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JNLei
Perfil de creador de GitHub

JNLei

Vista por repositorio de 3 skills recopiladas en 1 repositorios de GitHub.

skills recopiladas
3
repositorios
1
actualizado
2025-11-20
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Repositorios principales por número de skills recopiladas, con su participación en este catálogo del creador y su variedad ocupacional.

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Repositorios y skills representativas

frontend-design
Diseñadores gráficos

Create distinctive, production-grade frontend interfaces with exceptional design quality. Two modes - (1) New projects - bold aesthetic design philosophy including typography, color theory, spatial composition, motion design, visual details, avoiding generic AI aesthetics, creating unforgettable interfaces. (2) Existing codebases - mandatory design language analysis enforcing consistency by scanning layout patterns, typography hierarchy, component structure, spacing, theme systems before implementation. Use when building components, pages, applications, design systems, UI modifications. Covers React, Vue, Next.js, HTML/CSS, Tailwind. Keywords - create component, build page, design interface, add UI, aesthetic design, visual design, typography, animations, spatial layout, design system, consistency, pattern analysis, existing codebase.

2025-11-20
skill-developer
Desarrolladores de software

Create and manage Claude Code skills following Anthropic best practices. Use when creating new skills, modifying skill-rules.json, understanding trigger patterns, working with hooks, debugging skill activation, or implementing progressive disclosure. Covers skill structure, YAML frontmatter, trigger types (keywords, intent patterns, file paths, content patterns), enforcement levels (block, suggest, warn), hook mechanisms (UserPromptSubmit, PreToolUse), session tracking, and the 500-line rule.

2025-11-20
skill-optimizer
Desarrolladores de software

Optimize Claude Code skills for token efficiency using progressive disclosure and content loading order. Use when optimizing skills, reducing token usage, restructuring skill content, improving skill performance, analyzing skill size, applying 500-line rule, implementing progressive disclosure, organizing reference files, optimizing YAML frontmatter, reducing context consumption, improving skill architecture, analyzing token costs, splitting large skills, or working with skill content loading. Covers Level 1 (metadata), Level 2 (instructions), Level 3 (resources) loading optimization.

2025-11-20
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