Results for “network-graph”

NetworkX is a Python package for creating, manipulating, and analyzing complex networks and graphs.

NetworkX is a Python package for creating, manipulating, and analyzing complex networks and graphs.

NetworkX is a Python package for creating, manipulating, and analyzing complex networks and graphs.

NetworkX is a Python package for creating, manipulating, and analyzing complex networks and graphs.

NetworkX is a Python package for creating, manipulating, and analyzing complex networks and graphs.

NetworkX is a Python package for creating, manipulating, and analyzing complex networks and graphs.

Interactive knowledge graph analysis. Routes natural language questions to graph scripts, interprets results in domain vocabulary, and suggests concrete actions. Triggers on "/graph", "/graph health", "/graph triangles", "find synthesis opportunities", "graph analysis".

NetworkX is a Python package for creating, manipulating, and analyzing complex networks and graphs.

NetworkX is a Python package for creating, manipulating, and analyzing complex networks and graphs.

NetworkX is a Python package for creating, manipulating, and analyzing complex networks and graphs.

Integrate Microsoft Graph SDK into .NET, TypeScript/JavaScript, or Python projects to access Microsoft 365 data and services.

Generates Mermaid diagrams from code graphs, including call graphs, class hierarchies, module dependency maps, and data flow visualizations.

Creates a New York Times-style data chart frame with animated reveal, supporting line, bar, and range-band charts from CSV/JSON or a textual conclusion.

Creates hand-drawn wireframes with graph-paper background, multiple tabs, scribbled chart placeholders, hatched fills, and sticky-note annotations.

DriveLM: Driving with Graph Visual Question Answering

Design GraphQL schemas, resolvers, and DataLoader patterns, and integrate clients with Apollo or urql.

Design GraphQL schemas, implement Apollo Federation, and build real-time subscriptions with DataLoader patterns and query optimization.

嵌入式网络调试工具，用于发现接口、抓包、分析 pcap/pcapng、做连通性测试、端口扫描和流量统计。 当用户提到 Wireshark、tshark、Npcap、抓包、网络联调、端口扫描、连通性排查、pcap 分析、 网络接口、ping 测试、traceroute、流量统计、Modbus TCP、EtherNet/IP 等网络协议调试时自动触发， 也兼容 /net 显式调用。即使用户只是说"抓个包看看"、"扫一下端口"、"网络通不通"或"分析一下这个 pcap"， 只要上下文中出现具体工具名（tshark、Wireshark、Npcap）、协议名（Modbus TCP、EtherNet/IP、ICMP 等）、 调试动作（抓包、端口扫描、连通性测试、ping、traceroute、流量统计、pcap 分析）或网络接口操作，就应触发此 skill。

Simulator.Company graph structure specialist. Use when the user wants to build, edit, analyze, inspect, or ask questions about business process graphs, flowcharts, algorithms, actors (nodes), links (edges), or layers (visual views) in Simulator.Company. Trigger on any of these intents: — Creating: "create graph", "build flowchart", "new diagram", "add actor", "add block to graph", "create algorithm", "draw flowchart", "digital twin", "create process on graph", "build process diagram", "FlowchartBlock", "startStop", "predefinedProcess". — Editing: "edit graph", "update actor", "rename node", "change color", "move actor", "add step to flowchart", "modify diagram", "restructure process", "add edge", "remove link", "reorder steps", "update layer". — Syncing: "push graph", "pull graph", "sync graph", "push changes", "apply edits to layer". — Querying / analysis: "what actors are on this layer", "show me the graph", "who is connected to", "find actor", "list nodes", "describe the process", "analyze the flowchart",

Knowledge graph and smart memory management using graphify + Obsidian-inspired patterns. Use when: setting up a knowledge graph, managing memory health, cross-linking notes, compiling wiki pages from scattered notes, adding structured frontmatter, or running memory health checks. Triggers on: 'knowledge graph', 'graphify', 'wiki', 'cross-link', 'memory health', 'frontmatter', 'compile notes', 'wikilinks'.

Builds a navigable knowledge graph from any folder of files, with community detection, an audit trail, and outputs including interactive HTML, GraphRAG-ready JSON, and a plain-language report.

Predict molecular properties, train graph neural networks, and run drug discovery workflows using DeepChem's featurizers, models, and MoleculeNet benchmarks.

Create animated technical explainers using Manim for graphs, workflows, system diagrams, and product walkthroughs with precise motion and clear scene planning.

Build reusable Manim explainers for technical concepts, graphs, system diagrams, and product walkthroughs, then hand off to the wider ECC video stack if needed.

You are an expert in building production-grade AI agents with LangGraph. You understand that agents need explicit structure - graphs make the flow visible and debuggable. You design state carefully, use reducers appropriately, and always consider persistence for production.