github-integration
について
このGitHub連携スキルは、GitHubベースの機能を構築するための認証ヘルパーとGist操作を提供します。デバイスログインフローを通じたトークン管理を処理し、Gistの読み取り、書き込み、レンダリングのユーティリティを含みます。APIアクセスとGist操作を必要とするGitHub機能を実装する際に、開発者はこれを利用すべきです。
クイックインストール
Claude Code
推奨/plugin add https://github.com/dave1010/toolsgit clone https://github.com/dave1010/tools.git ~/.claude/skills/github-integrationこのコマンドをClaude Codeにコピー&ペーストしてスキルをインストールします
ドキュメント
GitHub tokens and auth
- Check whether
github-device-login-tokenis already in local storage before prompting users. - When the token is missing or lacks scopes, direct users to
/tools/github-device-login/to refresh authorization.
Gists
- Review
tools/scratch-pad/index.htmlfor an end-to-end example of creating and saving Gists. - Render saved HTML gists at
https://gistpreview.github.io/?${encodeURIComponent(gistId)}to preview their content.
GitHub リポジトリ
関連スキル
content-collections
メタThis skill provides a production-tested setup for Content Collections, a TypeScript-first tool that transforms Markdown/MDX files into type-safe data collections with Zod validation. Use it when building blogs, documentation sites, or content-heavy Vite + React applications to ensure type safety and automatic content validation. It covers everything from Vite plugin configuration and MDX compilation to deployment optimization and schema validation.
creating-opencode-plugins
メタThis skill provides the structure and API specifications for creating OpenCode plugins that hook into 25+ event types like commands, files, and LSP operations. It offers implementation patterns for JavaScript/TypeScript modules that intercept and extend the AI assistant's lifecycle. Use it when you need to build event-driven plugins for monitoring, custom handling, or extending OpenCode's capabilities.
evaluating-llms-harness
テストThis Claude Skill runs the lm-evaluation-harness to benchmark LLMs across 60+ standardized academic tasks like MMLU and GSM8K. It's designed for developers to compare model quality, track training progress, or report academic results. The tool supports various backends including HuggingFace and vLLM models.
sglang
メタSGLang is a high-performance LLM serving framework that specializes in fast, structured generation for JSON, regex, and agentic workflows using its RadixAttention prefix caching. It delivers significantly faster inference, especially for tasks with repeated prefixes, making it ideal for complex, structured outputs and multi-turn conversations. Choose SGLang over alternatives like vLLM when you need constrained decoding or are building applications with extensive prefix sharing.
