Image-to-Code Skill
Generate website design references first, analyze them deeply, then implement a faithful frontend.
The skill contains only design workflow instructions, no credentials, network calls, external services, or user data, so least privilege is mostly satisfied. However, it explicitly instructs the model to generate design images itself, which is a side effect (resource consumption/content creation) that may require user confirmation, but the docs mention no confirmation step. Also publisher identity is unknown, attribution incomplete. These factor into the deduction, score 12.
The instructions are self-consistent with no obvious contradictions. The key path (generate images → analyze → implement) is clear, but it depends on external image-generation capability; if unavailable the skill fails. Static review cannot test, and error handling/feedback is thin (no clear statement for when image generation is unavailable). Score 7.
The skill targets visual website design, scenarios are clear, but capability boundaries and non-fit ranges are limited (does not explicitly state when not to use, only roughly mentions direct-code when mostly technical). Environmental fit: if image generation depends on services unreachable from mainland China, risk of unusability, not mentioned. Score 7.
Skill has clear structure and progressive disclosure, includes parameters, examples, and design guidance, but lacks install instructions, dependency notes, and known-limitation disclosure. License is explicit in repo as MIT, but this skill file itself lacks versioning or changelog. Maintenance responsibility is clear (via README) but governance info missing inside the skill. Score 10.
The skill promises improved visual design quality through an image-first process, which is a sensible approach. But evidence of directly usable output and marginal benefit is limited, static review cannot validate results, and it may generate many images making cost/benefit unclear. Score 7.
The repo contains example images and sponsor evidence in README, but the skill's key claims (improving design quality, avoiding slop) have no reproducible tests or independent verification. Only author claims and examples, static review cannot independently reproduce. Score 5.
- The skill depends on image generation capability; if unavailable the workflow fails; ensure environment supports it or provide fallback.
- The skill instructs the model to generate images itself, which may cause significant resource consumption; recommend explicit user confirmation when needed.
- If image generation services are unreachable from mainland China, the skill may be unusable; assess network accessibility.
- Static review did not verify actual output quality; recommend testing on small projects before wide adoption.
What does this skill do, and when should you use it?
This is an image-first design workflow skill for coding agents like Codex. It mandates generating high-quality reference images for each section of a website before writing code, then deeply analyzing those images to extract the design system (typography, spacing, colors, components), and finally implementing the frontend to match the images as closely as possible. The skill emphasizes avoiding common AI-generated template patterns like compressed boards, cards-inside-cards, and excessive pill labels. It provides detailed rules and a step-by-step workflow to ensure the resulting interface is art-directed, readable, and implementable.
It defines a workflow: first infer website type and section count from the user request; then generate separate reference images for each section, generating additional detail images (e.g., close-ups of text and buttons) when needed; perform deep analysis on the generated images, extracting text, typography, spacing, colors, buttons, and components; finally implement the frontend code based on these analyses, ensuring fidelity to the image design.
- A developer wants a visually high-quality marketing landing page and wants the agent to design before coding.
- A designer needs to translate a concept sketch into code while preserving design intent.
- A Codex user wants to avoid default template-like interfaces and achieve more art-directed designs.
- A team redesigning an existing website wants to explore new visual directions through images first.
- A developer wants to improve design consistency and implementation accuracy through an image-to-code workflow.
What are this skill's strengths and limitations?
- Enforces an image-first workflow to avoid arbitrary coding and improve design quality
- Provides detailed anti-pattern lists (e.g., cards-inside-cards, fake UI labels)
- Emphasizes generating separate images per section for readability and extractability
- Includes deep analysis rules to ensure the design system is faithfully preserved
- Requires an environment with image generation, otherwise the core workflow cannot execute
- Rules are very detailed and may be overly verbose for simple tasks
- No automated tests are provided; manual verification is needed
- Optimized specifically for Codex; other platforms may need adjustments
How do you install this skill?
Install the whole skill collection (which includes this skill) with:
npx skills add https://github.com/Leonxlnx/taste-skillOr install only this skill:
npx skills add https://github.com/Leonxlnx/taste-skill --skill "image-to-code"You can also manually copy the SKILL.md into your project or paste it into a conversation.
How do you use this skill?
In an agent that supports image generation (e.g., Codex), declare the skill in your prompt, e.g.:
"Follow the image-to-code skill: generate images, then analyze, then code."
Then describe your website request, like 'design an 8-section landing page'. The agent will generate section images, analyze them, and then implement the code.
How does this skill compare with similar options?
Compared to other skills in the collection (e.g., design-taste-frontend), this skill focuses on a workflow involving image generation and implementation, rather than design rules for direct coding.