data▌

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

Automatically analyzes CSV files and generates comprehensive statistics with intelligent, context-aware visualizations. \n \n Intelligently adapts analysis to data type (sales, customer, financial, operational, survey) by inspecting columns first, then runs relevant analyses without asking \n Generates only applicable visualizations: time-series plots for date columns, correlation heatmaps for multiple numeric columns, distributions for categorical data \n Provides complete output in one respons

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

Analyze data in spreadsheets, uncover insights, and create compelling visualizations.

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

Before writing queries, consult references/api-reference.md for endpoints, ID conventions, and data shapes.

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

The Data Layer coordinates data from multiple sources.

$22