Results for “relationships”

Statistical visualization with pandas integration. Use for quick exploration of distributions, relationships, and categorical comparisons with attractive defaults. Best for box plots, violin plots, pair plots, heatmaps. Built on matplotlib. For interactive plots use plotly;...

Provides JPA/Hibernate patterns for Spring Boot data modeling, including entity design, relationships, query optimization, transactions, auditing, indexing, pagination, and connection pooling.

Build and optimize Power BI semantic models with star schema design, DAX measures, relationships, and row-level security following Microsoft best practices.

Evaluates Power BI data model architecture, relationships, storage modes, and performance to identify optimization opportunities and ensure adherence to best practices.

Detect abuse of legitimate Windows binaries (LOLBins) used for living off the land attacks by monitoring process creation, command-line arguments, and parent-child relationships.

Manage AWS RDS relational databases: provision instances, configure backups, manage replicas, troubleshoot connectivity, and optimize performance.

Explains the Financial Services Cloud data model, covering managed-package and Core FSC object structures, household relationships, financial account ownership, and the rollup framework, with SOQL query patterns.

MySQL relational database. Covers queries, indexes, and optimization. Use when working with MySQL databases. USE WHEN: user mentions "mysql", "mariadb", asks about "AUTO_INCREMENT", "ON DUPLICATE KEY UPDATE", "GROUP_CONCAT", "mysql specific syntax" DO NOT USE FOR: PostgreSQL - use `postgresql` instead, MongoDB - use `mongodb` instead, Oracle - use `oracle` instead, SQL Server - use `sqlserver` instead

Finds, scores, and reaches high-value contacts through social graph analysis and warm path discovery, generating personalized outreach drafts for email, LinkedIn, and X.

Connect to PostgreSQL, MySQL, SQLite, and SQL Server databases using SQLAlchemy, Pandas, and DuckDB. Read and write tables, manage sessions, and handle connection strings securely.

Explains ACID transaction properties and provides Python implementations for atomic operations, consistency validation, isolation levels, and durable storage.

Map cloud infrastructure assets and relationships into a Neo4j graph using Cartography to discover attack paths, IAM permission chains, and security gaps across AWS, GCP, and Azure.

Perform systematic exploratory data analysis to understand dataset structure, distributions, relationships, and anomalies before modeling. Use when a dataset is new, its quality is unknown, or the user requests open-ended profiling; use data-analysis instead for a defined hypothesis or decision question.

Guides OLTP database schema design through six stages: domain and access patterns, entities and relationships, keys and constraints, normalization trade-offs, indexing and performance, and operations and evolution. Includes a final review checklist and tips for effective guidance.

TypeORM for TypeScript/JavaScript. Covers entities, repositories, and relations. Use with SQL databases. USE WHEN: user mentions "typeorm", "@Entity", "Repository", "DataSource", "QueryBuilder", "typeorm migration", asks about "decorators for database", "active record pattern", "entity relationships", "typeorm relations" DO NOT USE FOR: Prisma projects - use `prisma` skill; Drizzle - use `drizzle` skill; SQLAlchemy (Python) - use `sqlalchemy` skill; raw SQL - use `database-query` MCP; NoSQL - use `mongodb` skill; Sequelize - not supported

Creates, analyzes, and visualizes complex networks and graphs in Python with NetworkX. 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 topologies — applicable to social, biological, transportation, citation, and any pairwise-relationship networks. This is classical graph analytics, not deep learning — for training graph neural networks (GCN/message passing, node/edge/graph classification on Cora-style data) use alterlab-torch-geometric instead. Part of the AlterLab Academic Skills suite.

Builds statistical plots with the seaborn Python library and pandas DataFrame integration, on attractive matplotlib-based defaults. Use for quick exploration of distributions, relationships, and categorical comparisons — box plots, violin plots, swarm/strip plots, KDE/histograms, pair plots, joint plots, regression plots, correlation heatmaps, and faceted small multiples (relplot/displot/catplot/lmplot). For interactive/hover/zoom charts defer to alterlab-plotly; for exact journal/manuscript styling (column widths, point fonts, CMYK, vector export) defer to alterlab-scientific-viz; for low-level custom matplotlib figures defer to alterlab-matplotlib (seaborn integrates with it for fine-tuning). Part of the AlterLab Academic Skills suite.