NetworkX
技能 已验证 活跃Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing network topologies. Applicable to social networks, biological networks, transportation systems, citation networks, and any domain involving pairwise relationships.
To enable AI agents to perform complex network and graph analysis tasks, from creating and manipulating graph structures to running advanced algorithms and generating visualizations.
功能
- Graph creation and manipulation (Graph, DiGraph, MultiGraph, MultiDiGraph)
- Extensive graph algorithms (shortest paths, centrality, community detection, flow)
- Graph generators for various network models (random, small-world, scale-free)
- Support for multiple graph I/O formats (edge list, GraphML, JSON, Pandas, NumPy)
- Integrated visualization capabilities using Matplotlib and links to interactive libraries
使用场景
- Analyzing social networks, biological networks, or transportation systems
- Computing graph algorithms like shortest paths or centrality measures
- Detecting communities or generating synthetic networks for testing
- Visualizing network topologies and structures
非目标
- Performing graph analysis outside of Python's NetworkX library
- Offering real-time graph database integration
- Providing a graphical user interface for graph editing
工作流
- Load or create a graph structure.
- Examine graph properties (nodes, edges, degree).
- Apply relevant algorithms (shortest paths, centrality, community detection).
- Optionally, visualize the graph.
- Export results or save the modified graph.
安装
npx skills add K-Dense-AI/claude-scientific-skills通过 npx 运行 Vercel skills CLI(skills.sh)— 需要本地安装 Node.js,以及至少一个兼容 skills 的智能体(Claude Code、Cursor、Codex 等)。前提是仓库遵循 agentskills.io 格式。
质量评分
已验证类似扩展
AlterLab NetworkX
98Part of the AlterLab Academic Skills suite. Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing network topologies. Applicable to social networks, biological networks, transportation systems, citation networks, and any domain involving pairwise relationships.
Measure Dashboard Requirements
100Specifies requirements for an analytics dashboard including metrics, visualizations, filters, and data sources. Use when requesting dashboards from data teams, defining KPI tracking, or documenting reporting needs.
Alterlab Nmc Data Journalist
100This skill should be used when the user asks about "data journalism", "data visualization", "data storytelling", "FOIA request", "RTI request", "spreadsheet analysis", "chart design", "act as a data journalist", "data journalist mode", "infographic", "open data", "statistical analysis for journalism", "investigative data", "public records", "Datawrapper", "Flourish", "Tableau Public", "D3.js", "chart type selection", or needs expertise in finding, analyzing, and visualizing data for journalistic storytelling. Part of the AlterLab FC Skills collection (New Media & Communication department).
Embedding Strategies
100Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embedding quality for specific domains.
Aws Cdk Development
100AWS Cloud Development Kit (CDK) 专家，用于使用 TypeScript/Python 构建云基础设施。在创建 CDK 堆栈、定义 CDK 构造、实现基础设施即代码，或当用户提及 CDK、CloudFormation、IaC、cdk synth、cdk deploy，或希望以编程方式定义 AWS 基础设施时使用。涵盖 CDK 应用结构、构造模式、堆栈组合和部署工作流。
Fit Drift Diffusion Model
100Fit cognitive drift-diffusion models (Ratcliff DDM) to reaction time and accuracy data with parameter estimation (drift rate, boundary separation, non-decision time), model comparison, and parameter recovery validation. Use when modeling binary decision-making with reaction time data, estimating cognitive parameters from experimental data, comparing sequential sampling model variants, or decomposing speed-accuracy tradeoff effects into latent cognitive components.