design-review-panel

Design

Run a multi-agent design review on a UI surface — capture reproducible baseline screenshots, then rate them with a panel of design experts (one agent per craft dimension) and, optionally, a panel of user personas with different cognitive styles. Iterate implement → re-screenshot → re-rate until every panel hits a numeric target (e.g. avg ≥8/10). Use when polishing or redesigning a screen/modal/dialog and the user wants grounded, scored design feedback ("summon the design panel", "rate this with the experts", "run the persona panel").

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Source SKILL.md: https://github.com/matthiasn/lotti/blob/HEAD/.claude/skills/design-review-panel/SKILL.md

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Design Review Panel

A repeatable, grounded process for design polish and redesign work. It pairs two parallel agent panels — design experts (craft dimensions) and, optionally, user personas (cognitive styles) — and drives them against real screenshots the agents actually Read, iterating to a numeric target.

This is how the user wants UI/design tasks run. The numeric target is the success condition: keep iterating until both panel averages clear the bar (the workflow's cleared flag).

The loop

stateDiagram-v2
    [*] --> Baseline
    Baseline --> RateBaseline: capture reproducible PNGs (app-screenshots)
    RateBaseline --> Implement: both panels score the current state
    Implement --> Rescreenshot: apply highest-leverage fixes (design-system tokens only)
    Rescreenshot --> Rerate: regenerate the SAME shots
    Rerate --> Implement: either panel average < target
    Rerate --> Harden: both panel averages ≥ target
    Harden --> [*]: tests, l10n, README, CHANGELOG+flatpak, analyzer clean, PR

Baseline screenshot first. Use the app-screenshots skill / the test/test_utils/screenshot_harness.dart captureInApp harness to render the surface at phone and desktop, dark (add light + large-text shots when accessibility is in scope). Reproducible PNGs are mandatory — the panels are only as honest as the pixels they read.
Rate the baseline with BOTH panels up front. Get grounded starting scores before changing anything. Never carry over scores from a previous session — re-rate on a freshly regenerated PNG every time (grounded re-rating reliably deflates inflated prior numbers).
Iterate with the expert panel until experts clear the bar. Bring the persona panel into the loop once experts reach ≥8 so the two converge together. Each iteration: implement → regenerate the exact same screenshots → re-rate.
Adjudicate genuine tradeoffs with the user (AskUserQuestion) instead of silently picking a side — before declaring a conflict irreducible, hunt for a both-sides fix (one change that serves two opposed reviewers).
Harden to PR-ready once converged: tests, l10n, feature README, CHANGELOG + flatpak metainfo, analyzer zero-warning, formatter, PR on latest main.

The two panels

Design-expert panel (always)

One agent per craft dimension. Default lenses (adapt to the surface):

Visual hierarchy / IA — what reads primary/secondary/tertiary; scent.
Design-system consistency — tokens, spacing rhythm, component reuse; does the surface feel like ONE system or bolted-together parts.
Color / contrast / semantics — palette restraint, status-by-more-than-color.
Typography — ramp, weight contrast, measure, rhythm.
Spacing / density / rhythm — padding, alignment, optical balance.
Interaction / task-flow — taps-to-goal, affordances, dead-ends, the "1-tap to the common case" promise.

User-persona panel (optional — pass `includePersonas: true`)

Different cognitive styles stress the surface as real users:

ADHD / clutter-sensitive — needs "what now" instant; abandons noise.
Power user — counts seconds, allergic to wasted space/steps.
Low-vision / low-confidence — large text, strong contrast, fears irreversible taps.
Minimalist aesthete — wants calm, uniform, restrained.
Non-technical novice / second-language — reads copy literally; jargon and ambiguous labels break them.

Personas return a verdict (would-use / would-struggle / would-abandon) plus blockers / frictions / delights. Experts return a 1–10 score per surface plus severity-tagged issues with evidence + a concrete fix.

Grounding rules (give these to every agent verbatim)

Read every screenshot path. Base every visual claim on actual pixels. Panels hallucinate failures when they don't — forbid it.
No invented measurements. Don't fabricate px gaps or contrast ratios; if you can't measure it, describe it qualitatively and tie it to something visible.
Every issue carries evidence — a named screenshot ("picker_desktop: …") or a file:line. Code claims (e.g. "hardcodes spacing") require Reading the file and citing the line.
Grumpy, calibrated scoring: 10 = ship-grade, nothing to fix; 8 = good, only polish left; 6 = usable but rough; 4 = several real problems; ≤3 = broken. Do not be generous.
List test-rig artifacts to IGNORE (stand-in nav bars, tofu glyphs, folded-hour bands) so the panel doesn't score the harness.

Running it

Drive both panels as a Workflow so the agents run in parallel and return structured scores. A parameterized reference script lives next to this file: panel_workflow.js — pass args describing the surface, screenshot paths, source files, expert lenses, personas, and includePersonas / target. Adapt the lenses and persona prompts to the surface; keep the schema and the iterate-to-target loop.

Workflow({ scriptPath: ".claude/skills/design-review-panel/panel_workflow.js", args: { ...see file header... } })

Read the returned synthesis, apply the must-fixes, regenerate the same screenshots, and re-run until the returned cleared is true (both panel averages meet the target). Then delete the scratch capture test and test/screenshots/ (per the app-screenshots skill) unless the user asks to keep them.