imagegen-frontend-web: Frontend Website Reference Generator
Generate premium, implementation-ready horizontal reference images per section — free from generic AI slop.
The skill only generates images based on prompt text, requests no system permissions, exfiltrates no data, and shows no malicious behavior or overreach. MIT license is explicit; unverified publisher is not a deduction. Deductions: no user confirmation mechanism before output, and no disclosure on usage scope (e.g., copyright, sensitive content).
The skill's instructions are self-consistent, clearly requiring one horizontal image per section. However, static review cannot execute validation, and no tests or CI prove key-path reproducibility. Deductions: lack of executable verification evidence; insufficient handling description for abnormal input (e.g., user requesting combined images).
The skill clearly targets landing pages, marketing sites, etc., with explicit style mappings and default section counts. Boundaries are clear. However, no fallback strategy for inaccessible overseas services is documented, and the image generation model may require overseas network access. Deduction: no statement on mainland-China network reachability.
The SKILL.md is well-structured, providing hard rules, configuration, and component guidance. The repo has README and MIT license, but the skill file itself has no version or changelog (though repo has CHANGELOG). Deductions: no version info in the skill file; maintenance responsibility not explicit.
The skill provides detailed anti-AI-slop rules and design system guidance, theoretically producing high-quality frontend reference images. But static review cannot verify output quality, and no actual samples or user feedback are provided. Deductions: value claims unverifiable in static review; comparative evidence limited.
Repo has no CI workflows or test suites; the skill is prompt text, not automatable testable. README includes example images, but the skill itself has no independent reproducible evidence. Deduction: only author claims, no corroboration.
- Generated images may involve copyright or sensitive content; ensure compliance when using.
- This skill depends on overseas image generation services, which may be inaccessible from mainland China; consider alternatives.
- Publisher identity is unverified; assess risks before installation.
What does this skill do, and when should you use it?
This is an Agent Skill for generating 'website visual reference images'. It mandates one separate horizontal image per page section (e.g., an 8-section landing page yields 8 images) and exposes tunable baseline dials (design variance, visual density, art direction, etc.) to avoid repetitive AI defaults. The skill encodes extensive anti-AI-slop rules—banning clichés like the overused left-text/right-image hero, enforcing palette consistency, and requiring varied composition anchors and background modes. It targets landing pages, marketing sites, and product comps that developers or coding models can accurately recreate.
The skill reads the user's prompt (e.g., 'landing page' or '12 sections'), determines the section count, and then generates one independent horizontal image per section (default: 6 for landing pages, 8 for full websites). Internally, using baseline dials (DESIGN_VARIANCE: 8, VISUAL_DENSITY: 4, ART_DIRECTION: 8, etc.), it selects a theme paradigm, background character, typography character, hero architecture, composition anchor, background mode, CTA style, hero scale, and narrative spine—then enforces strict output rules: one section per frame, horizontal orientation, and consistent brand world across all images. It produces no code; it only outputs reference images.
- Designers or developers who want quick, high-quality section-by-section visual references for a landing page; they can prompt with 'generate a 6-section landing page'.
- When using ChatGPT Images or Codex image mode, users can paste this SKILL.md and request specific frames like 'hero', 'trust bar', etc.
- In an image-to-code workflow, this skill first generates the reference images, then hands them to a coding agent (e.g., Codex, Cursor, Claude Code) for implementation.
- When creating a cohesive set of reference images for a marketing site or product page without falling into tired AI visual patterns.
What are this skill's strengths and limitations?
- Enforces one-image-per-section output, preventing multi-section compression into a single frame.
- Extensive anti-AI-slop rules significantly reduce generic, templated designs.
- Tunable dials (e.g., VISUAL_DENSITY, ART_DIRECTION) allow adaptation to different stylistic briefs.
- Designed for implementation clarity—the images serve as clear references for frontend developers or coding models.
- Open source under the MIT license.
- Produces reference images only; no code is generated.
- Actual output quality depends on the underlying image model, so it may not always achieve Awwwards-level polish.
- No test suite or verification mechanism exists to guarantee every prompt executes flawlessly.
- The rule set is dense, requiring some learning curve to fully leverage.
How do you install this skill?
This skill can be installed via the repo's npx skills add command (specify --skill imagegen-frontend-web), or by copying skills/imagegen-frontend-web/SKILL.md into your project or conversation. Exact folder placement and activation steps are covered in the repository's README; for a single skill, copying the SKILL.md content into your agent chat is enough.
How do you use this skill?
Attach or paste the skill's content into an image-capable conversation (e.g., ChatGPT Images, Codex image mode), then issue a natural-language request such as 'Design an 8-section marketing site', or 'Hero section for an AI startup'. The skill will automatically determine the number of sections and generate the corresponding horizontal images, labeled 'Section X of N'. Afterwards, feed the renders to your coding agent for implementation.
How does this skill compare with similar options?
Within the same repository, imagegen-frontend-mobile targets mobile screens and flows, and brandkit generates brand identity boards; this skill focuses specifically on web frontend reference images.