OpenAI Brand Styling

Overview

Apply OpenAI’s brand identity and style resources with this skill.

Keywords: branding, corporate identity, visual identity, post-processing, styling, brand colors, typography, OpenAI brand, Codex, visual formatting, visual design

Brand Guidelines

Colors

Main Colors:

• Charcoal: #0E0F12 – Primary text and dark backgrounds
• Slate: #202123 – Surface backgrounds
• Mist: #F5F7FA – Light backgrounds and text on dark
• Steel: #9EA1AA – Secondary elements and dividers

Accent Colors:

• OpenAI Green: #10A37F – Primary accent
• Azure: #2B8FFF – Secondary accent
• Graphite: #40434A – Neutral accent for icons or strokes

Typography

• Headings: Inter, Semibold/Bold (with Arial fallback)
• Body Text: Inter, Regular (with Arial fallback)
• Code/Monospace: IBM Plex Mono (with Menlo/monospace fallback)
• Note: Fonts should be pre-installed in your environment for best results

Features

Smart Font Application

• Applies Inter Semibold/Bold to headings (24pt and larger)
• Applies Inter Regular to body text
• Applies IBM Plex Mono for code snippets or inline code
• Automatically falls back to Arial/Menlo if custom fonts unavailable
• Preserves readability across all systems

Text Styling

• Headings (24pt+): Inter Semibold/Bold
• Body text: Inter Regular
• Smart color selection based on background (Charcoal or Mist as appropriate)
• Preserves text hierarchy and formatting

Shape and Accent Colors

• Non-text shapes use accent colors
• Cycle accent usage: OpenAI Green → Azure → Graphite
• Maintains visual interest while staying on-brand

Technical Details

Font Management

• Uses system-installed Inter and IBM Plex Mono fonts when available
• Provides automatic fallback to Arial (headings/body) and Menlo/monospace (code)
• No font installation required – works with existing system fonts
• For best results, pre-install Inter and IBM Plex Mono fonts in your environment

Color Application

• Uses RGB color values for precise brand matching
• Applied via python-pptx’s RGBColor class
• Maintains color fidelity across different systems