What does this skill do, and when should you use it?
The brandkit skill is an image-generation prompt framework for creating high-end brand-kit images, such as brand-guideline boards, logo systems, identity decks, and visual-world presentations. It is optimized for categories including minimalist, cinematic, editorial, dark-tech, luxury, cultural, security, gaming, developer-tool, and consumer-app brand systems. The skill emphasizes intentionality, restraint, and strategic brand storytelling, guiding AI to produce professional brand presentations rather than generic or messy designs.
The skill takes a brand name and optional references, then generates a brand-kit overview image. It infers brand strategy (category, audience, emotional promise, etc.), selects a visual mode (e.g., dark developer, luxury/beauty), and constructs a grid-based presentation board. It allows custom layouts (3x3, 2x3, 2x2, etc.) and enforces design principles like sparse typography, disciplined palettes, symbolic logo design, and premium detailing. The output is an image suitable for presentation or as a design reference.
- A freelance designer needs to quickly mock up a professional brand-guideline board for a startup
- A marketing team wants to generate visual brand directions for a new product launch
- An entrepreneur needs a visual identity concept to pitch to investors
- A developer wants to generate brand-style reference images for an app before coding it
- A design student wants to learn premium brand identity system principles
How do you install this skill?
- The skill depends on external image generation models; actual output quality cannot be verified from static files.
- No explicit non-fit scenarios or user confirmation mechanism provided; users must judge on their own.
- Environment fit for Chinese users (e.g., mainland network accessibility) is not specified.
Install via an AI agent's skills mechanism, or by copying the SKILL.md file manually. For Vercel's agent-skills CLI, run npx skills add https://github.com/Leonxlnx/taste-skill --skill "brandkit".
tmp="$(mktemp -d)"
git clone --depth 1 https://github.com/Leonxlnx/taste-skill.git "$tmp"
mkdir -p ~/.claude/skills
cp -R "$tmp/skills/brandkit" ~/.claude/skills/
rm -rf "$tmp"Generated from the source repository and skill path; it copies only this skill's folder. If the author's install steps above differ, follow those first. To scope it to one project, replace ~/.claude/skills with that project's .claude/skills.
How do you use this skill?
Once installed, send your agent any of these to trigger it:
- Use the brandkit skill to generate a brand-kit image for [Brand Name].
Once installed, provide the brand name and context in a prompt to a skill-aware AI agent (e.g., Claude Code, Codex, ChatGPT Images) and specifically invoke the brandkit skill, e.g., "Use the brandkit skill to generate a brand-kit image for [Brand Name]." Iterate on the output until it meets desired quality. For image-to-code workflows, feed the generated images to a coding agent.
What are this skill's strengths and limitations?
- Establishes clear brand strategy for more intentional design
- Provides multiple visual modes and preset layouts for diverse brand types
- Includes anti-generic rules to avoid boilerplate aesthetics
- Output is presentation-ready and can serve as a design brief
- Produces only images, no code
- Relies on external image generation tools (e.g., ChatGPT Images) and may not always handle text rendering perfectly
- The skill is textual guidance, so results depend on the AI's ability to interpret and follow complex design rules
- No screenshots or example outputs are provided, so it's hard to preview the exact output style
How does this skill compare with similar options?
Side by side with related skills; every score comes from the same FSRS standard.
| Skill | FS score | Stars | Last updated | License |
|---|---|---|---|---|
| Brandkit Generation Skill this page | 50 · Use with care | ★ 94k | 4d ago | MIT |
| Superdesign Design Skill | 53 · Use with care | ★ 647 | 1mo ago | MIT |
| Hatch Pet Animation Workshop ✓ OpenAI · Official | 51 · Use with care | ★ 28k | 3mo ago | — |
| imagegen-frontend-mobile — Mobile App Visual Generation Skill | 63 · Recommended | ★ 94k | 4d ago | MIT |
| Slides Grab HTML Skill | 60 · Recommended | ★ 1.2k | 1mo ago | MIT |
How did FollowSkills review this skill?
The skill provides only image generation guidance, with no code execution, permission requests, data collection, or external network calls. Data flow is transparent. However, no user confirmation or rollback mechanism is provided, and it relies on external image generation models, introducing uncertainty. Deductions: incomplete permissions and confirmation, but no severe overreach.
The skill's instructions are internally consistent and the content is self-coherent, with clear structure. However, it lacks tests, edge case handling, and failure feedback. As a static assessment, key paths requiring execution cannot be reproduced. Deductions: no reproducible tests or error handling.
The skill clearly declares applicable scenarios (brand kit image generation), target audience (designers, developers), and output formats, with detailed visual style guidance. However, capability boundaries and non-fit scenarios are not explicit, and trigger conditions depend on user usage. Chinese user environment fit is not mentioned, and potential reliance on overseas services is unclear. Deductions: incomplete boundaries and environment fit.
Documentation is well-structured with clear layering, detailed examples, FAQ, explicit installation method, and license. However, version history and changelog are missing, maintenance responsibility is partially clear but update path is not explicit. Deductions: missing versioning and change records.
The skill provides detailed guidance that could theoretically help generate high-quality brand kit images, but actual results depend on external image models, which cannot be verified statically. Output completeness is unknown. Deductions: unverified output effectiveness, limited evidence of marginal value.
The files contain only author claims, with no third-party verification or reproducible tests. No execution evidence. Deductions: lack of independent verification, only author claims.
Open a dimension to read why it scored that way
Evidence confidence：Low — Mostly static review, author material or a limited demo; useful for discovery, not high-risk decisions.
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