Reverse-brand prompt for Claude (Opus, agentic coding mode)

A developer published a reusable prompt that instructs Claude Code to build a CLI tool called reverse-brand, which captures a public brand website with Playwright and extracts its typography, colour, spacing, components and design tokens into an unofficial, art-directed brand guide. The prompt specifies measured token extraction across a 1440×900 desktop viewport and a 390×844 mobile pass, deduplication of repeated header and footer chrome, WCAG contrast checks, and safeguards against copying logos or bypassing CAPTCHAs.

Paste everything below the line into Claude Code or any Claude setup with a shell, a browser and file access . Replace the three settings at the top. URL: https://example.com https://example.com IMAGES: none | api | manual IMAGE PROVIDER only if IMAGES = api : e.g. Google Gemini Nano Banana , OpenAI Images, fal, Replicate. The key goes in .env . Never paste it into chat. Build reverse-brand <url , a reusable CLI that turns a public brand website into an unofficial, art-directed brand guide . Then run it on the URL above. The guide should look and feel like the brand's own site, not like a report. Every value in it is measured, tokenised, and rebuilt to prove the tokens work. - Mark everything "Unofficial analysis" : the guide's cover, top bar and footer, every slide, and the video. Add "Not affiliated with or endorsed by . Trademarks belong to their owners." - Never copy logos, font files or the brand's images. Describe them and link to the source pages. A logo becomes a dashed placeholder with its measured size and colour. - Never bypass bot checks or CAPTCHAs. If headless capture gets a challenge, stop and offer two options: a --headed , where the user completes the check, or b a paste-into-DevTools snippet that runs the same extractor in same-origin iframes at 1440px and 390px and downloads snapshot.json . - AI images are generated from text prompts only never from the brand's photos as input . Each one is labelled "AI-generated image" wherever it appears. - Copy is plain and technical. Section titles are standard brand-book names: Overview, Contents, Typography, Fonts, Colour, Layout and spacing, Components, Imagery, Voice and tone, Design tokens, Method. No manifestos or slogans. - Use Playwright with an installed Chrome launch with --disable-gpu ; some machines hang on the first canvas call without it . - Capture the home page plus 3–5 key pages, chosen by scoring same-origin links. Nav links and product, pricing, business and company pages score highest. Skip legal, help, login and locale duplicates, and take one page per top-level path. - Use a 1440×900 viewport, plus a 390×844 pass of the home page for responsive type. - Scroll each page to trigger lazy content. Then walk every visible element and record computed font, size, weight, line-height and tracking; text, background, border and fill colours normalised to hex through a 1px canvas, so oklch / lab work ; padding, margin and gap; radius; shadows; max-width containers; grid columns; and media stats. - Also record button and card candidates the painted layer can be a child of the <a , header and nav height, sticky, link style, logo size and colour , form fields, h1–h3, paragraphs, CTA labels, :root custom properties and media-query breakpoints. - Exclude cookie and consent UI by id/class and by dialog text. - Dedupe repeated chrome: any count that is identical on every page header and footer is counted once. - Type: cluster heading sizes within 12%, then assign roles: display, h1–h4, lead, body, small. Record size, line-height ratio, weight and tracking in em. Match the mobile size per role and emit clamp . Report the scale ratio and its nearest named interval. - Colour: report roles background, surface, surface-inverse, text, text-muted, text-inverse, action, on-action, action-alt, accent, border as hex plus OKLCH, with usage share. Weight text by characters and backgrounds by painted area. List gradients and the WCAG contrast of the most-read pairs. - Layout: derive the spacing base unit 8/4/2 and separate the on-grid and off-grid values. Also record radii pill vs px , container width, columns and gutter, breakpoints, and the home page's section rhythm light/dark bands . - Components: button variants filled, outline, translucent , card variants fill, backdrop, radius, padding, media and the nav. If no forms were found, infer an input and flag it "inferred". - Imagery: describe it in words: formats, crops, full-bleed share, object-fit, media share of page area, and alt-text themes. - Voice: quote 5 lines from the site each 15 words or fewer, with source links . Derive 3 rules from measurable stats: headline length, case, "you" vs "we", numbers, the "X, Y" pattern, CTA verbs and exclamation marks. Show the stats. Export CSS custom properties fonts, type roles, colour roles, spacing, layout, radii, button, card, input, nav and the same set as W3C design-tokens JSON, with OKLCH next to hex. Every brand sample in the guide must use only these variables. Derive the art direction from the brand's own tokens instead of hard-coding it. Use its fonts, its alternating light and dark grounds, its card radius, its button shape, its gradient and its declared motion easing. The guide's own controls are the brand's rebuilt buttons. Build it as a sequence of full pages that mirror the site's own layout patterns: 1. Cover: a full-bleed photo or the brand-gradient fallback , " brand guidelines" as a big headline on the image, a one-line factual lede, a pill CTA, and a frosted-glass card showing one token as the site's balance cards do . Add the Unofficial pill and disclaimer at the bottom. 2. Overview: centred headline and 4 big stats typefaces, colours, spacing base, button variants . 3. Contents: 8 image cards, styled like the site's product cards: title bottom-left, number top-left, arrow top-right, slow zoom on hover. 4. Token rebuild test: a button and two cards built only from tokens, on the site's light and dark grounds, with real images in the card media. 5. Typography: a full-bleed chapter photo with a glass specimen card and chip tabs Display, Heading, Subhead, Lead, Body . Each tab shows the role at its real size, weight, leading and tracking; never one size for all. 6. Fonts: a family card per typeface with a giant "Aa", weights and share, and a check whether the font is installed locally. Below it: the character set, the type scale with mobile→desktop values, sizes in use, and the site's declared type variables. 7. Colour dark page : a chrome or product image next to a proportional palette bar, role tiles hex, OKLCH, contrast , the gradient, contrast pairs, and the full palette in a collapsible. 8. Layout and spacing: big facts base, columns, gutter, container , a column diagram, a spacing staircase, radius tiles, breakpoints and the section rhythm. 9. Components dark page : an ordered material image beside the title, then a gallery of measured button variants on their grounds, the nav mock, cards and the form input. 10. Imagery: a photo mosaic with labelled AI images, the measured facts, and source-page links. 11. Voice and tone: a split page photo plus facts , then large quoted lines and 3 numbered rule cards. 12. Design tokens dark page : copy, copy-JSON and download buttons, plus code panels. 13. Method: assumptions, run log and the image prompts table. Motion: subtle rise-in reveals and a slow zoom on photos, using the brand's easing; honour prefers-reduced-motion . Use a sticky top bar that is transparent over the cover and frosted after. Make it responsive with no horizontal scroll at 390px. Write two editions : - brand-guide.html : no images CSS-gradient fallbacks in every image slot , at most 150 KB, works offline. - brand-guide-hero.html : the same guide with images embedded as WebP, each embedded once and reused through CSS classes. - First study the site's photography look, don't copy . Write prompts.json with ~9 slots: hero cover , horizon typography chapter , object studio render for colour , street lifestyle for voice , sky clean background , glass orderly material for components , grid layout , tokens tokens , plus whatever the brand needs. Use new subjects in the brand's photographic style, and append a suffix: "photorealistic, no text, letters, logos, brand marks, cards or app screens". - IMAGES = none : skip; the gradient fallbacks are the design. - IMAGES = api : write a small provider adapter generate {prompt, aspectRatio, resolution} → image bytes for the chosen provider. Read the key from .env and never log it. Save job ids before polling so a crash never loses a paid image, stream downloads to disk, check the balance first, and log the cost per image. - IMAGES = manual : print the prompts. The user generates images in any tool and drops them into images/<slot .png . Add an --import step that builds the manifest from those files. - Re-encode everything to WebP 2000px for wide images, 1400px for others and review the results yourself; regenerate anything off-brand or noisy. Export an Instagram carousel 8 × 1080×1350: cover, typography, colour, layout, components, voice, tokens, outro and X images 4 × 1600×900 . They use the same tokens and images. Every slide carries an Unofficial badge, plus an AI credit when it shows a photo. Put text-heavy content beside the image, not on top of busy areas. Produce a 1920×1080 presentation at real 60 fps ~45 s, 11 scenes from intro to outro . Scenes cross-dissolve with a slight scale and blur, photos get a slow Ken Burns zoom, and text, bars and columns animate in with a staggered ease-out. Render it frame-accurately : keep all CSS animations paused, seek every animation to i/fps through document.getAnimations , capture each frame, and pipe the frames into ffmpeg libx264, CRF 14, yuv420p , TV range, BT.709, faststart . Also write --stills one frame per scene for review. Write verify.js and make it pass. It checks that the guide opens offline with zero network requests, the size budgets 150 KB light, 4 MB hero , that the required sections exist, the Unofficial label, that the token JSON parses, five quotes and three rules, that the rebuild button and card computed styles equal the token values, AI labels on every image, and no horizontal scroll at 390px. Then screenshot every section, slide and video still, look at them, and fix what looks wrong before you finish: truncation, low contrast, collisions, crops, washed-out images. Write run-log.md with the timing per phase, pages and elements, sizes, image cost per image, LLM token usage if known, and every assumption viewports, page picks, role inference, inferred components, fallbacks . out/<host / : - brand-guide.html - brand-guide-hero.html - tokens.css - tokens.json - snapshot.json - prompts.json - images/ - slides/ - video/<host -presentation-1920x1080-60fps.mp4 - run-log.md - the screenshots from verification Finish with a short summary of what you measured, what you assumed and what it cost.