Brand kit

Brand Kit

Build a brand-kit.json describing the user's product in a way other skills can consume — colors, type, voice, do/don't language, optional logo. Other Cogny skills (/tiktok-launch-video, /reddit-launch-video, /linkedin-launch-video, /landing-page-review, /ad-copy-writer) read this file when present.

The skill is built around the reality that users don't always have access to their site's source code. There are six paths to a kit, in priority order. Stop at the first one that yields enough.

Usage

/brand-kit https://example.com — extract from a live site /brand-kit ~/git/our-site — extract from a local repo (Tailwind / CSS vars) /brand-kit ~/Desktop/logo.png — extract palette from a logo or screenshot /brand-kit ~/Desktop/homepage.png logo.png — combine site screenshot + logo /brand-kit — interview the user

Output

A brand-kit.json written to (in priority order, whichever path makes sense for the user's setup):

1. .agents/brand-kit.json (if .agents/ exists)
2. .claude/brand-kit.json (if .claude/ exists)
3. ./brand-kit.json (otherwise — current working directory)

Always print the path you wrote to so the user can move it.

Schema

{
  "name": "cogny",
  "site": "https://cogny.com",
  "logo": {
    "path": "./brand/logo-square.png",
    "use_in_video": false
  },
  "colors": {
    "background": "#1f1d1d",
    "foreground": "#ede8e3",
    "primary":    "#b09acd",
    "accent":     "#c9badf",
    "muted":      "#9a9494",
    "card":       "#282525",
    "fuchsia":    "#d4848a",
    "success":    "#7ec8a4",
    "amber":      "#d4a857"
  },
  "type": {
    "family":   "'JetBrains Mono', 'Fira Code', ui-monospace, monospace",
    "weight_display": 700,
    "weight_body":    500,
    "letter_spacing_display": "-0.02em"
  },
  "voice": {
    "register":   "technical, direct, anti-jargon, CLI-aesthetic",
    "person":     "first-person plural (we / your)",
    "do":         ["concrete numbers", "name the tradeoff", "name the tool"],
    "dont":       ["leverage", "unlock", "revolutionary", "next-gen"]
  },
  "source": "repo: ~/git/cogny/tailwind.config.ts + src/index.css",
  "captured_at": "2026-04-25"
}

Minimum viable kit: colors.background, colors.foreground, colors.primary, type.family, voice.register. Don't ship without all five.

---

Path 0 — Cogny MCP context tree (cheapest, run first if connected)

Use when: the cogny MCP server is in .mcp.json. The user may have already documented their brand — voice, palette, tone — in their context tree, in which case scraping is redundant.

mcp__cogny__get_context_tree_overview              # see what's documented
mcp__cogny__search_context query="brand"           # palette, voice, tone, design tokens
mcp__cogny__search_context query="positioning"     # voice + register often live here
mcp__cogny__read_context_node node_id="…"

If the tree returns brand / palette / voice nodes, hydrate the kit from those values. Cross-check against Path 1 or 3 only if something looks stale or contradictory.

If the cogny MCP isn't connected, or the tree has nothing brand-related, fall through to Path 1.

Path 1 — Repo with Tailwind / CSS variables (best signal)

Use when: the user has access to the site's source.

Look for, in order:

# Tailwind config — custom color namespaces
fd -t f 'tailwind\.config\.(ts|js|mjs|cjs)$' <repo>
# CSS custom properties (most modern stacks)
fd -t f '(globals|index|app|theme|variables)\.css$' <repo>
# Design tokens
fd -t f '(design-tokens|tokens|theme)\.json$' <repo>

When you find a Tailwind config, look for a custom color namespace (commonly the brand name, e.g. cogny: {...}):

// tailwind.config.ts (cogny example)
colors: {
  cogny: { dark: "#1f1d1d", purple: "#b09acd", secondary: "#7E69AB", light: "#c9badf" }
}

When you find a CSS file, grep for the canonical custom-property names:

grep -E '^\s*--(background|foreground|primary|accent|muted|card)' <css-file>

Map the values into the kit schema. If you find both --primary (HSL or hex) and a Tailwind namespace, prefer the Tailwind namespace — it's usually the explicit brand intent rather than a shadcn default.

Type detection in the same files:

grep -E "(font-family|fontFamily|--font-)" <repo>/src/**/*.{css,ts,tsx,js} 2>/dev/null | head

---

Path 2 — Live site URL (works without source access)

Use when: the user only has a public URL.

Steps:

1. Fetch the homepage with WebFetch <url> and extract any inline <style> blocks and the head's stylesheet links.
2. Fetch the main stylesheet (the largest CSS link in the head, typically). curl -sL <css-url> | head -c 200000 is enough — look for CSS custom properties (--primary, --background, etc.) and @font-face rules.
3. Look for a manifest at /site.webmanifest, /manifest.json, or /manifest.webmanifest. Manifests contain theme_color, background_color, and an icon list — gold for brand kits.
4. Look for a favicon and a high-res logo:

   curl -sL "<site>/favicon.ico" -o /tmp/favicon.ico
   curl -sL "<site>/apple-touch-icon.png" -o /tmp/touch.png
   # Or extract from <link rel="icon"> in the homepage HTML
   

   The largest PNG icon on the site (Apple touch icon, manifest icon) is the brand mark.
5. Detect typography: search the inline CSS for font-family declarations on body, html, or h1. If you see Google Fonts links in <head>, the family name is right there in the URL.

If the site ships a static frontend (Next.js, etc.), the bundled CSS often contains the same custom properties as the dev source — so you might still recover the full brand palette without repo access.

If you only get partial info (e.g. you got theme_color from the manifest but no full palette), proceed to Path 3 to fill in gaps from the logo / a screenshot.

---

Path 3 — Logo or screenshot palette extraction (the fallback that always works)

Use when: the user uploads a logo image, a screenshot of their homepage, or a brand asset PNG/JPG.

The user usually has some image — a logo on Desktop, a slack avatar, a website screenshot. Extract a palette directly with FFmpeg:

# Generate a small palette image (max 6 distinct colors) from any image.
ffmpeg -y -i <input.png> \
  -vf "palettegen=max_colors=6:reserve_transparent=0:stats_mode=single" \
  -frames:v 1 -update 1 /tmp/_palette.png

# Read those palette colors back as hex. The PPM header is
#   P6\n<W> <H>\n255\n  → 13 bytes for the standard 16x16 palette.
ffmpeg -y -i /tmp/_palette.png -c:v ppm -f image2 -update 1 - 2>/dev/null \
  | tail -c +14 \
  | xxd -c 3 -p \
  | awk '!seen[$0]++' \
  | head -8 \
  | awk '{print "#" toupper($1)}'

Output for the cogny logo (gradient C on dark warm bg):

#1D1B1B   ← background
#5F4953   ← deep plum
#BB93B9   ← lavender (primary brand)
#CA8B9D   ← coral-pink
#D2858C   ← coral (accent)

Mapping rules — the palette is sorted by frequency, but the role assignment is yours to make:

• Darkest color → colors.background (if it covers >40% of pixels)
• Brightest non-white → colors.foreground (if there's no off-white, default #fafafa)
• Most saturated chroma → colors.primary
• Second most saturated → colors.accent

Eyeball the original image once and confirm — algorithmic role assignment gets it right ~70% of the time.

For typography from a screenshot: image-based font detection is unreliable. Either:

• Ask the user ("the headline font on this image is…?"), or
• Use a free tool like WhatTheFont or Fontspring Matcherator — show the user the URL, don't try to do it programmatically.

Optional: semantic palette via node-vibrant (one npm install, gives vibrant/muted/dark-vibrant/light-vibrant categories — more useful for primary/accent role mapping):

npm install -g node-vibrant
node -e "
  const Vibrant = require('node-vibrant');
  Vibrant.from(process.argv[1]).getPalette().then(p => {
    const out = {};
    for (const k of ['Vibrant','Muted','DarkVibrant','LightVibrant','DarkMuted','LightMuted']) {
      if (p[k]) out[k] = p[k].hex;
    }
    console.log(JSON.stringify(out, null, 2));
  });
" <input.png>

Use whichever is available. FFmpeg ships with the video skills already, so that's the default.

---

Path 4 — Color picker / manual entry (always available)

Use when: nothing else worked, or the user wants to override the kit explicitly.

Ask, in this order, and accept partial answers:

1. Background hex? (the page background users see most)
2. Foreground hex? (body text color)
3. Primary brand hex? (the color a button or link uses)
4. Accent hex? (optional — if there's a second brand color)
5. Font family? ("a sans like Inter", "JetBrains Mono", etc. — best-effort, system fallbacks are fine)

Three colors and a font is enough to ship.

---

Path 5 — Voice extraction (always run, regardless of color path)

Voice is the part of a brand kit that copy-paste tooling forgets. It changes which beats a video would write or which headlines an ad-copy skill would produce.

Source for voice: the homepage hero + 1–2 deeper pages (about, pricing, blog).

Steps:

1. Fetch the homepage hero text (h1, h2, the first paragraph after the hero CTA).
2. Fetch one deep page (/about or /pricing typically).
3. Read the copy and produce three fields:

"voice": {
  "register": "<one-line description>",
  "do":   ["pattern 1", "pattern 2", "pattern 3"],
  "dont": ["banned word 1", "banned word 2"]
}

Examples (these are real, derived from each company's homepage):

Brand	register	do	dont
cogny	"technical, direct, anti-jargon, CLI-aesthetic"	concrete numbers, name the tradeoff, name the tool	leverage, unlock, revolutionary, next-gen
Linear	"operator, opinionated, fast"	second-person, terse, build-mode language	enterprise, synergy, robust
Mailchimp	"warm, conversational, plain-English"	first-person, friendly contractions, clear CTAs	utilize, paradigm, leverage

If you can't infer voice from the page (e.g. very thin homepage), ask the user for one sentence describing how they want to sound. Don't make it up.

---

Steps (the actual flow)

1. Detect what input the user gave you:

   • URL → Path 2 → fall through to 3 / 5 if needed
   • Repo path → Path 1 → fall through to 5
   • Image path → Path 3 + ask for site URL or repo for voice (Path 5)
   • Nothing → Path 4 + ask if they have a logo to extract from

2. Build the kit incrementally. Print partial state after each step so the user can intervene:

   ◆ Brand kit (cogny)
   ✓ colors    bg=#1f1d1d  fg=#ede8e3  primary=#b09acd  accent=#c9badf
   ✓ type      JetBrains Mono / mono
   ◇ voice     pending — fetching /about for analysis…
   

3. Show the user the result before writing the file. They should approve color role assignments (background vs primary, etc.) — these are the high-leverage calls.

4. Write brand-kit.json to the most appropriate location (see Output section). If a kit already exists, diff it and ask before overwriting.

5. Print a one-line install hint for the consumer skills:

   Saved → .agents/brand-kit.json
   Used by: /tiktok-launch-video, /reddit-launch-video, /linkedin-launch-video,
            /ad-copy-writer, /landing-page-review
   

Common mistakes

1. Treating a single hex color as the brand — most brands have a primary + an accent + a background-foreground pair. Three colors minimum.
2. Shipping without voice — colors carry brand 30%; voice carries it 70%. Don't skip Path 5.
3. Using shadcn defaults as the brand — if the user's CSS file has the literal shadcn --primary: 240 5.9% 10%, that's a placeholder not a brand. Cross-check with the homepage screenshot.
4. Over-saturating the kit with logo gradient artifacts — palette extraction from a gradient logo will return 4–5 transitional colors. Pick the endpoints (primary + accent), drop the in-betweens.
5. Letting LinkedIn-blue / Twitter-blue / TikTok-pink slip into the kit — those are platform UI colors that bleed into screenshots, not brand colors.
6. Trusting theme-color blindly — sites sometimes set theme-color to match the address bar of mobile Safari, which is decorative and may not match the actual brand primary.

Related

• /tiktok-launch-video, /reddit-launch-video, /linkedin-launch-video — consume the kit
• /ad-copy-writer — uses voice rules to write platform-appropriate copy
• /landing-page-review — checks consistency between a landing page and the brand kit
• /non-commodity-content — voice extraction overlaps with this skill's interview step