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Génère des images de conception pour des sections de site web, les analyse en profondeur, puis implémente un frontend responsive qui correspond étroitement aux références visuelles.
Cette compétence impose un flux de travail « image-first » pour une conception et une implémentation de site web haute fidélité. Elle génère des images de conception claires au niveau de la section, effectue une analyse visuelle approfondie (typographie, espacement, couleurs, rythme des composants) et utilise ces références pour implémenter du code frontend responsive. L'objectif est d'obtenir des traductions fidèles et faciles à implémenter de visuels sous direction artistique, plutôt que des sorties de modèles génériques.
Utilisez cette compétence lorsque la qualité visuelle est primordiale : sections hero, pages d'atterrissage (landing pages), sites marketing, pages éditoriales, pages produits et refontes où des références de conception lisibles et extractibles sont requises avant le codage. Elle est idéale pour les flux de travail où les images peuvent être générées et utilisées comme source principale de vérité.
Idéal pour les agents dotés de capacités de génération d'images et d'implémentation frontend (Codex, Claude Code, Cursor, agents de codage de type Copilot et autres agents de design sensibles aux images).
A lengthy design-to-code prompting skill for Codex that instructs an AI to generate website section images first, deeply analyze them, then implement frontend to match. The SKILL.md is extremely detailed with 38 sections covering image-first workflow rules, anti-slop guidelines, and extraction discipline. No scripts bundled — purely a prompting/instruction skill. No security concerns beyond the upstream reference in frontmatter. Well-written but narrowly targeted at Codex image-generation workflows and extremely verbose.
Pure instruction/prompting skill with no executable code. Security is clean — no credentials, no network calls, no destructive commands. The 'od.upstream' field references an external repo but that's just attribution. Quality is good — the instructions are detailed and coherent — but there's significant repetition (anti-slop rules stated 3+ times, image-first rule repeated throughout). Architecture is mediocre: frontmatter is present and valid, but there are no scripts, no references/ directory, no output contracts, and the body is a monolithic 38-section prompt that would benefit from progressive disclosure or splitting into references/. Usefulness is limited: this only helps in Codex or similar image-gen environments for website design tasks; the audience is narrow and the skill is essentially a very long system prompt rather than an actionable tool.
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