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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

Query your data warehouse to answer business questions with cached patterns and concept mappings. \n \n Supports pattern lookup and caching for repeated question types, with outcome recording to improve future queries \n Includes concept-to-table mapping cache and table schema discovery via INFORMATION_SCHEMA or codebase grep \n Provides run_sql() and run_sql_pandas() kernel functions returning Polars or Pandas DataFrames for analysis \n CLI commands for managing concept, pattern, and table cach

Bulk enrichment of company, people, or product data with web-sourced fields like CEO names, funding, and contact info. \n \n Accepts inline JSON data or CSV files; outputs enriched results to CSV \n Runs asynchronously with progress tracking via monitoring URL and polling commands \n Requires parallel-cli tool and internet access; handles large datasets with configurable timeouts \n Supports flexible field requests through natural language intent descriptions (e.g., \"CEO name and founding year\

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

Build a production-ready, AI-powered data collection agent for any public data source. Runs on a schedule, enriches results with a free LLM, stores to a database, and improves over time.

Advanced data modeling tool for complex multi-table schemas with automated relationship management and ER diagram generation. \n \n Designed for enterprise-level scenarios requiring multi-entity relationships, foreign key automation, and visual documentation; most simple table creation should use relational-database-tool directly with SQL \n Generates Mermaid class diagrams following strict type mapping, naming conventions, and constraint rules for consistent schema design \n Supports required/u