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GitHub リポジトリ

claude-tools

claude-tools には JNLei から収集した 3 個の skills があり、リポジトリ単位の職業カバレッジとサイト内 skill 詳細ページを表示します。

収集済み skills
3
Stars
20
更新
2025-11-20
Forks
5
職業カバレッジ
2 件の職業カテゴリ · 100% 分類済み
リポジトリエクスプローラー

このリポジトリの skills

frontend-design
グラフィックデザイナー

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
ソフトウェア開発者

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
ソフトウェア開発者

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