/frontend-design-deslop

name: frontend-design-deslop
description: Designs distinctive, non-generic UI — typography, OKLCH color, design tokens (DESIGN.md), layout, components, motion, dark mode, accessibility — for landing pages, SaaS apps, dashboards, ecommerce, decks, docs, portfolios, avoiding the AI-slop / Claude-esque default look. Use whenever building or styling any web frontend, app, dashboard, landing page, deck, or artifact, or when the user says "make it not look like AI", "de-slopify", "deslop", "less generic", "give it character", "design a UI for X", "design an app", "update DESIGN.md", or complains the output looks like every other AI site. Trigger even on a bare "build a UI for X" with no aesthetic named. Not for writing marketing copy.
user-invocable: true
license: MIT
compatibility: Designed for Claude Code, Codex or similar harness.
metadata:
  author: samber
  version: "1.2.2"
  openclaw:
    emoji: "🎨"
    homepage: https://github.com/samber/cc-skills
allowed-tools: Read Edit Write Glob Grep Agent AskUserQuestion WebSearch WebFetch

**Questions:** Ask the user through the environment's question tool — never as plain-text prose. One question at a time, 2–4 tappable options, wait for the answer. If the environment has no question tool, ask in prose with the same options, one at a time.

frontend design deslop

AI-generated UI looks generic for two reasons:

1. **No constraints.** The model samples the statistical median of 2019-2024 web code — Tailwind UI's `bg-indigo-500`, Inter, rounded cards, soft shadows. You cannot out-prompt a vacuum. 2. **Designing before knowing what you're designing.** A corporate landing page, a creative portfolio, a developer-tool landing page, an analytics dashboard, and an ecommerce product page share almost no design DNA — a beautiful aesthetic that fights the artifact's job is its own slop.

The fix is a discipline borrowed from brand design: strategy drives design, never aesthetics first.

1. Commit to words first — what this is, who it serves, the adjectives it must feel like. 2. Translate those words into a typography and color system. 3. Build from tokens. 4. Apply the craft layer (layout, components, motion, iconography, imagery, dark mode, accessibility). 5. Audit.

Target the convergence mechanism, not a frozen blocklist; the slop fingerprint shifts over time (purple gradients in 2022, cream backgrounds and italic-serif heroes in 2026).

This skill does two jobs at once: it de-slops the default AI look, and it designs applications well. A distinctive theme on top of careless components, weak layout, or thoughtless motion still reads as amateur. The mechanisms behind every choice live in `references/design-theory.md` (hierarchy, Gestalt, CRAP, signal-vs-noise, affordances, the interaction laws); read it once so the rest is reasoning rather than rule-following.

Asking questions (CRITICAL)

Every single user question must go through the question tool (see the **Questions:** directive above) — never as plain-text prose. The bank is generous; the asking is selective. Do not interrogate.

• Batch related questions; offer 2 to 4 concrete options each.
• Ask only the high-signal subset that changes the design system.
• Infer from context first and confirm inferences rather than re-asking.

Phase 0: Discover and commit to words (do this FIRST, before any code)

Check for an existing `DESIGN.md` at the project root (and common locations like `docs/`) before writing any code.

• **Exists** — read it, honor its tokens, skip the questions it already answers, and extend it rather than starting over.
• **Missing** — resolve three things below before any pixel. Read `references/discovery.md` for the full protocol, question bank, and the personality-to-token translation table, and `references/artifact-types.md` for per-type priorities.

1. **WHAT is the artifact?** Classify it: marketing/landing page, pricing page, SaaS application, dashboard/data tool, ecommerce, marketplace, mobile app, AI/conversational interface, email/newsletter, blog/editorial publication, onboarding/auth flow, settings/admin/CMS, presentation/deck, docs/API reference, portfolio/brand site, or one of the long-tail types in `references/artifact-types.md`. Each optimizes for a different thing and has its own layout grammar and density. A composite artifact (a marketing site with an embedded app, an AI chat inside a SaaS app) is designed region by region. 2. **WHO and WHY?** Audience, positioning (corporate vs creative vs technical vs luxury vs playful), and the single primary action or outcome. 3. **Commit to words.** Lock 3 to 5 brand adjectives and a 3-word aesthetic essence before any visual exploration. This is the highest-leverage input; it drives type, color, density, radius, and motion. Strategy drives design, never the reverse.

Run discovery adaptively: infer, state inferences, ask the high-signal subset through the question tool, and ground the direction in 1 to 3 references (web-search strong current examples of the exact artifact type and positioning if none are given, then transpose rather than originate). Do not proceed until artifact type, positioning, and the adjectives are locked. On a generic brief (another dashboard, another SaaS landing page) where the category itself has little to say, widen the reference search past the product's own category rather than asking for more creativity within it — see `references/divergence.md`.

Phase 1: Translate strategy into a design system (the gate)

State these commitments in prose, briefly. Each must follow from Phase 0, not from reflex.

1. **Aesthetic commitment.** Pick ONE opinionated direction that fits the artifact and the adjectives; generic is the failure mode. See `references/aesthetics-library.md`. If the user gave a brand or reference, transpose it. Derive 2-3 real candidates before picking one, commit to the winner fully before softening it for legibility, and check the bare structure (not just the styled surfa