data▌

If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.

Use this skill when the user needs Salesforce data work: record CRUD, bulk import/export, test data generation, cleanup scripts, or data factory patterns for validating Apex, Flow, or integration behavior.

Build data pipelines and ETL workflows for data integration, transformation, and analytics automation. Based on n8n's data workflow templates.

Session store integration for tracking recently accessed topics, resources, and pages. \n \n Three initialization hooks ( useInitRecentTopic , useInitRecentResource , useInitRecentPage ) load recent data into session store at app startup \n Read recent data via selectors ( recentSelectors.recentTopics , etc.) or hook return values; selectors are recommended for multi-component access \n Built-in features include auto login detection, data caching, SWR-based auto-refresh on focus, and full TypeSc

Spring Data Neo4j integration for graph databases with repositories, Cypher queries, and reactive operations. \n \n Three abstraction levels: Neo4j Client (low-level), Neo4j Template (medium-level), and Neo4j Repositories (high-level query derivation) \n Supports both imperative Neo4jRepository and reactive ReactiveNeo4jRepository patterns; do not mix both in the same application \n Entity mapping with @Node and @Relationship annotations, supporting business keys or generated IDs with immutable

Interactive charts, dashboards, and data visualizations with Recharts, Chart.js, and D3.js. \n \n Supports three major libraries: Recharts for React projects, Chart.js for framework-agnostic use, and D3.js for custom, publication-quality graphics \n Covers 10+ chart types including line, bar, pie, area, scatter, and heatmaps, with guidance on when to use each \n Includes dashboard patterns for KPI cards, real-time monitoring with Server-Sent Events, and interactive filtering with drill-down capa

Production patterns for implementing data quality with Great Expectations, dbt tests, and data contracts to ensure reliable data pipelines.

Soccer data across 13 leagues with standings, schedules, match stats, xG, transfers, and player profiles — no API keys required. \n \n Covers 13 leagues including Premier League, La Liga, Bundesliga, Serie A, Ligue 1, MLS, Champions League, World Cup, and others \n Provides match-level data: lineups, team statistics, timelines (goals, cards, substitutions), and expected goals (xG) for top 5 leagues only \n Includes player profiles, season leaders, transfer history via Transfermarkt, and injury/d

Data manipulation, analysis, and visualization with Pandas, NumPy, and Matplotlib. \n \n Covers DataFrame and Series creation, indexing, filtering, and type conversions for structured data handling \n Includes data cleaning techniques: missing value handling, deduplication, string operations, and date/time parsing \n Provides GroupBy aggregation, pivot tables, multi-level indexing, and window functions for exploratory analysis \n Integrates Matplotlib and Seaborn for statistical plotting, trend

If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.