorming aligns what to build in words, no visual output. gacha-ui makes the look concrete via mockups. Hand off: brainstorming settles "which block, what goal" → gacha-ui visualizes and converges it.

Tools

• scripts/serve-and-shoot.js [port=auto] [width=1280] — screenshots every .html in a dir and serves them in a grid (side-by-side) for browser viewing; auto-picks a free port. Needs Playwright + Chromium (install: npm i -D playwright && npx playwright install chromium). If Playwright is missing it skips screenshots but still serves the gallery (open it and screenshot by hand). Any screenshotter works — this is just the batteries-included default; if the repo already has Playwright/Puppeteer, prefer its own path.
• reference/walkthrough.md — ONE concrete round end-to-end (recon → diverge → pick → extract → ledger → converge); read it to see the rhythm, especially the verdict→constraint step.
• reference/diversity-recipe.md — how to pick the axis, set N, and force real divergence.
• reference/style-vocabulary.md — 40+ named design languages (grouped by family) + the known-AI-mode list; per-c

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Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: fade2373
• Source: fade2373/gacha-ui
• License: MIT
• Homepage: https://fade2373.github.io/gacha-ui/

Install and usage instructions live in the source repository linked above.