logo-generator

logo-generator produces professional SVG logos and high-end showcase presentation images for products and brands. It addresses the full path from concept to deliverable: gathering brand information, generating multiple distinct logo concepts as clean SVG code, iterating on them with user feedback, and then rendering premium showcase images against a range of professional backgrounds. It is intended for creating a logo or icon, generating design concepts from product information, building showcase presentations, exporting logos in multiple formats, and iterating on designs with different visual styles.

The workflow runs in five phases. Phase 1 gathers essential information (product/brand name, industry, core concept, and design preferences such as style, color, and mood). Phase 2 generates at least 6 distinct design variants based on references/design_patterns.md, produces SVG code using viewBox="0 0 100 100" and currentColor for flexible color control, and builds an interactive showcase webpage from assets/showcase_template.html. Phase 3 supports targeted iteration and refinement. Phase 4 exports the SVG to PNG using scripts/svg_to_png.py (default 1024x1024, transparent background), selects showcase styles from references/background_styles.md, and generates showcase images with scripts/generate_showcase.py (including an --all-styles flag) using Nano Banana (Gemini image generation). Phase 5 delivers the interactive HTML showcase, SVG files, PNG exports, and showcase images. The skill documents 12 professional background styles split into 6 dark styles (void, frosted, fluid, spotlight, analog_liquid, led_matrix) and 6 light styles (editorial, iridescent, morning, clinical, ui_container, swiss_flat), plus common SVG patterns like concentric circle dots, geometric shape with line accent, and node network.

logo-generator suits designers, founders, and product teams who want brand-quality logo concepts with polished presentation assets. The showcase generation script requires Python 3.8+, dependencies installed via pip install -r requirements.txt, environment variables in a .env file (copied from .env.example), and a reference PNG exported from the SVG. It supports both the official Google Gemini API and third-party endpoints via GEMINI_API_BASE_URL. Troubleshooting guidance covers SVG display issues, PNG export failures (requiring cairosvg), showcase generation failures (valid GEMINI_API_KEY, readable reference PNG, API quota), and third-party API URL formatting.