Comprehensive guide to data quality validation, testing frameworks, anomaly detection, and data observability for production data pipelines

Techniques and tools for ensuring the accuracy, completeness, and reliability of data across the pipeline.

Systematic framework for catching data quality issues, query errors, metric calculation problems, and inconsistencies before they affect analysis results.

Build and refresh eval datasets from Front, run routing evals, and analyze agent response quality.

Set up database replication for high availability and disaster recovery. Use when configuring master-slave replication, multi-master setups, or replication monitoring.

Manage data lifecycle with automated retention and archiving.

Comprehensive data safety auditor for Vue 3 + Pinia + IndexedDB + PouchDB applications. Detects data loss risks, sync issues, race conditions, and browser-specific vulnerabilities with actionable remediation guidance.

Expert-level data science, analytics, visualization, and statistical modeling

Generate realistic, deterministic seed data for development and testing.

Create database seed scripts with realistic test data for development and testing. Use when setting up development environment or creating demo data.

Efficient data serialization for game networking including Protobuf, FlatBuffers, and custom binary

Ensure Alpaca API is used for quality data, not yfinance fallback. Trigger when: (1) crypto volume filter fails unexpectedly, (2) zero-volume bars in data, (3) API key configuration issues.

Production-grade SQL optimization for OLTP systems: EXPLAIN/plan analysis, balanced indexing, schema and query design, migrations, backup/recovery, HA, security, and safe performance tuning across PostgreSQL, MySQL, SQL Server, Oracle, SQLite.

Implement client-side data storage with localStorage, IndexedDB, or SQLite WASM. Use when storing user preferences, caching data, or building offline-first applications.

Transform CSV/Excel data into narrative reports with auto-generated insights, visualizations, and PDF export. Auto-detects patterns and creates plain-English summaries.

This skill should be used when reading any tabular data file (Excel, CSV, Parquet, ODS). It automatically detects and fixes common data issues including multi-level headers, encoding problems, empty rows/columns, and data type mismatches. Returns a clean DataFrame ready for analysis with zero user intervention.

Python data structure conventions for this codebase. Apply when choosing between Pydantic models, dataclasses, and other data containers.

Transform, clean, reshape, and preprocess data using pandas and numpy. Works with ANY LLM provider (GPT, Gemini, Claude, etc.).

Convert between data formats (JSON, CSV, XML, YAML, TOML). Handles nested structures, arrays, and preserves data types where possible.

Generate interactive validation reports with quality scoring, missing data analysis, and type checking. Combines Pandas validation, Plotly visualization, and YAML configuration for comprehensive data quality reporting.

Validate data against schemas, business rules, and data quality standards.

Provides expert design guidance for creating truthful, clear, beautiful data visualizations. Focuses on **DESIGN DECISIONS ONLY**—chart selection, color strategy, visual encoding, and validation. Assumes data is accurate and prepared. Auto-activates when user mentions: data viz, dashboard, chart type, visualization, infographic

Build mathematically correct, visually prominent data visualizations for time-series charts. Use this skill when creating charts with mathematical overlays (trendlines, patterns, indicators), fixing visual artifacts (wavy lines, domain mismatches), or validating chart correctness. Focuses on technical correctness and progressive validation, not aesthetic design.

data-visualizer

Snowflake, BigQuery, Redshift, dimensional modeling, and modern data warehouse architecture

Expert for developing Streamlit data apps for Keboola deployment. Activates when building, modifying, or debugging Keboola data apps, Streamlit dashboards, adding filters, creating pages, or fixing data app issues. Validates data structures using Keboola MCP before writing code, tests implementations with Playwright browser automation, and follows SQL-first architecture patterns.

'Use SQL (PostgreSQL) when:'

Expert in database schema design with focus on normalization, indexing strategies, FTS optimization, and performance-oriented architecture for desktop applications

Schema tasarımı, migration stratejileri, indexing, query optimization ve database best practices.

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Database design, optimization, and operations expert

Database schema design, migrations, query optimization with SQL, Exposed ORM, Flyway. Use for database, migration, schema, sql, flyway tags. Provides migration patterns, validation commands, rollback strategies.

Design and implement database indexing strategies. Use when creating indexes, choosing index types, or optimizing index performance in PostgreSQL and MySQL.

Comprehensive guide for database management patterns covering PostgreSQL and MongoDB including schema design, indexing, transactions, replication, and performance tuning

World-class expert database master covering PostgreSQL, MySQL, MongoDB, Redis, and database architecture. Use when designing schemas, optimizing queries, planning migrations, implementing caching strategies, or solving complex database challenges at production scale.

Database schema migration patterns for Aurora MySQL including reconciliation migrations, idempotent operations, and MySQL-specific gotchas.

Create production-ready Supabase migrations for Ballee following strict naming conventions, idempotent SQL, RLS patterns, and storage bucket policies; use when user requests schema changes, adding columns, RLS policies, database functions, or storage buckets

Database schema migration patterns and best practices. Use when creating database migrations, implementing zero-downtime schema changes, version control for databases, or managing data migrations.

Instrument database queries, connection pools, and detect N+1 queries

SQLModel async database operations with Neon PostgreSQL, migrations, user isolation, and proper indexing. Use when defining models, queries, or database operations.

Expert database performance agent for EasyPlatform. Optimizes queries, indexes, and data access patterns for MongoDB, SQL Server, and PostgreSQL.

Debug database performance issues through query analysis, index optimization, and execution plan review. Identify and fix slow queries.

Improve database query performance through indexing, query optimization, and execution plan analysis. Reduce response times and database load.

Analyze and optimize Django ORM queries including N+1 problems, missing indexes, slow queries, and migration issues. Use when troubleshooting slow API responses, database performance, query optimization, or migration errors.

| Atributo | Valor |

Resets local development database by deleting all data and restarting API container to trigger auto-seeding. SINGLE SOURCE OF TRUTH for dev database reset automation.

Resets staging database with full schema drop. Use for schema changes or migrations. SINGLE SOURCE OF TRUTH for staging database reset automation.

Analyze PostgreSQL/Supabase database schemas for design quality, security, performance, and best practices. Use when reviewing schemas, migrations, RLS policies, or when user mentions database design, indexing, or security issues.

Expert guidance for designing, optimizing, and maintaining database schemas for SQL and NoSQL systems. Use when creating new databases, optimizing existing schemas, planning migrations, implementing security policies, or ensuring GDPR compliance. Covers normalization, indexing, data types, relationships, performance optimization, and audit logging.

Database schema design patterns for SQL and NoSQL databases