MCP HubMCP Hub
スキル一覧に戻る

creating-data-visualizations

jeremylongshore
更新日 Today
50 閲覧
712
74
712
GitHubで表示
メタaidesigndata

について

このスキルは、Claudeが提供されたデータからチャートやグラフなどのデータ可視化を生成できるようにします。データ構造を自動的に分析し、最も適切な可視化タイプを選択して、情報豊富なグラフィックを作成します。ユーザーがプロットを要求したり、データパターンを視覚的に探索・提示する必要がある場合に、開発者はこのスキルを使用するのが適しています。

クイックインストール

Claude Code

推奨
プラグインコマンド推奨
/plugin add https://github.com/jeremylongshore/claude-code-plugins-plus
Git クローン代替
git clone https://github.com/jeremylongshore/claude-code-plugins-plus.git ~/.claude/skills/creating-data-visualizations

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

Overview

This skill empowers Claude to transform raw data into compelling visual representations. It leverages intelligent automation to select optimal visualization types and generate informative plots, charts, and graphs. This skill helps users understand complex data more easily.

How It Works

  1. Data Analysis: Claude analyzes the provided data to understand its structure, type, and distribution.
  2. Visualization Selection: Based on the data analysis, Claude selects the most appropriate visualization type (e.g., bar chart, scatter plot, line graph).
  3. Visualization Generation: Claude generates the visualization using appropriate libraries and best practices for visual clarity and accuracy.

When to Use This Skill

This skill activates when you need to:

  • Create a visual representation of data.
  • Generate a specific type of plot, chart, or graph (e.g., "create a bar chart").
  • Explore data patterns and relationships through visualization.

Examples

Example 1: Visualizing Sales Data

User request: "Create a bar chart showing sales by region."

The skill will:

  1. Analyze the sales data, identifying regions and corresponding sales figures.
  2. Generate a bar chart with regions on the x-axis and sales on the y-axis.

Example 2: Plotting Stock Prices

User request: "Plot the stock price of AAPL over the last year."

The skill will:

  1. Retrieve historical stock price data for AAPL.
  2. Generate a line graph showing the stock price over time.

Best Practices

  • Data Clarity: Ensure the data is clean and well-formatted before requesting a visualization.
  • Specific Requests: Be specific about the desired visualization type and any relevant data filters.
  • Contextual Information: Provide context about the data and the purpose of the visualization.

Integration

This skill can be integrated with other data processing and analysis tools within the Claude Code environment. It can receive data from other skills and provide visualizations for further analysis or reporting.

GitHub リポジトリ

jeremylongshore/claude-code-plugins-plus
パス: backups/skills-migration-20251108-070147/plugins/ai-ml/data-visualization-creator/skills/data-visualization-creator
aiautomationclaude-codedevopsmarketplacemcp

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.

スキルを見る

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.

スキルを見る

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.

スキルを見る

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.

スキルを見る