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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

SQL-powered analysis of Excel and CSV files with schema inspection, aggregation, and multi-format export. \n \n Execute arbitrary SQL queries against uploaded data, including joins across multiple files, window functions, and pivot-style aggregations \n Inspect file structure (sheets, columns, data types, row counts) and generate statistical summaries (mean, median, stddev, percentiles, null counts) for numeric and string columns \n Export query results to CSV, JSON, or Markdown; results are cac

Query onchain data on Base using SQL with per-query x402 payments. \n \n Access decoded events, transactions, and blocks via CoinbaseQL, a ClickHouse-based SQL dialect supporting joins, CTEs, subqueries, and standard functions \n Three main tables available: base.events (decoded smart contract logs), base.transactions (full transaction data), and base.blocks (block metadata) \n Requires filtering on indexed fields ( event_signature , address , block_timestamp ) in event queries to avoid full tab

Provides data discovery and analysis expertise specializing in extracting actionable insights from complex datasets, identifying patterns and anomalies, and transforming raw data into strategic intelligence. Excels at multi-source data integration, advanced analytics, and data-driven decision support.

This skill enables comprehensive data visualization and analysis for CSV files. It provides three main capabilities: (1) creating individual interactive visualizations using Plotly, (2) automatic data profiling with statistical summaries, and (3) generating multi-plot dashboards. The skill is optimized for exploratory data analysis, statistical reporting, and creating presentation-ready visualizations.

Automated analysis of 200+ scientific data formats with format-specific insights and quality assessment. \n \n Supports six major scientific domains: chemistry, bioinformatics, microscopy, spectroscopy, proteomics, and metabolomics, with 60+ formats per category \n Auto-detects file type and generates detailed markdown reports including metadata extraction, statistical summaries, data quality metrics, and visualization recommendations \n Includes reference documentation for each format covering

Validate data pipelines with Great Expectations, dbt tests, and data contracts. \n \n Covers three complementary frameworks: Great Expectations for statistical and schema validation, dbt tests for transformation layer checks, and data contracts for cross-team data agreements \n Includes six core quality dimensions (completeness, uniqueness, validity, accuracy, consistency, timeliness) with ready-to-use expectation patterns and custom test examples \n Provides checkpoint automation for CI/CD inte

GDPR-compliant data handling with consent management, data subject rights, and privacy controls. \n \n Implements consent management with audit trails, data subject access requests (DSARs), erasure, portability, and rectification workflows \n Provides data retention policies with legal basis tracking, anonymization options, and automated enforcement \n Includes breach notification handling with 72-hour authority reporting and affected individual notification workflows \n Covers privacy-by-design

PostgreSQL-backed GraphQL backend with auto-generated type-safe SDKs for web, mobile, and Flutter. \n \n Define GraphQL schema with @table decorators and relationships; Data Connect generates SQL and GraphQL operations automatically \n Write queries and mutations in GraphQL with filtering, ordering, pagination, and upsert support; transactions available via @transaction \n Secure operations with @auth levels (PUBLIC, USER, NO_ACCESS) and row-level controls using @check and @redact \n Generate ty

Network requests, API calls, and data fetching for Expo apps with caching, error handling, and offline support. \n \n Covers fetch API, React Query, error handling patterns, token management with expo-secure-store, and retry logic with exponential backoff \n Includes offline-first strategies using NetInfo and React Query persistence, plus environment variable configuration with EXPO_PUBLIC_ prefix for client-side URLs \n Supports Expo Router data loaders ( useLoaderData ) for route-level data lo